🖍️Fern Halper - Collected Quotes

"A lakehouse combines the scalability and flexibility of a data lake with the governance, structure, and performance of a data warehouse. It allows organizations to store both structured and unstructured data in one platform while supporting robust analytics, machine learning, and BI workloads. These lakehouses get by the data swamp problem by providing features that are database like. That includes support for ACID transactions." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"Adopting a data mesh requires more than architecture; it involves changes to culture, roles, and accountability structures. [...] Many organizations will need to create new roles, such as data product managers, who combine stewardship, analytics, and product thinking. These individuals oversee the life cycle of a data product to ensure it delivers business value and meets usability standards. Success often depends on building a culture where data is treated as a shared asset across the enterprise and on putting clear lines of accountability in place. In practice, this can mean forming cross-domain councils to align on standards, tying performance objectives to data quality, or investing in training so domain teams are equipped to manage their products responsibly." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"Agentic AI extends GenAI by embedding intelligence within autonomous or semi-autonomous systems that can plan, reason, and take actions within defined boundaries. Instead of simply generating a report, an agentic system might determine which data it needs, retrieve that data from multiple sources, perform analysis, summarize the results, and then trigger follow-up workflows, all while maintaining auditability and alignment with governance policies." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"AI maturity depends as much on people as it does on tools. Organizations need to build AI literacy across roles, from analysts to executives, so that teams can ask the right questions, interpret AI results, and act on them. Cross-functional teams with skills in data engineering, machine learning, business domain knowledge, and governance are essential. AI systems should be treated as products and not one-off projects." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"As part of the move to democratization and utilizing AI-infused tools and generative AI to gain insights into data, businesses need to improve their overall data literacy. Data literacy involves the awareness and recognition of the value of data, how well people understand and interact with data and analytics, and their ability to communicate data-driven insights to impact behavior and achieve business goals. It includes understanding the business and data elements, framing analytics, applying critical interpretation, and developing communication skills." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"As the need to store and analyze new data types (e.g., semi-structured, unstructured, real-time streams) grew, organizations began turning to data lakes. These systems are designed to store high volumes of raw data at scale and use a schema-on-read approach, offering greater flexibility. Initially built on platforms like Apache Hadoop, many early data lakes fell short due to lack of governance, poor performance, and inadequate metadata management. As a result, these early deployments often became so-called data swamps, where users struggled to find, trust, or use the data effectively. Such deployments delivered little to no business value." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"Data observability platforms often consolidate various data health metrics, such as freshness, accuracy, completeness, and consistency, into a unified dashboard, offering a holistic view of data health. This may take the form of a data scorecard. These tools can also provide metrics such as system uptime/availability, data processing times, error rates in data processing, query performance metrics, resource utilization, and user engagement metrics. They provide customizable key performance indicators (KPIs), real-time alerts, and root cause analysis, and they can help assess the impact of data quality issues on downstream applications, reports, and business processes." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"Data products are part of the enterprise data infrastructure. They are managed, versioned, and monitored much like APIs or microservices. This shift is supported by modern cloud-native infrastructure, which enables decentralized development, consistent governance, and scalable consumption. Data products don’t need to be stored in one location. They might be in a data lake, a data warehouse, a database, or a data lakehouse. They can become discoverable and accessible through a centralized data catalog or marketplace acting as a portal where users can find, understand, and request access to relevant data products." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"Governance readiness is about ensuring trust. That involves trust in the data, trust in the models, and trust in how AI affects users and stakeholders. Without it, organizations risk reputational damage, regulatory penalties, and internal resistance." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"Data readiness also involves the quality, integrity, and findability of the data. Is the data trustworthy and curated? Are data pipelines (to move data from source to target) and access controls (to ensure the right people get access to the right data) in place? Moreover, to be ready, the organization’s architectural components - data lakes, warehouses, lakehouses, etc. - must be coherent and aligned to support modern AI applications." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"GenAI, powered by large foundation models, is revolutionizing the way people interact with data and technology. Organizations are using GenAI to democratize AI through natural language interfaces, copilots, assistants, and creative tools that can draft text, summarize reports, or generate images on demand. Yet as transformative as this is, these systems remain largely reactive. They respond to prompts but do not decide what to do next. They do not plan, reason over time, or act toward goals. In short, they generate but they don’t take action." