🤖Aldo Marzullo - Collected Quotes

"[...] a graph is a mathematical model that is used for describing relationships between entities. However, each complex network presents intrinsic properties. Such properties can be measured by particular metrics, and each measure may characterize one or several local and global aspects of the graph." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"A point of caution when dealing with projections: be aware of the dimension of the projected graph.  In certain cases, such as the one we are considering here, projection may create extremely large numbers of edges, which makes the graph hard to be analyzed." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"A small-world network is characterized by a high clustering coefficient and a short average path length, meaning that most nodes can be reached from any other node through a small number of intermediate connections. This structure often mirrors real-world social networks, where individuals are typically connected through a few mutual acquaintances, allowing for rapid information dissemination." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Although creating simple subgraphs and merging them is a way to generate new graphs of increasing complexity, networks may also be generated by means of probabilistic models   and/or generative models that let a graph grow by itself. Such graphs usually share   interesting properties with real networks and have long been used to create benchmarks and synthetic graphs, especially in times when the amount of data available was not as overwhelming as today." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"As with many other deep learning-based approaches, another major challenge is in interpretability. While knowledge graphs provide a structured and transparent way to store relationships, LLMs operate as a black box, making it difficult to understand how specific outputs are generated. [...] Data alignment is also a key issue, as structured knowledge graphs and unstructured text data must be carefully preprocessed to ensure consistency.  Differences in data formats, ontology mismatches, and information redundancy can create inefficiencies when integrating these two paradigms. Developing robust pipelines that seamlessly connect graph-based insights with LLM-generated text remains an open challenge." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Assortativity is used to quantify the tendency of nodes being connected to similar nodes, which can impact the network’s ability to withstand failures or 'attacks'. High assortativity indicates that nodes of similar degrees are more likely to be connected, leading to a resilient structure where the failure of some nodes does not significantly disrupt overall connectivity. Conversely, networks with low assortativity tend to have nodes connecting with dissimilar degrees, making them more vulnerable to targeted attacks on high-degree nodes [...]" (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Besides allowing us to compress a sparse representation into a denser vector, autoencoders are also widely used to process a signal in order to filter out noise and extract only a relevant (characteristic) signal. This can be very useful in many applications, especially when identifying anomalies and outliers." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Despite their impressive capabilities, LLMs are not without limitations. One of the most significant challenges is the problem of hallucination, where an LLM generates factually incorrect or misleading information that appears plausible. This is particularly problematic in domains requiring high factual accuracy, such as healthcare, finance, and legal applications. To mitigate hallucinations and enhance the reliability of LLM outputs,  Retrieval-Augmented Generation (RAG) has emerged as a powerful technique. RAG works by dynamically retrieving relevant information from an external knowledge source (such as a knowledge graph) at inference time, rather than just relying on pre-trained knowledge. This approach ensures that the model has access to up-to-date and accurate data, grounding answers in verified information rather than generating content purely from its internal representations." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Generally speaking, all the unsupervised embedding algorithms based on matrix factorization use the same principle. They all factorize an input graph expressed as a matrix in different components. The main difference between each method lies in the loss function used during the optimization process and the constraints/formulations posed for the V and H matrices. Indeed, different loss functions allow the creation of an embedding space that emphasizes specific properties of the input graph." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Graph analytics is generally very effective in clustering users, merchants, and communities to provide an effective implementation of behavior analytics. On the other hand, second-party fraud can be identified with the implementation of monitoring employee behavior, as well as compliance checks. Graph analytics can indeed be useful for these use cases. Similar to the first-party models, employee behavior can also be analyzed using graph machine learning, although the dataset may need to encode a number of other sources of information besides transactional data. From a compliance standpoint, process mining techniques that still rely on a graph representation of the various procedural steps/pathways can be effective in identifying fraudulent behavior or non-compliant processes. Finally, third-party fraud, especially in the form of phishing attacks, can also be addressed using graph machine learning. In this context, understanding the network from which the phishing attack comes as well as the URLs being used (which can also benefit from a graph representation) can be critical for building an effective phishing classifier." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Graphs are mathematical structures that are used for describing relationships between entities, and they are used almost everywhere." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"In a nutshell, training LLMs involves optimizing a large number of parameters on very large datasets. This process, known as pretraining, typically employs unsupervised learning objectives, such as predicting missing words in a sentence (masked language modeling) or forecasting subsequent words (causal language modeling). As a side effect, the pre-training phase lets the model learn and 'understand' a language, resulting in a remarkable ability to generalize across various tasks, often achieving state-of-the-art performance. LLMs have demonstrated proficiency in a diverse array of applications, reflecting their versatility and depth of language understanding. Key areas include text generation, language translation, question answering, and summarization, among many others." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Machine learning is a subset of artificial intelligence that aims to provide systems with the ability to learn and improve from data. It has achieved impressive results in many different applications, especially where it is difficult or unfeasible to explicitly define rules to solve a specific task." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Most of the complexity indeed arises from the presence of entities that appear only once or very few times, but still generate cliques within the graph. Such entities are not very informative to capture patterns and provide insights. Besides, they are possibly strongly affected by statistical variability. On the other hand, we should focus on strong correlations that are supported by larger occurrences and provide more reliable statistical results." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Overfitting is one of the main problems that affect machine learning practitioners. It can occur due to several reasons. Some of the reasons can be as follows: The dataset can be ill-defined or not sufficiently representative of the task. In this case, adding more data could help to mitigate the problem. The mathematical model used for addressing the problem is too powerful for the task. In this case, proper constraints can be added to the loss function in order to reduce the model’s 'power'. Such constraints are called regularization terms.(Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"RAG is a framework that combines the strengths of traditional information retrieval systems with the generative capabilities of LLMs. In this setup, an LLM is augmented with a retrieval component that fetches relevant information from external data sources, such as knowledge bases or databases, to produce more accurate and contextually relevant responses. This method enhances the LLM’s output by grounding it in authoritative, up-to-date information." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Reinforcement learning is used for training machine learning agents to make a sequence of decisions. The artificial intelligence algorithm faces a game-like situation, where the agent gets penalties or rewards based on the actions performed The goal of the agent is to understand how to act in order to maximize rewards and minimize penalties". (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"The concept of temporal graphs is useful in all the real-world problems that can be represented as a graph, where the nodes and edges of the graph may change over time. For example, temporal graphs are extensively applied in modeling social networks. By capturing the evolving relationships between individuals, temporal graphs enable a more accurate representation of social dynamics. This is particularly useful for predicting changes in friendships, community structures, and the information diffusion over time." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"The label spreading algorithm is another semi-supervised shallow embedding algorithm.  It was built in order to overcome one big limitation of the label propagation method: the   initial labeling. Indeed, according to the label propagation algorithm, the initial labels cannot be modified in the training process and, in each iteration, they are forced to be equal to their original value. This constraint could generate incorrect results when the initial labeling is affected by errors or noise. As a consequence, the error will be propagated in all nodes of the input graph." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"The main difference between unsupervised and supervised embedding methods essentially lies in the task they attempt to solve. Indeed, if unsupervised shallow embedding algorithms try to learn a good graph, node, or edge representation in order to understand the underlying structure, the supervised algorithms try to find the best solution for a prediction task such as node classification, label prediction, or graph classification." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

🪙Business Intelligence: Data Sharing (Just the Quotes)

