🕋Data Warehousing: Extraction, transformation, and loading [ETL] (Definitions)

"A process for acquiring data from source systems, reformatting and cleansing it based on business requirements, and loading it, usually into a data warehouse. The term is more generally used as a means of moving data between systems." (Jill Dyché & Evan Levy, "Customer Data Integration", 2006)

"The process of taking data from one source - the source - and transforming it and loading it into another location - the target." (William H Inmon & Anthony Nesavich, "Tapping into Unstructured Data", 2007)

[ETL system:] "Extract, transformation, and load system consisting of a set of processes by which the operational source data is prepared for the data warehouse. Consists of extracting operational data from source applications, cleaning and conforming the data, and delivering the data to the presentation servers, along with the ongoing management and support functions." (Ralph Kimball, "The Data Warehouse Lifecycle Toolkit", 2008)

"The very essence of business intelligence, this is the process of removing raw data from a data system, processing and cleaning it, and then making it available in a business-intelligence database." (Stuart Mudie et al, "BusinessObjects™ XI Release 2 for Dummies", 2008)

"The process of connecting to a source database, pulling data out of the source database, transforming the data into a standard format, and then loading the data into a destination system." (Ken Withee, "Microsoft Business Intelligence For Dummies", 2010)

"A term that describes the activities used to prepare a collection of data sources for loading into a database, but also used to describe data mapping and transformation processes in general." (John R Talburt, "Entity Resolution and Information Quality", 2011)

"Generally, an approach to data integration from multiple source databases to integrated target databases." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"In computing, extract, transform, and load refers to a process in database usage and especially in data warehousing that: extracts data from outside sources; transforms it to fit operational needs, which can include quality levels, and loads it into the end target (database, more specifically, operational data store, data mart, or data warehouse)." (Keith Holdaway, "Harness Oil and Gas Big Data with Analytics", 2014)

"Data warehouse process used to extract data from disparate or homogeneous data sources. Transforms the data to align with data structure for storing and analysis." (Dennis C Guster, "Scalable Data Warehouse Architecture: A Higher Education Case Study", 2018)

"An acronym for the Extract, Transform and Load process. This refers to extracting data from the source system, transforming (cleaning and manipulating etc.) the data and then loading the data into the data warehouse." (BI System Builders)

"ETL is a process that extracts, transforms, and loads data from multiple sources to a data warehouse or other unified data repository." (IBM) [source] 

"Refers to the process by which data is:

– Extracted from sources

– Transformed or standardized for storing in the proper heterogeneous format

– and Loaded into the final store or warehouse. The ETL process is commonly run in parallel with transformation processes executing as data is being extracted from sources." (Insight Software)

"Short for extraction, transformation, and loading. The process used to populate a data warehouse from disparate existing database systems." (Microstrategy)

"The act of extracting data from various sources, transforming data to consistent types, and loading the transformed data for use by applications." (Microsoft)

"a process in data warehousing responsible for pulling data out of one source, transforming them so that they meet the needs of the processes that will be using them on next stages, and placing them into target database." (KDnuggets)

"ETL is shorthand for – extract, transform, load. An ETL solution facilitates the replication of data from one or more sources that is converted into format suitable for use in analytics and moved into a destination system." (Qlik) [source]

"ETL (extract, transform, load) is three combined processes that are used to pull data from one database and move it to another database. It is a common function in data warehousing." (snowflake) [source]

"ETL (extract, transform, load) processes are three common functions performed on databases. Extract or read data from a database, transform the extracted data into a structure that can be placed into another database, and load or write the data into the target database." (kloudless)

"ETL (extract, transform, load) processes are three database functions. They extract or read data from a database, transform the extracted data into a structure that can be placed into another database, and load or write the data into the target database." (MuleSoft) 

"ETL stands for Extract-Transform-Load and it refers to the process used to collect data from numerous disparate databases, applications and systems, transforming the data so that it matches the target system’s required formatting and loading it into a destination database." (Databricks) [source]

"Extract Transform Load refers to a trio of processes that are performed when moving raw data from its source to a data warehouse, data mart, or relational database." (Informatica) [source]

"Extract, Transform and Load (ETL) refers to the process in data warehousing that concurrently reads (or extracts) data from source systems; converts (or transforms) the data into the proper format for querying and analysis; and loads it into a data warehouse, operational data store or data mart). ETL systems commonly integrate data from multiple applications or systems that may be hosted on separate hardware and managed by different groups or users. ETL is commonly used to assemble a temporary subset of data for ad-hoc reporting, migrate data to new databases or convert database into a new format or type." (Teradata) [source]

"Refers to a process in database usage consisting of three phases: extracting data from external sources, transforming it to fit operational needs (can include a quality check), and loading it into a target database or data warehouse." (Board International)

"In business intelligence, an ETL tool extracts data from one or more data-sources, transforms it and cleanses it to be optimized for reporting and analysis, and loads it into a data store or data warehouse. ETL stands for extract, transform, and load." (Logi Analytics) [source]

🪙Business Intelligence: Data Warehouse (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)

"Having a purposeless or poorly performing dashboard is more common than not. This happens when the underlying architecture is not designed properly to support the needs of dashboard interaction. There is an obvious disconnect between the design of the data warehouse and the design of the dashboards. The people who design the data warehouse do not know what the dashboard will do; and the people who design the dashboards do not know how the data warehouse was designed, resulting in a lack of cohesion between the two. A similar disconnect can also exist between the dashboard designer and the business analyst, resulting in a dashboard that may look beautiful and dazzling but brings very little business value." (Nils H Rasmussen et al, "Business Dashboards: A visual catalog for design and deployment", 2009)