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"If AI is owned solely by IT or the data science team, it will fail. AI must be co-developed with business stakeholders. This means engaging stakeholders from the start, co-owning key performance indicators (KPIs), and embedding AI into workflows, not bolting it on after the fact. AI teams need domain experts, business sponsors, and clear lines of feedback. Use cases should be selected with the business, not handed down from a tech function." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"One of the most overlooked aspects of AI deployment is the operational side, what happens after a model is developed. Operational readiness is concerned with the systems, processes, and team structures required to bring AI into production. In many cases, organizations build prototypes that never make it past the lab. To succeed, organizations must have formalized processes for deploying models, integrating them into business workflows, and monitoring their performance over time (no model is good forever; they degrade as the external environment changes)." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"Organizations should use a framework (such as business value vs. complexity) to identify high-impact, feasible AI projects. Organizations that succeed with AI don’t chase hype. Instead, they score and prioritize use cases based on criteria such as business value, data readiness, effort, and complexity. This helps avoid wasted time on technically interesting but low-value projects. Some create the frameworks themselves to determine the high-impact yet feasible projects. Others rely on consultants to help them with this." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"RAG applications must be built with semantics, metadata, and governance in mind. The retrieved information must be high-quality, secure, and appropriate for the user’s role. Equally important is monitoring and management: checking whether source data has changed, ensuring vector stores remain accurate, and watching for hallucinations or data leakage. Organizations are definitely starting to experiment with RAG models today; some are putting them into production applications. Some believe that using RAG helps mitigate hallucinations because it is grounded in trusted organizational data." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"The data architecture provides the blueprint for how the data is organized, accessed, and integrated. It defines the relationships between data sources, how data is modeled and transformed, and how the underlying technologies work together to support the business. While the terms data modeling and data architecture are sometimes used interchangeably, they are not the same. Data modeling is part of a data architecture and defines the specific structure and relationships within datasets (e.g., tables, columns, constraints), whereas data architecture is broader; it provides the framework and strategy for managing, storing, integrating, and utilizing data in an organization." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"The goal of a data product is to provide value, not just data. That value may come from enabling better decision-making, powering customer-facing applications, enriching AI models, or facilitating compliance. Importantly, data products are developed with users in mind, whether internal analysts, business stakeholders, external partners, or automated systems. This user orientation distinguishes data products from raw datasets or traditional reports." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"The modern data environment includes structured data as well as text, machine-generated logs and events, images, and audio. The rapid adoption of generative AI (GenAI) has accelerated both the creation and consumption of text data, increasing volume and variability. Additionally, hybrid and multicloud architectures are now standard. Data are stored and processed across warehouses, lakehouses, SaaS platforms, and edge services. Governance must operate consistently across these environments. In practice, this requires policies that can be enforced programmatically, with end-to-end lineage (where the data came from and how it has been changed) that is visible to both technical and business stakeholders." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"There are a few ways that organizations are trying put guardrails into place. First, they are providing enterprise-approved copilots and assistants that their teams can use. They are providing lightweight governance processes, such as AI councils or intake workflows to make it easy for employees to get approval for new use cases. They are implementing access controls if employees want to use certain tools against company data. Perhaps most importantly, some are implementing AI literacy programs so that employees understand both the risks and the responsible use of AI tools." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"[...] organizations are working to unify the data silos that often exist across their data ecosystem. Some are doing this through a centralized physical approach, such as implementing a data lakehouse pattern, which allows them to store and manage all types of data in one place. Many lakehouse implementations such as those from vendors such as Snowflake and Databricks have evolved into what is often referred to as the modern data platform. This is an architectural pattern that combines the lakehouse with tightly integrated tools for data ingestion, transformation, analytics, observability, and governance. This pattern reflects a shift toward cloud-native architectures that are designed to support end-to-end data workflows with scalability and flexibility." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"Unreliable data is a problem because when data cannot be relied upon to deliver valid insights, organizations begin to question the value of their analytics and AI investments. It’s easy to underestimate the cost of bad data until it surfaces in painful ways. Inaccurate or incomplete data can derail projects, corrupt analytics, introduce legal risk, and erode trust, both internally and externally. Have you ever heard the phrase, 'garbage in, garbage out' related to building a model with poor quality data?" (Fern Halper, "Data Makes the World Go 'Round", 2026)