"Having multiple data lakes replicates the same problems that were created with multiple data warehouses - disparate data siloes and data fiefdoms that don't facilitate sharing of the corporate data assets across the organization. Organizations need to have a single data lake from which they can source the data for their BI/data warehousing and analytic needs. The data lake may never become the 'single version of the truth' for the organization, but then again, neither will the data warehouse. Instead, the data lake becomes the 'single or central repository for all the organization's data' from which all the organization's reporting and analytic needs are sourced." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"Data governance policies must not enforce constraints on data - Data governance intends to control the level of democracy within the data lake. Its sole purpose of existence is to maintain the quality level through audits, compliance, and timely checks. Data flow, either by its size or quality, must not be constrained through governance norms. [...] Effective data governance elevates confidence in data lake quality and stability, which is a critical factor to data lake success story. Data compliance, data sharing, risk and privacy evaluation, access management, and data security are all factors that impact regulation." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"A data product encapsulates more than just the data. It needs to contain all the structural components needed to manifest its baseline usability characteristics - discoverable, understandable, addressable, etc. - in an autonomous fashion, while continuing to share data in a compliant and secure manner." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data Mesh is a sociotechnical approach to share, access and manage analytical data in complex and large-scale environments - within or across organizations." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"In the case of data mesh, a data product is an architectural quantum. It is the smallest unit of architecture that can be independently deployed and managed. It has high functional cohesion, i.e., performing a specific analytical transformation and securely sharing the result as domain-oriented analytical data. It has all the structural components that it requires to do its function: the transformation code, the data, the metadata, the policies that govern the data, and its dependencies to infrastructure." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"A data sharing data service shares data, in any format and any size, from multiple sources within an organization or other organizations. This type of service provides the required control to share data and allows data-sharing policies to be created. It also enables data sharing in a structured manner and offers complete visibility into how the data is shared and how it is used. A data-sharing system uses APIs for data sharing." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"An API is an interface that allows applications to interact with an external service using a simple set of commands. Data can also be served as part of API interaction. As the data is exposed to multiple external services, API-based methods can scale to share data securely with external services. Data through an API is served in JSON format, therefore the technology used to serve the data using APIs should be able to support JSON formats. For example, a NoSQL database can store such data." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"Each domain data lakehouse may opt to have its data catalog. However, the critical component in this architecture is the data mesh catalog. The data mesh catalog is the master catalog used to discover the data elements available in different nodes. Each domain-oriented node will donate its metadata to the data mesh catalog. This donation of metadata determines the effectiveness of the data mesh architecture. Once the metadata is contributed, other nodes can browse through the data mesh catalog. They can select the data of interest and mutually share data between the nodes through a governed data sharing process. The critical point to note here is that, unlike the hub-spoke architecture, the data mesh architecture enables data sharing between the 'spoke nodes'. There is no hub node in a data mesh architecture." (Pradeep Menon, "Data Lakehouse in Action", 2022)

"A data strategy must align with the business goals and overall framework of how data will be used and managed within an organization. It needs to include standards for how data will be discovered, integrated, accessed, shared, and protected. It needs to address how data will meet regulatory compliance policies, Master Data Management, and data democratization. There needs to be an assurance that both data and metadata have a quality control framework in place to achieve data trust. A data strategy needs to have a clear path on how an organization will accomplish data monetization." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 2023)

"Data lineage is the path the data took from its source origin, the stops it made along the way, and its destination. This includes information on all the systems it passed through, how it was cleansed, what it was enriched with, and how it was secured. Capturing all that metadata is difficult because many of those systems and applications don’t share information. It’s up to you to assemble the data’s path by pulling metadata from all those systems/applications and assembling them in hopes that you find the path your data took from its current location (destination) to its source system. Lineage is probably the hardest piece of metadata to acquire for either streaming or batching data pipelines." (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"Manage data as a strategic asset that evolves into a data product. The premise here is to stop managing data as a byproduct and create an ecosystem that manages data as a valuable strategic asset that can evolve into a data product. Data producers are accountable for managing the life cycle of data from creation to end of life and ensuring it creates business value along the way for data consumers. This requires data that is governed, trusted, protected, secure, and easily accessible. Move data from technical data assets to Data Products by operationalizing data for high scale sharing." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 2023)

"Since domains are used to create data products, and sharing data products across many domains ultimately builds a mesh of data, we need to ensure that the data being served follows some guidelines. Data governance involves creating and adhering to a set of global rules, standards, and policies applied to all data products and their interfaces to ensure a collaborative and interoperable data mesh community. These guidelines must be agreed upon among the participating data mesh domains." (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"Federation is about providing autonomy to each data product owner to make their own decisions about the storage, computing, and sharing of data. However, this autonomy cannot come at a risk to the security and compliance standards of the company." (Aniruddha Deswandikar, "Engineering Data Mesh in Azure Cloud", 2024)

"When data is considered a product, it creates opportunities for collaboration across different domains. This collaboration involves working with other teams to create, share, and use data products that span multiple areas of expertise, interest, or value. Data Mesh promotes cross-domain collaboration by focusing on the consumers rather than the producers. Data products are made available through standardized interfaces and protocols that support various modes of consumption and are governed by domain experts who understand the context and nuances of their data." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"A lakehouse is a data storage space that hosts and manages all types of data in one place (structured, semi-struc-tured, and unstructured), allowing different tools to normalize and examine this data according to organizational requirements and/or individual choices. A lakehouse thus combines the best aspects of a data lake and a data warehouse by eliminating data duplication and friction related to ingestion, transformation, and sharing of data within the organization, all in the open format, Delta Lake." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"Fabric integrates the various technologies needed for an end-to-end data project (namely, ingestion, preparation, storage, processing, enrichment, analysis, visualization, and data sharing) within a single platform accessible as Software as a Service (SaaS), meaning via a simple connection on a web browser. This reduces complexity, costs, and delays related to using multiple tools and technologies, and eliminates all the operational maintenance of infrastructure serving data analytics needs." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"This transition to OneDrive highlights the importance of governance adapted to new methods of collaborative work and data sharing. The idea of OneLake is, therefore, based on this same concept: rather than subscribing to a data lake technology that must be maintained, why not simply subscribe to a storage service that offers a layer of abstraction over the complexities of these data storage infrastructures? As a result, the data lake becomes a controlled or governed environment, but still accessible to users who can view it as a simple and intuitive way to securely share data with their colleagues and IT teams."(Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