"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)

"Unfortunately, some organizations are replicating the bad data warehouse practice by creating special-purpose data lakes - data lakes to address a specific business need. Resist that urge! Instead, source the data that is needed for that specific business need into an 'analytic sandbox' where the data scientists and the business users can collaborate to find those data variables and analytic models that are better predictors of the business performance. Within the 'analytic sandbox', the organization can bring together (ingest and integrate) the data that it wants to test, build the analytic models, test the model's goodness of fit, acquire new data, refine the analytic models, and retest the goodness of fit." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"Data quality in warehousing and BI is typically defined in terms of the 4 C’s - is the data clean, correct, consistent, and complete? When it comes to big data, there are two schools of thought that have different views and expectations of data quality. The first school believes that the gold standard of the 4 C’s must apply to all data (big and little) used for clinical care and performance metrics. The second school believes that in big data environments, a stringent data quality standard is impossible, too costly, or not required. While diametrically opposite opinions may play well in panel discussions, they do little to reconcile the realities of healthcare data quality." (Prashant Natarajan et al, "Demystifying Big Data and Machine Learning for Healthcare", 2017) 

"Data warehousing has always been difficult, because leaders within an organization want to approach warehousing and analytics as just another technology or application buy. Viewed in this light, they fail to understand the complexity and interdependent nature of building an enterprise reporting environment." (Prashant Natarajan et al, "Demystifying Big Data and Machine Learning for Healthcare", 2017)

"A data lake is a storage repository that holds a very large amount of data, often from diverse sources, in native format until needed. In some respects, a data lake can be compared to a staging area of a data warehouse, but there are key differences. Just like a staging area, a data lake is a conglomeration point for raw data from diverse sources. However, a staging area only stores new data needed for addition to the data warehouse and is a transient data store. In contrast, a data lake typically stores all possible data that might be needed for an undefined amount of analysis and reporting, allowing analysts to explore new data relationships. In addition, a data lake is usually built on commodity hardware and software such as Hadoop, whereas traditional staging areas typically reside in structured databases that require specialized servers." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)

"A data warehouse follows a pre-built static structure to model source data. Any changes at the structural and configuration level must go through a stringent business review process and impact analysis. Data lakes are very agile. Consumption or analytical layer can be modified to fit in the model requirements. Consumers of a data lake are not constant; therefore, schema and modeling lies at the liberty of analysts and scientists." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data warehousing, as we are aware, is the traditional approach of consolidating data from multiple source systems and combining into one store that would serve as the source for analytical and business intelligence reporting. The concept of data warehousing resolved the problems of data heterogeneity and low-level integration. In terms of objectives, a data lake is no different from a data warehouse. Both are primary advocates of terms like 'single source of truth' and 'central data repository'." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"A defining characteristic of the data lakehouse architecture is allowing direct access to data as files while retaining the valuable properties of a data warehouse. Just do both!" (Bill Inmon et al, "Building the Data Lakehouse", 2021)

"The data lakehouse architecture presents an opportunity comparable to the one seen during the early years of the data warehouse market. The unique ability of the lakehouse to manage data in an open environment, blend all varieties of data from all parts of the enterprise, and combine the data science focus of the data lake with the end user analytics of the data warehouse will unlock incredible value for organizations. [...] "The lakehouse architecture equally makes it natural to manage and apply models where the data lives." (Bill Inmon et al, "Building the Data Lakehouse", 2021)

"A data warehouse service provides cleansed and transformed data that can be used for multiple purposes. First, it serves as a layer for reporting and BI. Second, it is a platform to query data for business or data analysis. Third, it serves as a repository to store historical data that needs to be online and available. Finally, it also acts as a source of transformed data for other downstream data marts that may cater to specific departmental requirements." (Pradeep Menon, "Data Lakehouse in Action", 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)

"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)

"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)

"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)

"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)

🪙Business Intelligence: Problems (Just the Quotes)

"Charts and graphs are a method of organizing information for a unique purpose. The purpose may be to inform, to persuade, to obtain a clear understanding of certain facts, or to focus information and attention on a particular problem. The information contained in charts and graphs must, obviously, be relevant to the purpose. For decision-making purposes. information must be focused clearly on the issue or issues requiring attention. The need is not simply for 'information', but for structured information, clearly presented and narrowed to fit a distinctive decision-making context. An advantage of having a 'formula' or 'model' appropriate to a given situation is that the formula indicates what kind of information is needed to obtain a solution or answer to a specific problem." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"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)

"Changing measures are a particularly common problem with comparisons over time, but measures also can cause problems of their own. [...] We cannot talk about change without making comparisons over time. We cannot avoid such comparisons, nor should we want to. However, there are several basic problems that can affect statistics about change. It is important to consider the problems posed by changing - and sometimes unchanging - measures, and it is also important to recognize the limits of predictions. Claims about change deserve critical inspection; we need to ask ourselves whether apples are being compared to apples - or to very different objects." (Joel Best, "Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists", 2001)

"[...] a good definition of a data product is a product that facilitates an end goal through the use of data. It’s tempting to think of a data product purely as a data problem. After all, there’s nothing more fun than throwing a lot of technical expertise and fancy algorithmic work at a difficult problem." (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"Metrics can serve two purposes: identifying problems and measuring performance. When the goal is to identify problems and pinpoint areas of operational inefficiency and ineffectiveness, defining the right metric requires a bit of detective work. It requires you to uncover the data residue of a problem and to determine what evidence can be found and how exactly it shows up. When the goal is to measure performance, the right success metrics focus on measures that can be controlled and where improvement in the metric is an unambiguously good thing." (Zach Gemignani et al, "Data Fluency", 2014)