#️⃣Software Engineering: Mea Culpa (Part V: All-Knowing Developers are Back in Demand?)

Software Engineering Series

I’ve been reading many job descriptions lately related to my experience and curiously or not I observed that many organizations look for developers with Microsoft Dynamics experience in the CRM, respectively Finance and Operations (F&O) and Business Central (BC) areas. It’s a good sign that the adoption of Microsoft solutions for CRM and ERP increases, especially when one considers the progress made in the BI and AI areas with the introduction of Microsoft Fabric, which gives Microsoft a considerable boost. Conversely, it seems that the "developers are good for everything" syntagma is back, at least from what one reads in job descriptions. 

Of course, it’s useful to have an inhouse developer who can address all the aspects of an implementation, though that’s a lot to ask considering the different non-programming areas that need to be addressed. It’s true that a developer with experience can handle Requirements, Data and Process Management, respectively Data Migrations and Business Intelligence topics, though if one considers that each of the topics can easily become a full-time job before, during and post-project implementations. I’ve been there and I (hopefully) know that the jobs imply. Even if an experienced programmer can easily handle the different aspects, there will be also times when all the topics combined will be too much for a person!

It's not a novelty that job descriptions are treated like Christmas lists, but it’s difficult to differentiate between essential and nonessential skillset. I read many jobs descriptions lately in which among a huge list of demands, one of the requirements is to program in the F&O framework, sign that D365 programmers are in high demand. I worked for many years as programmer and Software Engineer, respectively in the BI area, where SQL and non-SQL code is needed. Even if I can understand the code in F&O, does it make sense to learn now to program in X++ and the whole framework? 

It's never too late to learn new tricks, respectively another programming language and/or framework. It even helps to provide better solutions in usual areas, though frankly I would invest my time in other areas, and AI-related topics like AI prompting or Data Science seem to be more interesting on the long run, especially when they are already in demand!

There seems to be a tendency for Data Science professionals to do everything, building their own solutions, ignoring the experience accumulated respectively the data models built in BI and Data Analytics areas, as if the topics and data models are unrelated! It’s also true that AI-modeling comes with its own requirements in what concerns data modeling (e.g. translating non-numeric to numeric values), though I believe that common ground can be found!

Similarly, the notebook-based programming seems to replicate logic in each solution, which occasionally makes sense, though personally I wouldn’t recommend it as practice! The other day, I was looking at code developed in Python to mimic the joining of tables, when a view with the same could be easier (re)used, maintained, read and probably more efficient, even if different engines will be used. It will be interesting to see how the mix of spaghetti solutions will evolve over time. There are developers already complaining of the number of objects used in the process by building logic for each layer from the medallion architecture! Even if it makes sense from architectural considerations, it will become a nightmare in time.

One can wonder also about nomenclature used – Data Engineer or Prompt Engineering for the simple manipulation of data between structures in data transformations, respectively for structuring the prompts for AI. I believe that engineering involves more than this, no matter the context! 

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🧭🏭Business Intelligence: Microsoft Fabric (Part I: Notebooks)

Business Intelligence Series 

When several technologies make their entrance in a data-related field like Data Warehousing, Data Analitics or Data Science, one is forced to understand how the respective technologies can be used or misused, respectively what's their place in the bigger picture. Microsoft Fabric introduces several important technologies that will change the way data are stored, processed and consumed. 

The first important technology is the notebook - a web document-like cell-based container for writing and executing code in a collaborative manner. The concept is not new, Jupyter notebooks have been around for almost a decade. In Microsof Fabric, notebooks support multiple languages, from which a default one applies to the whole notebook, while on cell level any of the supported languages can be used. 

One can execute a single cell, multiple cells or the entire notebook in a sequential manner, mix languages for the various operations - load, transform, save, and visualize data when needed. Notebooks can be parametrized and run via the homonymous activity in Data Factory pipelines, automating thus data processing. Probably more functionality is to come. 

Data engineers seems to have great flexibility, though usually flexibility implies constraints and/or mischiefs in other areas. I see for example in presentations the overuse of temporary data objects (mainly views) in Spark SQL as part of complex logic. That's acceptable during prototyping, though such code becomes a danger as soon the logic is deployed into production. Data objects should be created outside of the logic that uses them and should be treated as artifacts, with version control and proper documentation. It's maybe true that temporary objects reduce the volume of objects in the metastore, though is this the way to go?