🎯Jean-Georges Perrin - Collected Quotes

"Accessibility is another key principle. It’s not enough for data to be findable; In fact, once found, a data product must also be easily accessible. Accessibility includes providing comprehensive documentation that explains how to use the data, as well as ensuring that the data can be easily integrated into various applications and workflows. A good data product should be as straightforward to use as a well-designed software application, with clear instructions and support."  (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Consider data silos. Data silos hinder data accessibility and collaboration, making it difficult to gain a holistic view and leverage the full potential of the available data. They present a real, present, and formidable challenge that almost all data practitioners experience in modern enterprises. Data silos, much like isolated islands in an immense ocean, are repositories of data that are confined within specific departments or systems, disconnected from the broader organizational data landscape. This segregation results in a fragmented data ecosystem, where valuable insights remain untapped, and the collective intelligence of the enterprise is underutilized." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Data Mesh addresses data governance challenges by advocating for a federated governance model, which positions accountability for governance with the data owners who are most knowledgeable about the data. In this model, governance is decentralized, with each domain team responsible for the governance of its data products. This approach ensures that governance decisions are made by those who have the deepest understanding of the data’s context, use, and risks. It leads to more relevant, efficient, and effective governance practices that are closely aligned with the specific needs of each domain." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Data Mesh advocates for domain-driven ownership of data, enabling individual teams to manage and share their data effectively while aligning with the overall organizational objectives. By embracing this paradigm, enterprises can gradually dismantle the barriers of data silos, paving the way for a more integrated, agile, and data-centric organizational culture." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"[...] Data Mesh introduces agility into the data landscape, emphasizing decentralized ownership, responsive data management, and collaborative cross-functional teams. Just as Agile promotes self-organizing teams, Data Mesh advocates for domain-oriented decentralized ownership, putting the power of data in the hands of individual domain teams. In an Agile context, customer collaboration involves continuous engagement with stakeholders to understand their evolving needs. Likewise, Data Mesh encourages domain teams to engage with data consumers within their organization, gathering feedback, and iterating on their data products to meet their specific requirements." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"[...] Data Mesh is, then, at its foundation, a conceptual framework in the realm of data architecture, which emphasizes decentralized data ownership and architecture. It recognizes that in large organizations, data is vast and varied, where each business domain has autonomy over its own data. By decentralizing control, it empowers individual domains to manage and make decisions about their data while maintaining a cohesive overall structure. And presumably, with this autonomy comes better, more localized, and faster decisions, which in-turn, leads to speed and agility." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Federated computational governance is essential for maintaining consistency and compatibility across the Data Mesh. It ensures that despite the decentralized nature of data ownership, there is a unified framework governing how data is managed, used, and shared. This unified approach is crucial in preventing data silos, ensuring data interoperability, and maintaining the overall integrity of the data ecosystem." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Implementing federated computational governance requires a delicate balance. It involves creating governance structures that are robust enough to ensure consistency and compliance, yet flexible enough to accommodate the unique needs and contexts of different data products. This balance is key to fostering an environment where innovation can thrive without compromising the standards and protocols essential for a cohesive data ecosystem." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"In the context of a Data Mesh, a data product is a package of data that is self-contained, self-descriptive, and oriented towards a specific business purpose or function. But, it is not just a mere collection of data, rather it is a coherent unit that provides value to its consumers, akin to a product in any other industry." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Observability extends the concept of reliability. It’s about having the ability to monitor the health and performance of the data product. By using tools to track various metrics like response times and error rates, organizations can proactively manage the data product’s health. This proactive management plays a crucial role in maintaining the product’s reliability, as it allows for the early identification and resolution of potential issues before they escalate." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"[...] scalability and maintainability are key aspects of a valuable data product. It should be capable of handling increasing volumes of data or user demands without necessitating significant redesign or rework. Alongside scalability, maintainability - the ease with which a data product can be updated, modified, or repaired - is critical for its long-term utility. This also includes the product’s ability to evolve based on user feedback and changing business needs, ensuring that it remains relevant and valuable over time." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Self-serve capability in a Data Mesh not only empowers users but also fosters a culture of innovation and agility. It enables individuals to leverage data for their specific needs, encouraging experimentation and personalized analysis. This capability reduces bottlenecks typically associated with centralized data systems, where requests for data access and analysis can slow down decision-making processes." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"The fourth principle, Reuse, focuses on the ability to apply data in multiple contexts. This principle is particularly important in maximizing the value of data. By designing data products to be modular and reusable, they can be used across different projects and applications. For instance, a data product containing customer demographic information can be used by marketing teams for campaign planning, by sales teams for sales strategy development, and by product development teams for market analysis."  (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"The principle of a clear boundary in a Data Mesh ensures that every data product is a well-defined entity within the larger ecosystem. This clarity prevents overlap and confusion, establishing a clear understanding of the data product’s purpose and scope. It aids in managing expectations and directs efforts and resources appropriately, ensuring that each data product can effectively fulfill its intended role." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"The ramifications of data silos extend beyond mere inefficiencies; they actively hinder collaboration and innovation within an organization. When data is trapped in silos, it becomes difficult for teams to access the information they need to collaborate effectively. This lack of accessibility and visibility leads to duplicated efforts, inconsistent data practices, and a general sense of organizational disjointedness." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"The role of an empowered owner is multifaceted. They are tasked with ensuring that the data product aligns with both specific business requirements and the overarching governance framework. This alignment is crucial for maintaining the integrity and usefulness of the data product, ensuring it remains a valuable asset within the organization’s data landscape." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"The structure of a data product is self-contained, meaning that it includes everything necessary for its effective utilization. It adheres to strict standards of quality and governance, thereby ensuring reliability, security, and compliance with relevant regulations. This comprehensive approach makes data products a trusted and dependable resource within the organization. They are designed with user accessibility in mind, offering interfaces and documentation that are easily navigable by a wide range of users, from data experts to those with minimal technical expertise." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Usability is a key determinant of a data product’s value - if it is complex or unintuitive, its potential utility diminishes irrespective of the underlying data quality. Therefore, the design and interface of a data product should facilitate ease of use to ensure that it can be effectively employed by its target users. Somewhat related to this is interoperability - in other words it is also usable from an operations perspective. A valuable data product should not only function in isolation but also integrate seamlessly with other data products. This interoperability is vital for comprehensive analytics and insight generation, as it allows for the combination and analysis of data across various domains. Additionally, compliance with regulatory requirements and security standards is non-negotiable."  (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

📉Graphical Representation: Features (Just the Quotes)

"Graphic methods convey to the mind a more comprehensive grasp of essential features than do written reports, because one can naturally gather interesting details from a picture in far less time than from a written description. Further than this, the examination of a picture allows one to make deductions of his own, while in the case of a written description the reader must, to a great degree, accept the conclusions of the author." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"An important rule in the drafting of curve charts is that the amount scale should begin at zero. In comparisons of size the omission of the zero base, unless clearly indicated, is likely to give a misleading impression of the relative values and trend." (Rufus R Lutz, "Graphic Presentation Simplified", 1949)

"The quantile plot is a good general display since it is fairly easy to construct and does a good job of portraying many aspects of a distribution. Three convenient features of the plot are the following: First, in constructing it, we do not make any arbitrary choices of parameter values or cell boundaries [...] and no models for the data are fitted or assumed. Second, like a table, it is not a summary but a display of all the data. Third, on the quantile plot every point is plotted at a distinct location, even if there are duplicates in the data. The number of points that can be portrayed without overlap is limited only by the resolution of the plotting device. For a high resolution device several hundred points distinguished." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"Maps containing marks that indicate a variety of features at specific locations are easy to produce and often revealing for the reader. You can use dots, numbers, and shapes, with or without keys. The basic map must always be simple and devoid of unnecessary detail. There should be no ambiguity about what happens where." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Maps used as charts do not need fine cartographic detail. Their purpose is to express ideas, explain relationships, or store data for consultation. Keep your maps simple. Edit out irrelevant detail. Without distortion, try to present the facts as the main feature of your map, which should serve only as a springboard for the idea you're trying to put across." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Scatter charts show the relationships between information, plotted as points on a grid. These groupings can portray general features of the source data, and are useful for showing where correlationships occur frequently. Some scatter charts connect points of equal value to produce areas within the grid which consist of similar features." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Boxplots provide information at a glance about center (median), spread (interquartile range), symmetry, and outliers. With practice they are easy to read and are especially useful for quick comparisons of two or more distributions. Sometimes unexpected features such as outliers, skew, or differences in spread are made obvious by boxplots but might otherwise go unnoticed." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"A well-constructed graph can show several features of the data at once. Some graphs contain as much information as the original data, and so (unlike numerical summaries) do not actually simplify the data; rather, they express it in visual form. Unexpected or unusual features, which are not obvious within numerical tables, often jump to our attention once we draw a graph. Because the strengths and weaknesses of graphical methods are opposite those of numerical summary methods, the two work best in combination." (Lawrence C Hamilton, "Data Analysis for Social Scientists: A first course in applied statistics", 1995)