"Data mart: A subset of a data warehouse that’s usually oriented to a business group or process rather than enterprise-wide views. They have value as part of the overall enterprise data architecture, but can cause problems when they sprout uncontrolled as data silos with their own data definitions, creating data shadow systems." (Rick Sherman, "Business Intelligence Guidebook: From Data Integration to Analytics, 2015)

"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)

"There are, however, many problems with independent data marts. Independent data marts: (1) Do not have data that can be reconciled with other data marts (2) Require their own independent integration of raw data (3) Do not provide a foundation that can be built on whenever there are future analytical needs." (William H Inmon & Daniel Linstedt, "Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault", 2015)

"Data warehousing, as we are aware, is the traditional approach of consolidating data from multiple source systems and combining into one store that would serve as the source for analytical and business intelligence reporting. The concept of data warehousing resolved the problems of data heterogeneity and low-level integration. In terms of objectives, a data lake is no different from a data warehouse. Both are primary advocates of terms like 'single source of truth' and 'central data repository'." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data has historically been treated as a second-class citizen, as a form of exhaust or by-product emitted by business applications. This application-first thinking remains the major source of problems in today’s computing environments, leading to ad hoc data pipelines, cobbled together data access mechanisms, and inconsistent sources of similar-yet-different truths. Data mesh addresses these shortcomings head-on, by fundamentally altering the relationships we have with our data. Instead of a secondary by-product, data, and the access to it, is promoted to a first-class citizen on par with any other business service." (Adam Bellemare,"Building an Event-Driven Data Mesh: Patterns for Designing and Building Event-Driven Architectures", 2023)

"With all the hype, you would think building a data mesh is the answer to all of these 'problems' with data warehousing. The truth is that while data warehouse projects do fail, it is rarely because they can’t scale enough to handle big data or because the architecture or the technology isn’t capable. Failure is almost always because of problems with the people and/or the process, or that the organization chose the completely wrong technology." (James Serra, "Deciphering Data Architectures", 2024)

🕋Data Warehousing: Data Warehouse [DWH] (Definitions)

"A subject oriented, integrated, time variant, and non-volatile collection of summary and detailed historical data used to support the strategic decision-making processes for the corporation." (William H Inmon, "What is a Data Warehouse?", Prism Vol. 1 (1), 1995)

"A copy of transaction data specifically structured for query and analysis." (Ralph Kimball, The Data Warehouse Toolkit, 1996)

"A database specifically structured for query and analysis. A data warehouse typically contains data representing the business history of an organization. Data in a data warehouse is usually less detailed and longer lived than data from an OLTP system." (Microsoft Corporation, "Microsoft SQL Server 7.0 System Administration Training Kit", 1999)

"The conglomeration of an organization’s data warehouse staging and presentation areas, where operational data is specifically structured for query and analysis performance and ease-of-use." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit" 2nd Ed, 2002)

"The data warehouse is a subject-oriented, integrated, time-variant, nonvolatile collection of data used to support the strategic decision-making process for the enterprise. It is the central point of data integration for business intelligence and is the source of data for the data marts, delivering a common view of enterprise data." (Claudia Imhoff et al, "Mastering Data Warehouse Design", 2003)

"A data warehouse is a set of computer databases specifically designed with related, historical blissful [high quality] data that assist in formulating decisions and taking action." (Margaret Y Chu, "Blissful Data", 2004)

"A set of computer databases specifically designed with related, historical blissful data that assist in formulating decisions and taking action." (Margaret Y Chu, "Blissful Data ", 2004)

"A collection of integrated, subject-oriented databases designed to support the DSS function, where each unit of data is relevant to some moment in time. The data warehouse contains atomic data and lightly summarized data." (William H Inmon, "Building the Data Warehouse", 2005)

"A database that can follow a Third Normal Form (3NF) or dimensional design and that houses a time-variant collection of data from multiple sources. It’s generally used to collect and store integrated sets of historical data from multiple operational systems and then feed one or more dependent data marts. In some cases, a data warehouse may also provide end user access to support enterprise views of data." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)

"A database containing a copy of operational data that is organized for analytic purposes." (Christopher Adamson, "Mastering Data Warehouse Aggregates", 2006)

[Enterprise Data Warehouse] "A database that contains a copy of enterprise data, reorganized for analytic purposes. Subject areas within the enterprise data warehouse are called data marts." (Christopher Adamson, "Mastering Data Warehouse Aggregates", 2006)

"A database specifically structured for query and analysis. A data warehouse typically contains data representing the business history of an organization." (Thomas Moore, "MCTS 70-431: Implementing and Maintaining Microsoft SQL Server 2005", 2006)

"A relational database used as a repository for storing and analyzing numerical information that has been cleansed and verified." (Reed Jacobsen & Stacia Misner, "Microsoft SQL Server 2005 Analysis Services Step by Step", 2006)

"A technology platform that stores business data for the purpose of strategic decision making. Data warehouses are normally the central integration point for large amounts of detailed, historical data from heterogeneous systems across the company to avail data for business intelligence." (Jill Dyché & Evan Levy, "Customer Data Integration", 2006)