Temporary objects tend to lead to wheel's reinvention or they get duplicated across multiple notebooks, which can easily create a maintenance nightmare. One needs to consider that the business logic changes a lot, the requirements and the data sources change, and on the long term, the cost of maintaining the code can easily overweight the benefits. 

Notebooks remind me of the beginnings of web programming when HTML was mixed up with client scripting languages like VB Script or Javascript, CSS, respectively server-side scripting languages. It was kind of a spaghetti code, modified repeatedly by multiple programmers, unendingly duplicated, and through a miracle it worked, until it stopped working unexpectedly in strangest situations. The strangest part was when after removing  commented code from a section made the code run again. 

The debugging of another person's code was a nightmare. Code developed by two people for similar purposes was looking unrecognizable different in terms of structure, programming techniques and layout. The technical debt was high, increasing in exponential manner. One was aware that the code needed refactoring, though there were more important things to do or no time allocated for it.

In the meantime the maturity of programming languages, frameworks, methodologies, best practices, and hopefully of programmers improved the overall quality of software (at least on average). Thinking of software from an Engineer's perspective improved the efficiency and effectiveness of a programmer's endeavor. The average programmer is able to write quality code, though there's a considerable minimum of "engineering" knowledge involved beside the mere knowledge of languages and tools. 

Notebooks are good up to a point, beyond which one needs to take a step back, restructure, move the code where it belongs, take a few more steps back and review the good practices and their application, disseminate the knowledge inside the team and use it in the next iterations, respectively refractor the code when needed! Hopefully, people learned from the mistakes of the past. 

Resources:
[1] Microsoft Learn (2023) How to use Microsoft Fabric notebooks (link) 

🧭Business Intelligence: A One-Man Show (Part VI: The Lakehouse Perspective)

Business Intelligence Suite

Continuing the ideas on Christopher Laubenthal's article "Why one person can't do everything in the data space" [1] and why his analogy between a college's functional structure and the core data roles is poorly chosen. In the last post I mentioned as a first argument that the two constructions have different foundations.

Secondly, it's a matter of construction, namely the steps used to arrive from one state to another. Indeed, there's somebody who builds the data warehouse (DWH), somebody who builds the ETL/ELT pipelines for moving the data from the sources to the DWH, somebody who builds the sematic data model that includes business related logic, respectively people who tap into the data for reporting, data visualizations, data science projects, and whatever is still needed in the organization. On top of this, there should be somebody who manages the DWH. I haven't associated any role to them because one of the core roles can be responsible for more than one step. 

In the case of a lakehouse, it is the data engineer who moves the data from the various data sources to the data lake if that doesn't happen already by design or configuration. As per my understanding the data engineers are the ones who design and build the new lakehouse, move transform and manage the data as required. The Data Analysts, Data Scientist and maybe some Information Designers can tap then into the data. However, the DWH and the lakehouse(s) are technologies that facilitate their work. They can still do their work also if the same data are available by other means.

In what concerns the dorm analogy, the verbs were chosen to match the way data warehouses (DWH) or lakehouses are built, though the congruence of the steps is questionable. One could have compared the number of students with the numbers of data entities, but not with the data themselves. Usually, students move by themselves and occupy the places. The story tellers, the assistants and researchers are independent on whether the students are hosted in the dorm or not. Therefore, the analogy seems to be a bit forced. 

Frankly, I covered all the steps except the ones related to Data Science by myself for both described scenarios. It helped that I knew the data from the data sources and the transformations rules I had to apply, respectively the techniques needed for moving and transforming the data, and the volume of data entities was manageable somehow. Conversely, 1-2 more resources in the area of data analysis and visualizations could have helped to bring more value to the business. 

This opens the challenge of scale and it has do to with systems engineering and how the number of components and the interactions between them increase systems' complexity and the demand for managing the respective components. In the simplest linear models, for each multiplier of a certain number of components of the same type from the organization, the number of resources managing the respective layer matches to some degree the multiplier. E.g. if a data engineer can handle x data entities in a unit of time, then for hand n*x components are more likely at least n data engineers required. However, the output of n components is only a fraction of the n*x given the dependencies existing between components and other constraints.

An optimization problem resumes in finding out what data roles to chose to cover an organization's needs. A one man show can be the best solution for small organizations, though unless there's a good division of labor, bringing a second person will make the throughput slower until will become faster.