"When analyzing data it is many times advantageous to generate a variety of graphs using the same data. This is true whether there is little or lots of data. Reasons for this are: (1) Frequently, all aspects of a group of data can not be displayed on a single graph. (2) Multiple graphs generally result in a more in-depth understanding of the information. (3) Different aspects of the same data often become apparent. (4) Some types of graphs cause certain features of the data to stand out better (5) Some people relate better to one type of graph than another." (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996) 

"[…] a chart is a picture of relationships, and only the picture counts. Everything else - titles, labels, scale values - merely identifies and explains. The most important feature of the picture is the impression you receive. Scaling has an important controlling effect on that impression." (Gene Zelazny. "Say It with Charts: The executive’s guide to visual communication" 4th Ed., 2001)

"Anyone who has seen, and especially used, a highly responsive interactive visualization tool will be struck by two features. First, that a mere rearrangement of how the data is displayed can lead to a surprising degree of additional insight into that data. Second, that the very property of interactivity can considerably enhance that tool's effectiveness, especially if the computer's response follows a user's action virtually immediately, say within a fraction of a second." (Robert Spence, "Information Visualization", 2001)

"A feature shared by both the range and the interquartile range is that they are each calculated on the basis of just two values - the range uses the maximum and the minimum values, while the IQR uses the two quartiles. The standard deviation, on the other hand, has the distinction of using, directly, every value in the set as part of its calculation. In terms of representativeness, this is a great strength. But the chief drawback of the standard deviation is that, conceptually, it is harder to grasp than other more intuitive measures of spread." (Alan Graham, "Developing Thinking in Statistics", 2006)

"A useful feature of a stem plot is that the values maintain their natural order, while at the same time they are laid out in a way that emphasises the overall distribution of where the values are concentrated (that is, where the longer branches are). This enables you easily to pick out key values such as the median and quartiles." (Alan Graham, "Developing Thinking in Statistics", 2006)

"Old code rarely offers trendy graphics or flavor-of-the-month features, but it has one considerable ad - vantage: It tends to work. A program that has been well used is like an old garden that has been well tended or a vintage guitar that has been well played: Its rough edges have been filed away, its bugs have been found and fixed, and its performance is a known and valuable quantity." (Scott Rosenberg, "Dreaming in Code", 2007)

"Multivariate techniques often summarize or classify many variables to only a few groups or factors (e.g., cluster analysis or multi-dimensional scaling). Parallel coordinate plots can help to investigate the influence of a single variable or a group of variables on the result of a multivariate procedure. Plotting the input variables in a parallel coordinate plot and selecting the features of interest of the multivariate procedure will show the influence of different input variables." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)

"Parallel coordinate plots are often overrated concerning their ability to depict multivariate features. Scatterplots are clearly superior in investigating the relationship between two continuous variables and multivariate outliers do not necessarily stick out in a parallel coordinate plot. Nonetheless, parallel coordinate plots can help to find and understand features such as groups/clusters, outliers and multivariate structures in their multivariate context. The key feature is the ability to select and highlight individual cases or groups in the data, and compare them to other groups or the rest of the data." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009) 

"Histograms are often mistaken for bar charts but there are important differences. Histograms show distribution through the frequency of quantitative values (y axis) against defined intervals of quantitative values(x axis). By contrast, bar charts facilitate comparison of categorical values. One of the distinguishing features of a histogram is the lack of gaps between the bars [...]" (Andy Kirk, "Data Visualization: A successful design process", 2012)

"When various types of data are layered directly on top of one another, the viewer is able to spatially correlate multiple features. This is immediately intuitive in the case of spatial relationships […]" (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"When you decide how to depict your data, you decide on the abstraction. Will you present a graph? A cartoon? An accurate molecular model? And which features will you include in these representations? Your preferred abstraction should include all necessary information, exclude unnecessary information, and make use of your reader’s preexisting knowledge without being confined by it." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"Three high-level targets are very broadly relevant, for all kinds of data: trends, outliers, and features. A trend is a high-level characterization of a pattern in the data. Simple examples of trends include increases, decreases, peaks, troughs, and plateaus. Almost inevitably, some data doesn’t fit well with that backdrop; those elements are the outliers. The exact definition of features is task dependent, meaning any particular structures of interest." (Tamara Munzner, "Visualization Analysis and Design", 2014)

"A predictive model overfits the training set when at least some of the predictions it returns are based on spurious patterns present in the training data used to induce the model. Overfitting happens for a number of reasons, including sampling variance and noise in the training set. The problem of overfitting can affect any machine learning algorithm; however, the fact that decision tree induction algorithms work by recursively splitting the training data means that they have a natural tendency to segregate noisy instances and to create leaf nodes around these instances. Consequently, decision trees overfit by splitting the data on irrelevant features that only appear relevant due to noise or sampling variance in the training data. The likelihood of overfitting occurring increases as a tree gets deeper because the resulting predictions are based on smaller and smaller subsets as the dataset is partitioned after each feature test in the path." (John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies", 2015)

"The main advantage of decision tree models is that they are interpretable. It is relatively easy to understand the sequences of tests a decision tree carried out in order to make a prediction. This interpretability is very important in some domains. [...] Decision tree models can be used for datasets that contain both categorical and continuous descriptive features. A real advantage of the decision tree approach is that it has the ability to model the interactions between descriptive features. This arises from the fact that the tests carried out at each node in the tree are performed in the context of the results of the tests on the other descriptive features that were tested at the preceding nodes on the path from the root. Consequently, if there is an interaction effect between two or more descriptive features, a decision tree can model this."  (John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies", 2015)

"There are two kinds of mistakes that an inappropriate inductive bias can lead to: underfitting and overfitting. Underfitting occurs when the prediction model selected by the algorithm is too simplistic to represent the underlying relationship in the dataset between the descriptive features and the target feature. Overfitting, by contrast, occurs when the prediction model selected by the algorithm is so complex that the model fits to the dataset too closely and becomes sensitive to noise in the data."(John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies", 2015)

📉Graphical Representation: Heuristics (Just the Quotes)

"A good rule of thumb for deciding how long the analysis of the data actually will take is (1) to add up all the time for everything you can think of - editing the data, checking for errors, calculating various statistics, thinking about the results, going back to the data to try out a new idea, and (2) then multiply the estimate obtained in this first step by five." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"A good graph displays relationships and structures that are difficult to detect by merely looking at the data." (Gerald van Belle, "Statistical Rules of Thumb", 2002)

"Displaying numerical information always involves selection. The process of selection needs to be described so that the reader will not be misled." (Gerald van Belle, "Statistical Rules of Thumb", 2002)

"In assessing change, the spacing of the observations is much more important than the number of observations." (Gerald van Belle, "Statistical Rules of Thumb", 2002)

"The content and context of the numerical data determines the most appropriate mode of presentation. A few numbers can be listed, many numbers require a table. Relationships among numbers can be displayed by statistics. However, statistics, of necessity, are summary quantities so they cannot fully display the relationships, so a graph can be used to demonstrate them visually. The attractiveness of the form of the presentation is determined by word layout, data structure, and design." (Gerald van Belle, "Statistical Rules of Thumb", 2002)

"Three key aspects of presenting high dimensional data are: rendering, manipulation, and linking. Rendering determines what is to be plotted, manipulation determines the structure of the relationships, and linking determines what information will be shared between plots or sections of the graph." (Gerald van Belle, "Statistical Rules of Thumb", 2002)