"Central repository holding cleaned and transformed information needed by an organization to make decisions, usually extracted from an operational database." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A collection of integrated, subject-oriented databases designed to support the DSS function, where each unit of data is relevant to some moment in time. The data warehouse contains atomic data and lightly summarized data." (William H Inmon & Anthony Nesavich, "Tapping into Unstructured Data", 2007)

"A data structure that is optimized for distribution. It collects and stores integrated sets of historical data from multiple operational systems and feeds them to one or more data marts. (Standard definition from The Data Warehousing Institute)" (Steve Williams & Nancy Williams, "The Profit Impact of Business Intelligence", 2007)

"A specialised database containing consolidated historical data drawn from a number of existing databases to support strategic decision making." (Keith Gordon, "Principles of Data Management", 2007)

"Central repository holding cleaned and transformed information needed by an organization to make decisions, usually extracted from an operational database." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2007)

"Data warehouse is a central repository for summarized and integrated data from operational databases and external data sources." (S Sumathi & S Esakkirajan, "Fundamentals of Relational Database Management Systems", 2007)

"A logical warehouse of data that gathers production and operational information from various departments of a corporation into a single data entity. This information is loaded regularly, which allows for careful analysis over a period of time." (Stuart Mudie et al, "BusinessObjects™ XI Release 2 for Dummies", 2008)

"A repository of data for offline use in building reports and analyzing historical data." (Rod Stephens, "Beginning Database Design Solutions", 2008)

"A data warehouse is a system of records (a business intelligence gathering system) that takes data from a company's operational databases and other data sources and transforms it into a structure conducive to business analysis." (Sivakumar Harinath et al, "Professional Microsoft® SQL Server® Analysis Services 2008 with MDX", 2009)

"A database designed for reporting and data analysis. A data warehouse typically contains data representing the business history of an organization." (Jim Joseph, "Microsoft SQL Server 2008 Reporting Services Unleashed", 2009)

"A large data store containing the organization’s historical data, which is used primarily for data analysis and data mining." (Judith Hurwitz et al, "Service Oriented Architecture For Dummies" 2nd Ed., 2009)

"An integrated, centralized decision support database and the related software programs used to collect, cleanse, transform, and store data from a variety of operational sources to support Business Intelligence. A Data Warehouse may also include dependent data marts." (DAMA International, The DAMA Guide to the Data Management Body of Knowledge 1st Ed., 2009)

"(1) A centralized database for collecting the data from numerous other systems so that they can be made available for management reporting. The database is close to 3rd Normal Form.  (2)  A system that includes the central database described in 1; plus procedures for extracting, transforming, and loading data from other systems; and one or more data marts that organize subsets of the data for particular reporting purposes." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"[A] data warehouse is a collection of data designed to support management in the decision-making process. It is a subject oriented, integrated, time-variant, non-up datable collection of data used in support of management decision-making processes and business intelligence. It contains a wide variety of data that present a coherent picture of business condition at a single point of time. It is a unique kind of database which focuses on business intelligence, external data and time-variant data." (Vijay K Pallaw, "Database Management Systems" 2nd Ed., 2010)

"A data warehouse is a large, enterprise-wide database that acts as a central storage location for data that has been through the Extract, Transform, and Load (ETL) process. A data warehouse often includes historical data as well." (Ken Withee, "Microsoft Business Intelligence For Dummies", 2010)

[Active Data Warehouse (ADW):] "A data warehouse that is generally capable of supporting near-real-time updates, fast response times, and mixed workloads by leveraging well-architected data models, optimized ETL processes, and the use of workload management." (Martin Oberhofer et al, "The Art of Enterprise Information Architecture", 2010)

"A large, often enterprise-wide repository of data used for reporting and analysis. Data warehouses generally collect and manage data from a large number of operational and financial systems across an enterprise." (Janice M Roehl-Anderson, "IT Best Practices for Financial Managers", 2010) 

"A subject-oriented, integrated, time-variant, and historical collection of summary and detailed data used to support the decision-making and other reporting and analysis needs that require historical, point-in-time information. Data, once captured within the warehouse, is nonvolatile and relevant to a point in time." (David Lyle & John G Schmidt, "Lean Integration", 2010)

"A specialized type of database that is used to aggregate data from transaction databases for data analysis purposes, such as identifying and examining business trends, to support planning and decision making." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed, 2011)

[Enterprise Data Warehousing (EDW):] "A data repository of organizational data that is organized, analyzed, and used to enable more informed decision making and planning." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed, 2011)

[federated data warehouse:] "1.A conceptual Data Warehouse made up of multiple decision support databases, potentially on multiple servers, but presented transparently to Business Intelligence users as a unified schema for query, analysis, and reporting. 2.An Enterprise Data Warehouse fed by extracts from departmental Data Warehouses and/or legacy Data Warehouses prior to their incorporation and/or retirement." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The database that stores operations data for long periods of time." (Microsoft, "SQL Server 2012 Glossary", 2012)

"A database used for reporting and analysis." (Craig S Mullins, "Database Administration", 2012)

"A large data store containing the organization’s historical data, which is used primarily for data analysis and data mining. It is the data system of record." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A shared repository of data, often used to support the centralized consolidation of information for decision support." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"A shared repository of data, often used to support the centralized consolidation of information for decision support." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"A store that provides data from the originating source or the operational data stores; it contains historical and derived data. Also known as an information warehouse." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"A subject-oriented, integrated, nonvolatile, time-variant collection of data in support of management’s decisions" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"A large data store containing the organization’s historical data, which is used primarily for data analysis and data mining. It is the data system of record." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"A centralized database system which captures data and allows later analysis of the collected data." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"A secondary database that holds older data for analysis. In some applications, you may want to analyze the data and store modified or aggregated forms in the warehouse instead of keeping every outdated record." (Rod Stephens, "Beginning Software Engineering", 2015)