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Resources:
[1] Christopher Laubenthal (2024) "Why One Person Can’t Do Everything In Data" (link)

🧭Business Intelligence: A One-Man Show (Part V: Focus on the Foundation)

Business Intelligence Suite

I tend to agree that one person can't do anymore "everything in the data space", as Christopher Laubenthal put it his article on the topic [1]. He seems to catch the essence of some of the core data roles found in organizations. Summarizing these roles, data architecture is about designing and building a data infrastructure, data engineering is about moving data, database administration is mainly about managing databases, data analysis is about assisting the business with data and reports, information design is about telling stories, while data science can be about studying the impact of various components on the data. 

However, I find his analogy between a college's functional structure and the core data roles as poorly chosen from multiple perspectives, even if both are about building an infrastructure of some type. 

Firstly, the two constructions have different foundations. Data exists in a an organization also without data architects, data engineers or data administrators (DBAs)! It's enough to buy one or more information systems functioning as islands and reporting needs will arise. The need for a data architect might come when the systems need to be integrated or maybe when a data warehouse needs to be build, though many organizations are still in business without such constructs. While for the others, the more complex the integrations, the bigger the need for a Data Architect. Conversely, some systems can be integrated by design and such capabilities might drive their selection.

Data engineering is needed mainly in the context of the cloud, respectively of data lake-based architectures, where data needs to be moved, processed and prepared for consumption. Conversely, architectures like Microsoft Fabric minimize data movement, the focus being on data processing, the successive transformations it needs to suffer in moving from bronze to the gold layer, respectively in creating an organizational semantical data model. The complexity of the data processing is dependent on data' structuredness, quality and other data characteristics. 

As I mentioned before, modern databases, including the ones in the cloud, reduce the need for DBAs to a considerable degree. Unless the volume of work is big enough to consider a DBA role as an in-house resource, organizations will more likely consider involving a service provider and a contingent to cover the needs. 

Having in-house one or more people acting under the Data Analyst role, people who know and understand the business, respectively the data tools used in the process, can go a long way. Moreover, it's helpful to have an evangelist-like resource in house, a person who is able to raise awareness and knowhow, help diffuse knowledge about tools, techniques, data, results, best practices, respectively act as a mentor for the Data Analyst citizens. From my point of view, these are the people who form the data-related backbone (foundation) of an organization and this is the minimum of what an organization should have!

Once this established, one can build data warehouses, data integrations and other support architectures, respectively think about BI and Data strategy, Data Governance, etc. Of course, having a Chief Data Officer and a Data Strategy in place can bring more structure in handling the topics at the various levels - strategical, tactical, respectively operational. In constructions one starts with a blueprint and a data strategy can have the same effect, if one knows how to write it and implement it accordingly. However, the strategy is just a tool, while the data-knowledgeable workers are the foundation on which organizations should build upon!

"Build it and they will come" philosophy can work as well, though without knowledgeable and inquisitive people the philosophy has high chances to fail.

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Resources:
[1] Christopher Laubenthal (2024) "Why One Person Can’t Do Everything In Data" (link)

🧭Business Intelligence: A One-Man Show (Part IV: Data Roles between Past and Future)

Business Intelligence Series

Databases nowadays are highly secure, reliable and available to a degree that reduces the involvement of DBAs to a minimum. The more databases and servers are available in an organization, and the older they are, the bigger the need for dedicated resources to manage them. The number of DBAs involved tends to be proportional with the volume of work required by the database infrastructure. However, if the infrastructure is in the cloud, managed by the cloud providers, it's enough to have a person in the middle who manages the communication between cloud provider(s) and the organization. The person doesn't even need to be a DBA, even if some knowledge in the field is usually recommended.

The requirement for a Data Architect comes when there are several systems in place and there're multiple projects to integrate or build around the respective systems. It'a also the question of what drives the respective requirement - is it the knowledge of data architectures, the supervision of changes, and/or the review of technical documents? The requirement is thus driven by the projects in progress and those waiting in the pipeline. Conversely, if all the systems are in the cloud, their integration is standardized or doesn't involve much architectural knowledge, the role becomes obsolete or at least not mandatory. 