"In data visualization, the number one rule of thumb to bear is mind is: Function first, suave second." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"In general, analytic processing is known as 'heuristic' processing. In heuristic processing the requirements for analysis are discovered by the results of the current iteration of processing. […] In heuristic processing you start with some requirements. You build a system to analyze those requirements. Then, after you have results, you sit back and rethink your requirements after you have had time to reflect on the results that have been achieved. You then restate the requirements and redevelop and reanalyze again. Each time you go through the redevelopment exercise is called an 'iteration'. You continue the process of building different iterations of processing until such time as you achieve the results that satisfy the organization that is sponsoring the exercise." (William H Inmon & Daniel Linstedt, "Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault", 2015)

" [...] the rule of three applies to the choice of typography, too. In design practice, there is usually a heading font, body text, and then a font for details. [...]  Even though two of the roles (title and body) are the same font name, one is bold and the other is regular. This equates to two fonts. It is common, too, to use a serif font for a title and then a sans serif for the other two (or vice versa). Learning which fonts to use comes only from practice and studying examples." (Gerald Benoît,"Introduction to Information Visualization: Transforming Data into Meaningful Information", 2019)

"When teaching design composition for posters and for websites, there are some introductory rules [...]. One is the 'rule of thirds'. This equates to (no more than) three colors in the design, three typefaces, and three display areas in a design composition [...]" (Gerald Benoît,"Introduction to Information Visualization: Transforming Data into Meaningful Information", 2019)

"Design choices include more deliberate thought put into resizing, cropping, simplifying, and enhancing information within the limited real estate. These thumbnails need to be visually interpretable, yet inviting and engaging to the audience." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

⛩️Eric Broda - Collected Quotes

"Agents, unlike workflows, dynamically create their own plan to fulfill a task - they select their tools, pick execution paths, and control how they accomplish tasks. Unlike workflows, an agent has a built-in capacity to figure out how best to accomplish a task without predefined static implementation. That means the agent can decide on the fly when to perform a calculation, consult a database, or otherwise adapt its plan." (Eric Broda & Davis Broda,"Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem", 2026)

"An agent is a program powered by LLMs that can independently make decisions, plan iteratively, and execute tasks to achieve complex goals." (Eric Broda & Davis Broda,"Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem", 2026)

"An agent’s memory draws from multiple sources: the native knowledge encoded in its LLM weights, the transient information provided in its immediate context, and external repositories accessed through retrieval techniques such as retrieval-augmented generation (RAG). Together, these form a dynamic hierarchy of recall, reasoning, and adaptation that defines how an agent perceives, interprets, and acts in the world." (Eric Broda & Davis Broda,"Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem", 2026)

"An agentic mesh is an interconnected ecosystem that makes it easy for agents to find each other, collaborate, interact, and transact." (Eric Broda & Davis Broda,"Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem", 2026)

"As agent autonomy and sophistication grow, we believe that agents need to become enterprise grade. They must integrate easily into an enterprise’s technology and application landscape. They must adhere to enterprise processes - DevSecOps and MLOps, for example - that provide the rigor needed to move mission-critical applications, and soon agents, into production. They must adhere to the expectations of all enterprise applications to become discoverable, observable, operable, secure, and trustworthy." (Eric Broda & Davis Broda,"Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem", 2026)

"Context engineering is the practice of selecting, structuring, and delivering the right information to an agent’s LLM at the right time. Because LLMs do not “know” your current situation beyond the text you provide and the tokens they can access, performance depends heavily on what context you place in the prompt: instructions, constraints, facts, prior steps, and goals. Good context engineering turns a general model into a task-competent assistant by shaping what it sees and how it should reason." (Eric Broda & Davis Broda,"Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem", 2026)

"For agents, principles are especially important because agents can act autonomously and operate with minimal human oversight and often influence critical business outcomes. Well-defined agent principles steer agents toward outcomes that reflect organizational values, regulatory requirements, and ethical norms. By embedding these principles into agent design, we - society, organizations, developers - can understand and then manage risk, build stakeholder trust, and ensure that AI-driven processes remain aligned with strategic objectives." (Eric Broda & Davis Broda,"Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem", 2026)

"In an agentic mesh, agents are the core participants, designed with governance, interoperability, and trust so they can collaborate, interact, and even transact in a broader ecosystem of agents. The key distinction, however, lies between the needs of an individual agent and those of the larger ecosystem. Ecosystems exist to enable collaboration at scale, raising questions of how thousands of agents, each operating independently, can plan, execute, and deliver consistent outcomes. These are the challenges that agentic mesh is designed to address." (Eric Broda & Davis Broda,"Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem", 2026)

"LLMs enable an agent to interact with people and other agents using natural language. LLMs are designed to interpret and convert human inputs into data that can be used to plan and execute complex operations. By translating the words that humans type or speak into usable information, LLMs give agents the ability to interact with people, to reason, and to plan and execute all sorts of tasks." (Eric Broda & Davis Broda,"Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem", 2026)

"The concept of explainability in autonomous agents is rooted in the need to understand how agents reach their decisions. Given that LLMs underpin many agent functions, their inherent nondeterminism makes it critical to have a system that clearly outlines each operational step. Briefly, nondeterminism for our purposes refers to the variability in the output of systems like LLMs, meaning that identical inputs can yield different outputs upon each execution. This variability arises from probabilistic decision-making processes embedded within these models, where multiple plausible responses exist for a given prompt." (Eric Broda & Davis Broda,"Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem", 2026)

🪙Business Intelligence: Transactions (Just the Quotes)

"Unfortunately, just collecting the data in one place and making it easily available isn’t enough. When operational data from transactions is loaded into the data warehouse, it often contains missing or inaccurate data. How good or bad the data is a function of the amount of input checking done in the application that generates the transaction. Unfortunately, many deployed applications are less than stellar when it comes to validating the inputs. To overcome this problem, the operational data must go through a 'cleansing' process, which takes care of missing or out-of-range values. If this cleansing step is not done before the data is loaded into the data warehouse, it will have to be performed repeatedly whenever that data is used in a data mining operation." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"More and more data is exchanged between the systems through real-time (or near real-time) interfaces. As soon as the data enters one database, it triggers procedures necessary to send transactions to Other downstream databases. The advantage is immediate propagation of data to all relevant databases. Data is less likely to be out-of-sync. [...] The basic problem is that data is propagated too fast. There is little time to verify that the data is accurate. At best, the validity of individual attributes is usually checked. Even if a data problem can be identified. there is often nobody at the other end of the line to react. The transaction must be either accepted or rejectcd (whatever the consequences). If data is rejected, it may be lost forever!" (Arkady Maydanchik, "Data Quality Assessment", 2007)

"Data lakes have been in existence for a while now, so their need is no longer questioned. What is more relevant is the specifics of the solution's implementation. Consolidating all the siloed data by itself does not constitute a data lake. However, it is a starting point. Layering in governance makes the data consumable and is a step toward a curated data lake. Big data systems provide scale out of the box but force us to make some accommodations for data quality. Age-old aspects of transactional integrity were compromised on a distributed system because it was very hard to maintain ACID compliance. Due to this, BASE properties were favored. All of this was moving the needle in the wrong direction and from pristine data lakes we were moving toward data swamps, where the data could not be trusted and hence insights that were generated on the data could not be trusted either." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"A Data Fabric needs to serve analytical and transactional data consumption patterns to, for instance, address MLOps, trustworthy AI, MDM, inferencing, IoT, edge, and 5G." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"Data products should remain stable and be decoupled from the operational/transactional applications. This requires a mechanism for detecting schema drift, and avoiding disruptive changes. It also requires versioning and, in some cases, independent pipelines to run in parallel, giving your data consumers time to migrate from one version to another." (Piethein Strengholt, "Data Management at Scale: Modern Data Architecture with Data Mesh and Data Fabric" 2nd Ed., 2023)