"Decision-making data base containing the totality of a business’ decision-making data (all subjects)." (Fernando Iafrate, "From Big Data to Smart Data", 2015) 

[Enterprise Data Warehouse (EDW):] "A clean data store created to merge and store data from different sources for enterprise data analysis." (David K Pham, "From Business Strategy to Information Technology Roadmap", 2016)

"A granular, time-variant, structured store of historical data in a neutral, nonredundant format for multiple uses. Its purpose is the reuse of data." (Gregory Lampshire et al, "The Data and Analytics Playbook", 2016)

"Electronic storehouses where vast amounts of data are arrayed, integrated, categorised, stored, and sold." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"A repository for storing business-relevant data." (Jonathan Ferrar et al, "The Power of People", 2017)

"A very large database designed to support decision making in organizations. It is usually batch updated and structured for rapid online queries and managerial summaries. A data warehouse is a subject-oriented, integrated, time-variant, nonvolatile collection of data." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"A data warehouse is a repository for all the data that an enterprise collects from internal and external sources." (Amar Sahay, "Business Analytics" Vol. I, 2018)

"A data warehouse is a repository of enterprise data used for reporting and analysis." (Michelle Gutzait et al, "Hands-On Data Warehousing with Azure Data Factory", 2018) 

"A subject-oriented collection of data that is used to support strategic decision making. The warehouse is the central point of data integration for business intelligence. It is the source of data for data marts within an enterprise and delivers a common view of enterprise data." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

[Enterprise Data Warehouse (EDW):] "system used for analysis and reporting that consists of central repositories of integrated data from a wide spectrum of different sources." (Francesco Corea, "An Introduction to Data: Everything You Need to Know About AI, Big Data and Data Science", 2019)

"A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data that supports management’s decision-making process." (Piethein Strengholt, "Data Management at Scale", 2020)

"A cloud data warehouse is an enterprise data warehouse offered as a managed service (PaaS) on public clouds with optimized integrations for data ingestion, analytics processing, and BI analytics." (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022) 

"A data warehouse is a central storage for all data that an enterprise’s various business systems collect." (Logi Analytics) [source]

"A data warehouse is a data management solution to store large quantities of historical business data, performing queries to support various business intelligence and analytics use cases." (Qlik) [source]

"A data warehouse is a database designed to enable business intelligence activities: it exists to help users understand and enhance their organization's performance. It is designed for query and analysis rather than for transaction processing, and usually contains historical data derived from transaction data, but can include data from other sources. Data warehouses separate analysis workload from transaction workload and enable an organization to consolidate data from several sources." (Oracle)

"A data warehouse is a relational database that is designed for analytical rather than transactional work. It collects and aggregates data from one or many sources so it can be analyzed to produce business insights. It serves as a federated repository for all or certain data sets collected by a business’s operational systems." (snowflake) [source]

"A data warehouse is a repository for data generated and collected by an enterprise's various operational systems." (Techtarget) [source]

"A data warehouse is a storage architecture designed to hold data extracted from transaction systems, operational data stores and external sources. The warehouse then combines that data in an aggregate, summary form suitable for enterprise-wide data analysis and reporting for predefined business needs." (Gartner)

"A data warehouse is a system used to do quick analysis of business trends using data from many sources." (KDnuggets)

[Big data warehouse:] "A specialized, cohesive set of data repositories andplatforms that supports a broad variety of analytics running on-premises, inthe cloud, or in a hybrid environment. BDW leverages traditional and new bigdata technologies such as Hadoop, Spark, columnar and row-based datawarehouses, ETL and streaming, and elastic in-memory and storage frameworks." (Forrester)

[Cloud data warehouse:] "An on-demand, secure, and scalable self-service data warehouse that automates the provisioning, administration, tuning, backup, and recovery to accelerate analytics and actionable insights while minimizing administration requirements." (Forrester)

"A database, typically very large, containing the historical data of an enterprise. Used for decision support or business intelligence, it organizes data and allows coordinated updates and loads." (Microstrategy)

"A large store of data drawing from a wide range of sources that can be processed, split, and analyzed to extract insights that guide management decisions. Data warehouses are typically relational databases that contain historical data and are designed for query and analysis." (Insight Software)

"A record of an enterprise’s past transactional and operational information, stored in a database. Data warehousing is not meant for current 'live' data; rather, data from the production databases are copied to the data warehouse so that queries can be performed without disturbing the performance or the stability of the production systems." (Appian)

"A system used for data analytics. They are a central location of integrated data from other more disparate sources, storing both current (real-time) and historical data which can then be used to create trends reports." (Solutions Review)

"An implementation of an informational database used to store sharable data sourced from an operational database-of-record. It is typically a subject database that allows users to tap into a company's vast store of operational data to track and respond to business trends and facilitate forecasting and planning efforts." (Information Management)

"The database that stores operations data for long periods of time." (Microsoft)

[Enterprise data warehouse:] "A repository of information that is used for reporting and analytics. It includes key data management functions, such as concurrency, security, storage, processing, SQL access, and integration." (Forrester)

[Enterprise data warehouse:] "a single database or set of databases that allow all of an organisation’s data to be stored in a central repository ideally in relationship to each other." (BI System Builders)