The Data Engineer role is a bit more challenging to define because it appeared in the context of cloud-based data architectures. It seems to be related to the data movement via ETL/ELT pipelines and of data processing and preparation for the various needs. Data modeling or data presentation knowledge isn't mandatory even if ideal. The role seems to overlap with the one of a Data Warehouse professional, be it a simple architect or developer. Role's knowhow depends also on the tools involved, because one thing is to build a solution based on a standard SQL Server, and another thing to use dedicated layers and architectures for the various purposes. Engineers' number should be proportional with the number of data entities involved.

Conversely, the existence of solutions that move and process the data as needed, can reduce the volume of work. Moreover, the use of AI-driven tools like Copilot might shift the focus from data to prompt engineering. 

The Data Analyst role is kind of a Cinderella - it can involve upon case everything from requirements elicitation to reports writing and results' interpretation, respectively from data collection and data modeling to data visualization. If you have a special wish related to your data, just add it to the role! Analysts' number should be related to the number of issues existing in organization where the collection and processing of data could make a difference. Conversely, the Data Citizen, even if it's not a role but a desirable state of art, could absorb in theory the Data Analyst role.

The Data Scientist is supposed to reveal the gems of knowledge hidden in the data by using Machine Learning, Statistics and other magical tools. The more data available, the higher the chances of finding something, even if probably statistically insignificant or incorrect. The role makes sense mainly in the context of big data, even if some opportunities might be available at smaller scales. Scientists' number depends on the number of projects focused on the big questions. Again, one talks about the Data Scientist citizen. 

The Information Designer role seems to be more about data visualization and presentation. It makes sense in the organizations that rely heavily on visual content. All the other organizations can rely on the default settings of data visualization tools, independently on whether AI is involved or not. 

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🎯Anindita Mahapatra - Collected Quotes

"A data pipeline is an artifact of a data engineering process. It transforms raw data into data ready for analytics. These in turn help solve problems, aid support decisions, and make our lives more convenient. In some ways, it can be thought of as the stitch between the OLTP and OLAP systems. Data pipelines are sometimes referred to as ETL, which stands for extract, transform, load, and it has a variation called extract, load, transform (ELT). The main difference between the two is whether the incoming data is first saved to disk and then transformed (data wrangling) or vice versa. The processing is loosely referred to as ETL. Although, it is fair to say ELT is relevant in the context of Data Lakes and unstructured data, whereas ETL is used for Data Warehouses." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"A data silo is an isolated source of data that is only accessible to a single line of business (LOB) or department. It leads to inefficiencies, wasted resources, and obstacles in the form of incomplete data profiles and the inability to construct deep insights. [...] On the other hand, a data swamp is a large body of data that is ungoverned and unreliable. It is hard to find data and even harder to use it, which is why it's often used out of context. This is the opposite of data silos in the sense that the data is there and has been brought together, but because it has been done without adequate process and policy, it is as good as not being there. That would be a wasted investment." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"A model that has made it into production is a wonderful achievement! However, the journey does not stop there. There is a whole separate pipeline around model management. Over time, the model becomes stale and needs to be retrained. Yet another separate pipeline to monitor drift is needed. Model drift is often on account of data drift and is a signal to trigger a retraining process. This is where the champion model in production is compared against a new challenger version to see whether it is time to be replaced or not. Over time, it is important to be able to query what version exists in production, so that there is no confusion about which is the active one, which is the challenger, and which one needs to be promoted or rolled back. Many people have no idea what version is in production! This is where a central model registry that serves as the single source of truth for the models and their stages and versions is imperative." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Data-driven organizations exhibit a culture of analytics. This cannot be confined to just a few premiere groups but rather to the entire organization. There are both cultural and technical challenges to overcome and this is where people, processes, and tools need to come together to bring around sustainable changes. Every business needs a strategy for business transformation." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Data engineering is the process of converting raw data into analytics-ready data that is more accessible, usable, and consumable than its raw format. Modern companies are increasingly becoming data-driven, which means they use data to make business decisions to give them better insights into their customers and business operations. They can use these to improve profitability, reduce costs, and give them a competitive edge in the market. Behind the scenes, a series of tasks and processes are performed by a host of data personas who build reliable pipelines to source, transform, and analyze data so that it is a repeatable and mostly automated process." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Data governance refers to aligning all aspects of data strategy, business strategy, and compliance requirements. A three-pronged approach of people, policy, and process will provide oversight for all data operations from the time data touches a system to the point it leaves. Roles and responsibilities dictate who has access to what data, something that needs to be enforced and monitored. Data lineage is tracked to provide accountability for how data has been transformed at various steps. Delta's history functionality provides a good audit trail. A central catalog builds on top of it and provides a central place for defining the rules, enforcing them, and monitoring compliance via audit logs. Some of these catalogs have to be built and stitched together unless a managed platform that has taken care of these aspects is leveraged." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"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)