"Delta Lake brings capabilities such as transactional reliability and support for UPSERTs and MERGEs to data lakes while maintaining the dynamic horizontal scalability and separation of storage and compute of data lakes. Delta Lake is one solution for building data lakehouses, an open data architecture combining the best of data warehouses and data lakes." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Like data lakes, the lakehouse architecture leverages low-cost cloud storage systems with the inherent flexibility and horizontal scalability of those systems. The goal of a lakehouse is to use existing high-performance data formats, such as Parquet, while also enabling ACID transactions (and other features). To add these capabilities, lakehouses use an open-table format, which adds features like ACID transactions, record-level operations, indexing, and key metadata to those existing data formats. This enables data assets stored on low-cost storage systems to have the same reliability that used to be exclusive to the domain of an RDBMS. Delta Lake is an example of an open-table format that supports these types of capabilities." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"The Data Fabric architecture needs to guarantee this single version of the truth within the application and transactional landscape, which – depending on the deployment option of an MDM solution – could also mean to assemble this single version of the truth based on core information that is dispersed and maintained in various data stores." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"The main goal of the transaction log is to enable multiple readers and writers to operate on a given version of a dataset file simultaneously and to provide additional information, like data skipping indexes to the execution engine for more performant operations. The Delta Lake transaction log always shows the user a consistent view of the data and serves as a single source of truth. It is the central repository that tracks all changes the user makes to a Delta table." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"When you leverage Delta Lake with Structured Streaming, you get both the transactional guarantees of Delta Lake and the powerful programming model of Apache Spark Structured Streaming. With Delta Lake, you can now use Delta tables as both streaming sources and sinks, enabling a continuous processing model that processes your data through the Raw, Bronze, Silver, and Gold data lake layers in a streaming fashion, eliminating the need for batch jobs, resulting in a simplified solution architecture."(Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Delta Lake is a transactional storage software layer that runs on top of an existing data lake and adds RDW-like features that improve the lake’s reliability, security, and performance. Delta Lake itself is not storage. In most cases, it’s easy to turn a data lake into a Delta Lake; all you need to do is specify, when you are storing data to your data lake, that you want to save it in Delta Lake format (as opposed to other formats, like CSV or JSON)." (James Serra, "Deciphering Data Architectures", 2024)

🎯Bennie Haelen - Collected Quotes

"A data lake is a cost-effective central repository to store structured, semi-structured, or unstructured data at any scale, in the form of files and blobs. The term 'data lake' came from the analogy of a real river or lake, holding the water, or in this case data, with several tributaries that are flowing the water (aka “data”) into the lake in real time." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Data lakes have some very strong benefits. A data lake architecture enables the consolidation of an organization’s data assets into one central location. Data lakes are format agnostic and rely on open source formats, such as Parquet and Avro. These formats are well understood by a variety of tools, drivers, and libraries, enabling smooth interoperability. Data lakes are deployed on mature cloud storage subsystems, allowing them to benefit from the scalability, monitoring, ease of deployment, and low storage costs associated with these systems. Automated DevOps tools, such as Terraform, have well-established drivers, enabling automated deployments and maintenance." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Data lakes require very large, scalable storage systems, like the ones typically offered in cloud environments. The storage needs to be durable and scalable and should offer interoperability with a variety of third-party tools, libraries, and drivers. Note that data lakes separate the concepts of storage and compute, allowing both to scale independently. Independent scaling of storage and compute allows for on-demand, elastic fine-tuning of resources, allowing our solution architectures to be more flexible. The ingress and egress channels to the storage systems should support high bandwidths, enabling the ingestion or consumption of large batch volumes, or the continuous flow of large volumes of streaming data, such as IoT and streaming media." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Data silos often start to develop as the gap between data engineering activities and data science activities begins to grow. Data scientists frequently spend the majority of their time creating separate ETL and data pipelines that clean and transform data and prepare it into features for their models. These silos usually develop because the tools and technologies used for data engineering don’t support the same activities for data scientists." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Delta Lake brings capabilities such as transactional reliability and support for UPSERTs and MERGEs to data lakes while maintaining the dynamic horizontal scalability and separation of storage and compute of data lakes. Delta Lake is one solution for building data lakehouses, an open data architecture combining the best of data warehouses and data lakes." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Like data lakes, the lakehouse architecture leverages low-cost cloud storage systems with the inherent flexibility and horizontal scalability of those systems. The goal of a lakehouse is to use existing high-performance data formats, such as Parquet, while also enabling ACID transactions (and other features). To add these capabilities, lakehouses use an open-table format, which adds features like ACID transactions, record-level operations, indexing, and key metadata to those existing data formats. This enables data assets stored on low-cost storage systems to have the same reliability that used to be exclusive to the domain of an RDBMS. Delta Lake is an example of an open-table format that supports these types of capabilities." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"MLOps best practices include the need to reproduce and validate every stage of the ML workflow. The ability to reproduce a model reduces the risk of errors, and ensures the correctness and robustness of the ML solution. Consistent data is the most difficult challenge faced in reproducibility, and an ML model will only reproduce the exact same result if the exact same data is used. And since data is constantly changing over time, this can introduce significant challenges to ML reproducibility and MLOps." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Modern data platforms leveraging ETL for analytics will always be consumers of data as they ingest data from various data sources. And as organizations continue to collect, process, and analyze data from a growing number of data sources, the ability to swiftly handle schema evolution and data validation is a critical aspect of any data platform. [...] Delta Lake gives you flexibility to evolve a table’s schema through dynamic and explicit schema updates, while also enforcing schema validation." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Querying by timestamp makes it easy to perform time-series analysis because we can compare the data of the same table to itself at two different points in time. And while there are other ETL patterns we can follow to capture historical data and enable time-series analysis (e.g., slowly changing dimensions and change data feeds), time travel provides a quick and easy way to perform ad hoc analysis for tables that may not have these ETL patterns in place." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Schema evolution in Delta Lake refers to the ability to evolve the schema of a Delta table over time, while preserving the existing data in the table. In other words, schema evolution allows us to add, remove, or modify columns in an existing Delta table without losing any data or breaking any downstream jobs that depend on the table. This is important as your data and business needs change over time and you may need to add new columns to your table or modify the existing columns to support new use cases." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"The main goal of the transaction log is to enable multiple readers and writers to operate on a given version of a dataset file simultaneously and to provide additional information, like data skipping indexes to the execution engine for more performant operations. The Delta Lake transaction log always shows the user a consistent view of the data and serves as a single source of truth. It is the central repository that tracks all changes the user makes to a Delta table." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"The lakehouse is centered around the idea of unification and combining the best elements of different technologies in a single place. This means it is also important that the data flow within the lakehouse itself supports this unification of data. In order to support all use cases, this data flow requires merging batch and streaming data into a single data flow to support scenarios across the entire data lifecycle."(Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Traditionally, data lakes have always operated under the principle of schema on read, but have always had challenges enforcing schema on write. This means there is no predefined schema when data is written to storage, and a schema is only adapted when the data is processed. It is imperative for the case of analytics and data platforms that your table formats enforce the schema on write to prevent introducing change-breaking processes, and to maintain proper data quality and integrity." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Unlike data warehouses, data lakes support all data types, including semi-structured and unstructured data, enabling workloads such as media processing. Because of their high throughput ingress channels, they are very well suited for streaming use cases, such as ingesting IoT sensor data, media streaming, or web clickstreams. However, as data lakes become more popular and widely used, organizations started recognizing some challenges with traditional data lakes. While the underlying cloud storage is relatively inexpensive, building and maintaining an effective data lake requires expert skills, resulting in high-end staffing or increased consulting services costs." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"Unstructured and semi-structured data are often critical for AI and machine learning use cases, whereas structured and semi-structured data are critical for BI use cases. Because it natively supports all three types of data classifications, you can create a unified system that supports these diverse workloads in a data lake. These workloads can complement each other in a well-designed processing architecture [...]. A data lake helps solve many of the challenges related to data volumes, types, and cost, and while Delta Lake runs on top of a data lake, it is optimized to run best on a cloud data lake." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"When changing clustered columns, liquid clustering does not require the entire table to be rewritten. This clustering evolution is due to the dynamic data layout feature of liquid clustering and offers a significant advantage over partition features mentioned earlier in the chapter. Traditional partitioning is a fixed data layout and does not support changing how a table is partitioned without having to rewrite the entire table. This clustering evolution can be essential as query patterns for a table can often change over time, and this allows you to dynamically adapt to new query patterns without any significant overhead or challenges." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"When you leverage Delta Lake with Structured Streaming, you get both the transactional guarantees of Delta Lake and the powerful programming model of Apache Spark Structured Streaming. With Delta Lake, you can now use Delta tables as both streaming sources and sinks, enabling a continuous processing model that processes your data through the Raw, Bronze, Silver, and Gold data lake layers in a streaming fashion, eliminating the need for batch jobs, resulting in a simplified solution architecture."(Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