"In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc.)" (Wikipedia)

🕋Data Warehousing: Operational Data Store (Definitions)

"The operational data store is subject-oriented and contains current, integrated, consistent data that reflects the current state of its subject. Operational data stores are similar to enterprise data warehouses in that they may include data from different systems that has been made consistent. Operational data stores are different from enterprise data warehouses in that they are updated frequently to reflect the current state of the operational systems." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)

"A physical set of tables sitting between the operational systems and the data warehouse or a specially administered hot partition of the data warehouse itself. The main reason for an ODS is to provide immediate reporting of operational results if neither the operational system nor the regular data warehouse can provide satisfactory access. Because an ODS is necessarily an extract of the operational data, it also may play the role of source for the data warehouse." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit" 2nd Ed., 2002)

"The operational data store is a subject-oriented, integrated, current, volatile collection of data used to support the operational and tactical decision-making process for the enterprise. It is the central point of data integration for business management, delivering a common view of enterprise data." (Claudia Imhoff et al, "Mastering Data Warehouse Design", 2003)

"A hybrid structure designed to support both operational transaction processing and analytical processing." (William H Inmon, "Building the Data Warehouse", 2005)

"A collection of data from operational systems, most often integrated together, that is used for some operational purpose. The most critical characteristic here is that this is used for some operational function. This operational dependency takes precedence and the ODS should not be considered a central component of the data warehousing environment. An ODS can be a clean, integrated source of data to be pulled into the data warehousing environment." (Laura Reeves, "A Manager's Guide to Data Warehousing", 2009)

"A database designed to integrate data from multiple sources to facilitate operations. This is as opposed to a data warehouse, which integrates data from multiple sources to facilitate reporting and analysis." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"A database that is subject-oriented, read-only to end users, current (non-historical), volatile, and integrated; is separate from and derived from one or more systems of record; and supports day-today business operations and real-time decision making." (David Lyle & John G Schmidt, "Lean Integration", 2010)

"A DB system designed to integrate data from multiple sources to allow operational access to the data for operational reporting." (Martin Oberhofer et al, "The Art of Enterprise Information Architecture", 2010)

"Database for transaction processing systems that uses data warehouse concepts to provide clean data." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"A database designed to integrate data from multiple sources for additional operations on the data." (Craig S Mullins, "Database Administration", 2012)

"A data store that provides data from the original source in near real time." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"ODS is the decision support database that integrated operational data from multiple source systems used to capture operational data and is used primarily for near real time operational reporting and analytics. ODSs are used to measure the operations processes efficiencies. The integration pattern is at the lowest levels of granularity and can happen from near real-time to multiple times in a day." (Saumya Chaki, "Enterprise Information Management in Practice", 2015)

"A data store that integrates data from a range of sources, which is subsequently merged and cleaned to serve as the foundation for enterprise operational reporting. It is an important piece of an Enterprise Data Warehouse (EDW) used for enterprise analytical reporting." (David K Pham, "From Business Strategy to Information Technology Roadmap", 2016)

"An ODS system integrates operational or transactional data from multiple systems to support operational reporting." (John D Kelleher & Brendan Tierney, "Data science", 2018)

"An operational data store (ODS) is an alternative to having operational decision support system (DSS) applications access data directly from the database that supports transaction processing (TP). While both require a significant amount of planning, the ODS tends to focus on the operational requirements of a particular business process (for example, customer service), and on the need to allow updates and propagate those updates back to the source operational system from which the data elements were obtained. The data warehouse, on the other hand, provides an architecture for decision makers to access data to perform strategic analysis, which often involves historical and cross-functional data and the need to support many applications." (Gartner)

🕋Data Warehousing: Data Mart [DM] (Definitions)

"A subset of the contents of a data warehouse, stored within its database. A data mart tends to contain data focused at the department level, or on a specific business area. It is frequently implemented to manage the volume and scope of data." (Microsoft Corporation, "Microsoft SQL Server 7.0 System Administration Training Kit", 1999)

"A data warehouse, or repository, whose scope is limited to a single subject area." (William A Giovinazzo, "Internet-Enabled Business Intelligence", 2002)

"A type of data warehouse with data specifically designed for a defined set of functions." (Margaret Y Chu, "Blissful Data ", 2004)

[dependent data mart:] "A data mart that obtains its source data from an Enterprise Data Warehouse." (Margaret Y Chu, "Blissful Data ", 2004)

[independent data mart:] "A data mart that obtains its source data from operational systems or other external media." (Margaret Y Chu, "Blissful Data ", 2004)

"A database that contains a copy of operational data, organized to support analysis of a business process. A data mart may be a subject area within an enterprise data warehouse, or an analytic database that is departmentally focused. When not planned as part of an enterprise data warehouse, a data mart may become a stovepipe. When deployed as an adjunct to a normalized data warehouse, a data mart may contain aggregated data. When built around a conformance bus, the data mart is neither a stovepipe nor an aggregation." (Christopher Adamson, "Mastering Data Warehouse Aggregates", 2006)

[stovepipe data mart:] "A departmentally focused data warehouse implementation that does not interoperate with other subject areas. Stovepipes are avoided through the design of a data warehouse bus - a set of conformed dimensions used consistently across subject areas." (Christopher Adamson, "Mastering Data Warehouse Aggregates", 2006)

"A platform that maintains data for analysis by a single organization or user group for a specific set of business purposes." (Jill Dyché & Evan Levy, "Customer Data Integration", 2006)