"Lakehouse is a new architecture and data storage paradigm that combines the characteristics of both data warehouses and data lakes to create a unified basis for all types of use cases to be built on top of it. There is no need to move data around. Data is curated and remains in an open format and serves as the single source of truth (SSOT) for all the consumption layers. A modern data platform has needs that span traditional data warehouses, data lakes, machine learning systems, and streaming systems and there is some overlap among these systems. A Lakehouse offers features that span all four systems [...]" (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Many argue that model drift is best monitored by monitoring the data drift in incoming data and the drift in the generated features. As and when the ground truth is available, it is joined by some primary key criteria with the inference data in a Delta table. Again, the update and merge operation support in Delta makes this a breeze. Now the actual and predicted values of the inference data are computed to see how well the model is doing in terms of the quality of insight generation. The feature engineering pipeline is completely in-house and is easier to monitor for drift. The model interpretability may indicate that some columns contributing to the predictive power are incorrect, and it may be necessary to add or remove features. In such cases, a threshold of tolerance is violated, which signals a need for model retraining." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Metadata is critical in driving business value. It does this by facilitating innovation and collaboration among data teams, which indirectly helps mitigate risks such as misinterpretation and misrepresentation of data. Not only does it help ML practitioners discover the right datasets to use for their modeling exercises, but it also enables citizen data scientists to access the most valuable datasets, thereby ensuring the generation of timely and accurate insights." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Simply put, 'lakehouse' refers to an open data architecture that combines the best of data lakes and data warehouses on a single platform. At this point, it would be fair to say that a lakehouse is closer to a data lake than a data warehouse. In fact, it is an extension of your data lake to support all use cases, from BI to AI. All data science and ML personas who were shunted into downstream applications because the tools of their trade were so vastly different and can now share the same stage and have access to the same data as other data personas. This eliminates the need to stitch fragile systems together and leads to better data quality and end-to-end latencies since there is no need to copy data across disparate architectures." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Since data engineering is such a crucial field, you may be wondering who the main players are and what skill sets they possess. Building a data product involves several folks, all of whom need to come together with seamless handoffs to ensure a successful end product or service is created. It would be a mistake to create silos and increase both the number and complexity of integration points as each additional integration is a potential failure point." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"The main challenges include relentlessly chasing data issues that include schema and quality changes (data drift). Sometimes, fixing these issues can cause outages and delays to existing jobs. This is tied tightly to the underlying infrastructure, process, and technology and can be vulnerable to any changes there. For example, a temporary glitch in the cloud ecosystem will result in a failure of the data pipeline." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Traditional data lakes provide the necessary scalability, but not the real-time concurrency and latency needed for BI use cases. Delta comes to the rescue once again by providing performance at scale with a host of optimization techniques, such as caching, data compaction, and indexing. Previously, a subset of the curated data would be pushed to a warehouse to satisfy the latency and concurrency requirements of known queries. What this meant was that if a consumer needed a different access pattern or a slightly older dataset that was not available, they would have to request that their IT or data team get involved. This took data democratization a step backward. Ideally, we should allow people to access any data that they have privileges to. Delta Lake goes a step forward and allows BI tools to access data directly from the lake instead of accessing a sliver of the data in their expensive warehouses." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Understanding modern data architectures and sound data engineering principles and practices are crucial to ensure that your AI and BI strategies are reliable and defensible. Generated insights are going to be as good as the quality of the underlying data, so the upfront effort put into understanding the data, modeling it, and transforming it per the business needs goes a long way to foster innovation, productivity, and agility in your data teams." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"We are at the interesting conjunction of big data, the cloud, and artificial intelligence (AI), all of which are fueling tremendous innovation in every conceivable industry vertical and generating data exponentially. Data engineering is increasingly important as data drives business use cases in every industry vertical. You may argue that data scientists and machine learning practitioners are the unicorns of the industry, and they can work their magic for business. That is certainly a stretch of the imagination. Simple algorithms and a lot of good reliable data produce better insights than complicated algorithms with inadequate data." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)