"With cloud data lakes you typically pay for what you use, so your costs always align with your data volumes. Since there is only a single storage layer, less data movement across different systems, availability settings, and decoupled storage versus compute, you have isolated and minimized costs for just data storage. For greater cost allocation, most cloud data lakes offer buckets, or containers (filesystems, not to be confused with application containers), to store different layers of the data (e.g., raw versus transformed data). These containers allow you to have finer-grained cost allocation for different areas of your organization. Since data sources and volumes are growing exponentially, it is extremely important to allocate and optimize costs without limiting the volume or variety of data that can be stored." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)

🎯Fadi Maali - Collected Quotes

"A common mistake when implementing a data catalog is to focus only on technical metadata. This limits its use and the potential value. It also excludes business users who have valuable related input or need to use the catalog. A catalog should in fact function as a two-way translation layer between technical and business users." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"A core premise of data mesh is federating data ownership among domain data owners who are responsible for their data as a product. Offering the data as a product requires the data to be discoverable and to have explicitly stated quality characteristics and a clearly defined access method. Such requirements are at the core of what data catalogs support. With support for data labeling, curation, and crowdsourced feedback, data catalogs are well positioned to offer data as a product. Furthermore, data catalogs support the enforcement of compliant data usage, which becomes more important when data ownership is not managed centrally." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"Active governance guides users as they find and use data. A data catalog with active governance will surface compliance information about sensitive data at point of use, so as to encourage users to use canonical and high-quality data assets; it will also provide a way to ask domain experts for help. They actively help users to ensure compliant usage of data with features such as masking, which anonymizes PII for given user personas who are restricted from viewing it per the GDPR." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"Build a community around the catalog. Make sure data producers, stewards, and consumers are all involved and empowered to enrich the content of the catalog. Establish a leader or a team to have clear ownership of the data catalog." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"Data catalog platforms take a more holistic view to focus not only on data assets within an enterprise, but also on the surrounding ecosystem (including business and people elements). They are typically characterized by an extensible data model that can grow to define various assets and concepts, such as metrics, charts, AI features, and users. Data catalog platforms typically augment their data with a focus on business and users to support collaborative governance and enrichment of metadata and to interlink data with business glossaries and dictionaries. Moreover, they are architected to make them easily integrable with other systems." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"Data catalogs that focus on governance are concerned mainly with controlling data access and ensuring that data is used according to defined policies; this includes external policies such as data privacy laws as well as policies defined with an enterprise. Those catalogs apply techniques to identify data assets with sensitive information and to monitor data flow and access." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"Data catalogs that focus on search bring techniques and methods from information retrieval and web search engines to the data domain within enterprises. Some of those catalogs, such as Facebook Nemo, use advanced machine learning and NLP tools to provide personalized search of data within an enterprise. The search can also use data-specific signals such as usage, popularity, and freshness to rank data assets by usefulness." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"Enterprises typically become interested in data catalogs when they have a specific use case or need in mind. Data governance, self-service analytics, and cloud data migration are common examples. Having a specific need or use case helps focus efforts and measure impact. However, as with other technical efforts within enterprises, it is essential to prepare for long-term sustainable success and to have a plan to maximize successful adoption." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"Historically, for their analytics needs, enterprises relied upon a set of tightly coupled tools, typically provided by a single vendor. Nowadays, nearly all of the components of a traditional data warehouse are independent and interchangeable. Those independent tools can be flexibly combined to provide a modern data stack. It is common for current enterprises to have separate tools for data ingestion, data pipelines, data storage and querying, data visualization and business intelligence, and data quality. Furthermore, data can flow in the opposite direction out of the data warehouse in what is referred to as reverse extract, transform, and load (ETL)." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"In a self-service environment with multiple publishers, it’s impossible to completely avoid data redundancy and overlapping. Multiple data assets with similar content, but possibly with varying quality, will exist. A data catalog can guide users to trusted data that comes from a reliable source and is frequently used. A data catalog can also use various explicit and implicit quality signals when ranking datasets for recommendation. Some of those signals are discussed next. Furthermore, a data catalog can recommend domain experts who are automatically identified based on actual data usage." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"It is often said that data scientists and data analysts spend only 20% of their time doing data analysis work, with 80% consumed by data 'issues'. The bulk of their time is spent finding, evaluating, understanding, and preparing data before analysis can begin. A data catalog inverts this principle by enabling data analysts and data scientists to spend 20% of their time looking for data and 80% performing analysis." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"Self-service BI initiatives help organizations become more data-driven and democratize access to data. But data can’t be used if it can’t be found. Search and discovery of trustworthy data is a core value of enterprise data catalogs, and the value extends well beyond business users." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