"A departmentalized structure of data feeding from the data warehouse where data is denormalized based on the department’s need for information." (William H Inmon & Anthony Nesavich, "Tapping into Unstructured Data", 2007)

"A data mart is a specialized database containing a subset of data from a data warehouse that is needed for a particular business purpose. A data mart is used for reporting and analysis of business data." (Allen Dreibelbis et al, "Enterprise Master Data Management", 2008)

"A smaller data warehouse that holds data of interest to a particular group." (Rod Stephens, "Beginning Database Design Solutions", 2008)

"A specialized type of data warehouse that works with a specific set of data to answer a specific need. A data mart is designed to provide quick, easy access to crucial data." (Stuart Mudie et al, "BusinessObjects™ XI Release 2 for Dummies", 2008) 

"A collection of related data from internal and external sources, transformed, integrated, and stored for the purpose of providing strategic information to a specific set of users in an enterprise." (Paulraj Ponniah, "Data Warehousing Fundamentals for IT Professionals", 2010)

"A database in a data warehouse configuration that holds a subset of data specifically organized for a particular kind of reporting." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"A database structured for specific analysis and historical reporting needs." (David Lyle & John G Schmidt, "Lean Integration", 2010)

"A smaller, more specialized version of a data warehouse that includes data from a specific functional area or department." (Ken Withee, "Microsoft Business Intelligence For Dummies", 2010)

"A decision support database supporting Business Intelligence in a limited subject area, using a dimensional data model design." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Small data warehouse designed to support a department or SBU." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed, 2011)

"A subset of a data warehouse that is designed to focus on a specific set of business information." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A subset of a data warehouse that’s usually oriented to a business group or team." (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

[dependent data mart:] "A data mart whose sole source of data is the data warehouse; a dependent data mart is a component of the corporate information factory" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

[independent data mart:] "A data mart whose source data comes directly from legacy systems, rather than being sourced by a data warehouse" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"Data organized to support specific needs of a user community." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"An analytical database built for and used by a business unit or department to slice and dice for analytical reporting and analysis." (Andrew Pham et al, "From Business Strategy to Information Technology Roadmap", 2016)

"A focused collection of operational data that is usually confined to a specific aspect or subject of a business, such as customers, products, or suppliers. It is a more focused decision support data store than a data warehouse." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"A subset of a data warehouse that allows data to be accessed and customized by specific business functions." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"An analytical database built for and used by a business unit or department to slice and dice for analytical reporting and analysis." (Tiffany Pham et al, "From Business Strategy to Information Technology Roadmap", 2018)

"A subset of a data warehouse that contains data that is tailored and optimized for the specific reporting needs of a department or team. A data mart can be a subset of a warehouse for an entire organization, such as data that is contained in online analytical processing (OLAP) tools." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

"a subset of the data warehouse used for a specific purpose. Data marts are then department-specific or related to a single line of business (LoB)."  (Francesco Corea, "An Introduction to Data: Everything You Need to Know About AI, Big Data and Data Science", 2019)

"A data structure that is optimized for access. It is designed to facilitate end-user analysis of data. It typically supports a single, analytic application used by a distinct set of workers." (The Data Warehousing Institute)

"A database, usually smaller than a data warehouse, designed to help managers make strategic decisions about their business by focusing on a specific subject or department." (Microstrategy)

"A subset of the contents of a data warehouse that tends to contain data focused at the department level, or on a specific business area." (Microsoft)

"A simple data repository that houses data of a specific discipline." (Solutions Review)

"A data mart is a curated subset of data often generated for analytics and business intelligence users. Data marts are often created as a repository of pertinent information for a subgroup of workers or a particular use case." (snowflake) [source]

"A data mart is a subject-oriented database that is often a partitioned segment of an enterprise data warehouse. The subset of data held in a data mart typically aligns with a particular business unit like sales, finance, or marketing." (Talend) [source]

"A data mart is a subset of data from an enterprise data warehouse in which the relevance is limited to a specific business unit or group of users." (Informatica) [source]

"A data mart is a subset of data stored within the overall data warehouse, for the needs of a specific team, section or department within the business enterprise. […] Data marts make it much easier for individual departments to access key data insights more quickly and helps prevent departments within the business organization from interfering with each other’s data." (Sisense) [source]

"A data mart is the access layer of a data warehouse that is used to provide users with data. Data marts are often seen as small slices of the data warehouse. Data warehouses typically house enterprise-wide data, and information stored in a data mart usually belongs to a specific department or team." (Logi Analytics) [source]

"A data mart serves the same role as a data warehouse, but it is intentionally limited in scope. It may serve one particular department or line of business." (Oracle)

"The data mart is a subject-oriented slice of the data warehouse logical model serving a narrow group of users. Many data marts only need a subset of data from the full tables in the data warehouse." (Teradata) [source]

🧊Data Warehousing: Mea Culpa (Part I: A Personal Journey)

Any discussion on data warehousing topics, even unconventional, can’t avoid to mention the two most widely adopted concepts in data warehousing, B. Inmon vs. R. Kimball’s methodologies. There is lot of ink consumed already on this topic and is difficult to come with something new, however I can insert in between my experience and personal views on the topic. From the beginning I have to state that I can’t take any of the two sides because from a philosophical viewpoint I am the adept of “the middle way” and, in addition, when choosing a methodology we have to consider business’ requirements and objectives, the infrastructure, the experience of resources, and many other factors. I don’t believe one method fits all purposes, therefore some flexibility is needed into this concern even from most virulent advocates. After all in the end it counts the degree to which the final solution fits the purpose, and no matter how complex and perfect is a methodology, no matter of the precautions taken, given the complexity of software development projects there is always the risk for failure.