🎯Christopher Maneu - Collected Quotes

"A data lake is a distributed repository of raw and unprocessed data stored in its original format, without a predefined schema or structure. A data lake is designed to support a wide range of data types, sources, and use cases, such as exploration, discovery, and data experimentation. A data lake follows a 'schema on read' approach. Data is structured and processed only when it is accessed or consumed by a user or application (Extract, Load, Transform (ELT)). A data lake also enables data democratization, meaning data is accessible and available to anyone who needs it, without barriers or restrictions." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"A data warehouse is a centralized repository of structured, cleaned, and verified data that has been extracted, transformed, and loaded from various sources. These steps are commonly called ETL, which stands for Extract, Transform, Load. This data processing methodology involves extracting data from multiple sources, transforming it to meet business needs, and loading it into a destination for analysis and consultation." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"A lake based on the medallion architecture combines the best of lakes and data warehouses. By breaking down silos and eliminating data duplication, it becomes a standard for building data platform architecture." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"A lakehouse is a data storage space that hosts and manages all types of data in one place (structured, semi-struc-tured, and unstructured), allowing different tools to normalize and examine this data according to organizational requirements and/or individual choices. A lakehouse thus combines the best aspects of a data lake and a data warehouse by eliminating data duplication and friction related to ingestion, transformation, and sharing of data within the organization, all in the open format, Delta Lake." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"Considered by many companies as the next generation of data architecture, the data mesh represents the natural evolution of traditional data lakes and data warehouses. While the latter are often limited by their centralized and monolithic structure, the data mesh aims to enable companies to deploy a more flexible, responsive, and massively scalable data strategy." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"[...] the data mesh architecture of Microsoft Fabric primarily supports the organization of data into domains and federated governance [...]  Hierarchizing data within OneLake by domain simplifies organizing data, allowing a data producer to easily identify where to deposit data or a data consumer to filter and discover content by functional domain. But it also enables the distribution of governance responsibilities by defining roles and responsibilities for teams in charge of specific domains."  (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"Data transformation sits at the heart of every successful data platform, serving as the critical bridge between data ingestion and data consumption. While basic transformations might involve simple cleaning and formatting, advanced transformation techniques encompass complex operations such as data enrichment, sophisticated deduplication, machine learning-based predictions, and the creation of derived metrics that weren’t present in the original data sources. These processes are essential for organizations looking to extract maximum value from their data investments." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"Data virtualization is a technique that allows users and applications to access and interact with data stored in multiple, physically separate locations as if it were all in one place. Instead of moving or duplicating data, virtualization creates a logical layer that connects to the original sources and presents them in a unified view. This means users can query, analyze, or combine data from different systems - cloud storage, databases, or other platforms - without needing to know where or how the data is stored." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"Fabric integrates the various technologies needed for an end-to-end data project (namely, ingestion, preparation, storage, processing, enrichment, analysis, visualization, and data sharing) within a single platform accessible as Software as a Service (SaaS), meaning via a simple connection on a web browser. This reduces complexity, costs, and delays related to using multiple tools and technologies, and eliminates all the operational maintenance of infrastructure serving data analytics needs." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"Fabric Pipelines provide reliable and efficient end-to-end orchestration of data flows, managing ingestion, transformation, and loading through a sequence of steps that can leverage various data processing engines. They allow centralizing and orchestrating data movements from various sources, thanks to advanced connectivity features, and with great scalability. Built-in monitoring tools enable real-time tracking of data flow status and quick detection of anomalies or errors." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"Fabric relies on a lakehouse, a data storage model that combines the benefits of a data lake and a data warehouse. Within Fabric, the various data analytics and processing tools rely on a data lake that collects and stores data in its original format, whether structured, semi-structured, or unstructured, without the need to transform or normalize it beforehand. The lakehouse approach then enables converting these diverse data formats into a single format (i.e., compatible with all the data processing engines offered by Fabric) and in an open format, allowing other market vendors to interact with data in the Fabric lakehouse." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"In Fabric, a domain represents a way to logically group data corresponding to specific functional areas. Domains are frequently used to organize data by business sector in order to manage it according to each sector’s regulations, specifics, and requirements." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"It should be noted that, unlike Dataflow Gen2, in pipelines, it is not mandatory to enable staging to load data into a warehouse. Indeed, pipelines are designed for more general orchestration scenarios where you can combine various activities such as transformations, API calls, and so on to create complex workflows. They are not specifically focused on data preparation but rather on end-to-end process automation. Pipelines are more flexible and used for a variety of orchestration tasks, whereas Dataflow Gen2 is specifically designed for data preparation and transformation, hence the requirement for staging in that case." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"One of the most powerful enhancements in Real-Time Intelligence is the integration of anomaly detection capabilities, enabling systems to automatically flag unusual deviations in real time. Rather than relying on predefined thresholds or periodic audits, these AI-driven agents continuously monitor data streams, learning normal behavior patterns and surfacing outliers or unexpected shifts the moment they appear. This proactive approach transforms what was once passive reporting into active surveillance, allowing operational teams to respond instantly when something deviates from the norm." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"The hub and spoke, or 'star network', is a data architecture model that centralizes data from various sources into a single hub, such as a data warehouse or data lake. The hub serves as the source of truth for data and provides standardized schemas and formats. The spokes are the various applications or services that consume data from the hub for different purposes, such as analytics, reporting, or ma-chine learning. Spokes can also perform transformations or aggregations on data before presenting it to end users. The hub and spoke architecture aims to simplify data integration and management by reducing complexity and redundancy in data pipelines" (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"The problem with data lakes is that they have several drawbacks preventing them from being the perfect or ideal solution. The first drawback is an organizational problem: (•) How to organize data in the lake (•) How to classify, catalog, secure, document, and find it (•) How to avoid the lake turning into a swamp where data is mixed, duplicated, obsolete, or inaccessible (•) How to manage quality, governance, and traceability in the lake."(Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"The transformation phase represents the most resource-intensive stage of most data projects, often consuming 60-80% of total project time and effort. This significant investment stems from the inherent complexity of converting raw, inconsistent data into clean, structured, and enriched information ready for business use. Every data quality issue must be identified and resolved, every business rule must be correctly implemented, and every integration point must be properly validated. This meticulous work serves as the essential bridge between raw data ingestion and meaningful business insights." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"This transition to OneDrive highlights the importance of governance adapted to new methods of collaborative work and data sharing. The idea of OneLake is, therefore, based on this same concept: rather than subscribing to a data lake technology that must be maintained, why not simply subscribe to a storage service that offers a layer of abstraction over the complexities of these data storage infrastructures? As a result, the data lake becomes a controlled or governed environment, but still accessible to users who can view it as a simple and intuitive way to securely share data with their colleagues and IT teams."(Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"Traditionally, data engineers are responsible for the first steps of data transformation, commonly referred to as the transition from the 'bronze' stage to the 'silver' stage. This phase includes the normalization of raw data to clean and organize it into a structured and accessible format. Data Engineers ensure that data is properly ingested, stored, and prepared for subsequent steps. Their work focuses on building robust data pipelines and applying basic transformations that make the data usable. Next, responsibility may be handed over to an analytics engineer, who takes charge of the transition from the 'silver' stage to the 'gold' stage. This step involves more complex transformations aimed at refining, enriching, and modeling the data to meet specific analytical needs. The analytics engineer ensures that the data is ready to be used in reports, dashboards, and advanced analyses. The transition to the 'gold' stage means that the data is fully prepared for analytic use, providing strategic insights from consolidated data sources." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"We are now witnessing the rise of a new paradigm in technology, the age of agentic AI, where intelligence moves beyond automation and prediction to autonomy and intent. In this new world, operations across industries are no longer passive systems waiting for human input or post-event analysis. Instead, they have evolved into dynamic ecosystems of intelligence, continuously learning from every signal that flows through the organization. [...] Agentic AI marks the fourth great evolution of software, after client-server, cloud, and SaaS - and perhaps the most transformative of all. It represents the moment when technology stops being a tool we use and becomes a collaborator that thinks, learns, and acts alongside us." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"While Fabric provides all the traditional tools that data specialists use daily to work on data integration and processing projects, it also offers new intuitive interfaces to enable business users, citizen analysts, or business analysts to interact with their data regardless of their skill level. The primary goal is to meet the needs and expectations of these users, who often do not benefit from data analytics and processing tools because they are too complex to use, even though they are themselves the main consumers and producers of data within organizations." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)

"With Fabric, organizations can unlock the full potential of AI and machine learning in their data workflows. First, it provides users with all the tools necessary to create and deploy AI and machine learning models; users can use the frameworks and languages of their choice. Next, it enables these users to benefit from native integration of models that enrich the data present within Fabric with advanced cognitive analytics, such as vision and language, for example, and leverage the new capabilities of generative AI. Finally, it supports users at every stage of their data project with intelligent assistants that help create data integration flows, develop transformations or analyses, build data visualization reports, and even answer business questions by leveraging existing reports and semantic models to deliver contextual insights instantly." (Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)