  

B. Inmon defines the data warehouse as a “subject-oriented, integrated, non-volatile and time-varying collection of data in support of the management’s decisions” [3] - subject-oriented because is focused on an organization’s strategic subject areas, integrated because the data are coming from multiple legacy systems in order to provide a single overview, time-variant because data warehouse’s content is time dependent, and non-volatile because in theory data warehouse’s content is not updated but refreshed. 

Within my small library and the internet articles I read on this topic, especially the ones from Kimball University cycle,  I can’t say I found a similar direct definition for data warehouse given by R. Kimball, the closest I could get to something in this direction is the data warehouse as a union of data marts, in his definition a data mart is “a process-oriented subset of the overall organization’s data based on a foundation of atomic data, and that depends only on the physics of the data-measurement events, not on the anticipated user’s questions” [2]. This reflects also an important difference between the two approaches, in Inmon’s philosophy the data marts are updated through the data warehouse, the data in the warehouse being stored in a 3rd normal form, while in data marts are multidimensional and thus denormalized.

Even if it’s a nice conceptual tool intended to simplify data manipulation, I can’t say I’m a big fan of dimensional modeling, mainly because it can be easily misused to create awful (inflexible) monster models that can be barely used, sometimes being impossible to go around them without redesigning them. Also the relational models could be easily misused though they are less complex as physical design, easier to model and they offer greater flexibility even if in theory data’s normalization could add further complexity, however there is always a trade between flexibility, complexity, performance, scalability, usability and reusability, to mention just a few of the dimensions associated with data in general and data quality in particular.

  

In order to overcome dimensional modeling issues R. Kimball recommends a four step approach – first identifying the business processes corresponding to a business measurement or event, secondly declaring the grain (level of detail) and only after that defining the dimensions and facts [1]. I have to admit that starting from the business process adds a plus to this framework because in theory it allows better visibility over the processes, supporting processed-based data analysis, though given the fact that a process could span over multiple data elements or that multiple processes could partition the same data elements, this increases the complexity of such models. I find that a model based directly on the data elements allows more flexibility in the detriment of the work needed to bring the data together, though they should cover also the processes in scope.

  

Building a data warehouse it’s quite a complex task, especially if we take into consideration the huge percentage of software projects failure that holds also in data warehousing area. On the other side not sure how much such statistics about software projects failure can be taken ad literam because different project methodologies and data collection methods are used, not always detailed information are given about the particularities of each project, it would be however interesting to know what the failure rate per methodology. Occasionally there are some numbers advanced that sustain the benefit of using one or another methodology, and ignoring the subjective approach of such justifications they often lack adequate details to support them.

My first contact with building a data warehouse was almost 8 years ago, when as part of the Asset Management System I was supposed to work on, the project included also the creation of a data warehouse. Frankly few things are more scaring than seeing two IT professionals fighting on what approach to use in order to design a data warehouse, and is needless to say that the fight lasted for several days, calls with the customer, nerves, management involved, whole arsenal of negotiations that looked like a never ending story. 

Such fights are sometimes part of the landscape and they should be avoided, the simplest alternative being to put together the advantages and disadvantages of most important approaches and balance between them, unfortunately there are still professionals who don’t know how or not willing to do that. The main problem in such cases is the time which instead of being used constructively was wasted on futile fights. When lot of time is waisted and a tight schedule applies, one is forced to do the whole work in less time, leading maybe to sloppy solutions. 

  

A few years back I had the occasion to develop one data warehouse around the two ERP systems and the other smaller systems one of the customers I worked for was having in place, SQL Server 2000 and its DTS (Data Transformation Services) functionality being of great help for this purpose. Even if I was having some basic knowledge on the two data warehousing approaches, I had to build the initial data warehouse from scratch evolving the initial solution in time along several years. 

The design was quite simple, the DTS packages extracting the data from the legacy systems and dumping them in staging tables in normalized or denormalized form, after several simple transformations loading the data in the production tables, the role of the multidimensional data marts being played successfully by views that were scaling pretty well to the existing demands. Maybe many data warehouse developers would disregard such a solution, though it was quite an useful exercise and helped me to easier understand later the literature on this topic and the issues related to it. In addition, while working on the data conversion of two ERP implementations I had to perform more complex ETL (Extract Transform Load) tasks that the ones consider in the data warehouse itself.

In what concerns software development I am an adept of rapid evolutional prototyping because it allows getting customers’ feedback in early stages and thus being possible to identify earlier the issues as per customers’ perceptions, in plus allowing customers to get a feeling of what’s possible, how the application looks like. The prototyping method proved to be useful most of the times, I would actually say all the times, and often was interesting to see how customers’ conceptualization about what they need changed with time, changes that looked simple leading to partial redesign of the application. In other development approaches with long releases (e.g. waterfall) the customer gets a glimpse of the application late in the process, often being impossible to redesign the application so the customer has to live with what he got. Call me “old fashion” but I am the adept of rapid evolutional prototyping also in what concerns the creation of data warehouses, and even if people might argue that a data warehousing project is totally different than a typical development project, it should not be forgotten that almost all software development projects share many particularities from planning to deployment and further to maintenance.

Even if also B. Inmon embraces the evolutional/iterative approach in building a data warehouse, from a philosophical standpoint the rapid evolutional prototyping applied to data warehouses I feel it’s closer to R. Kimball’s methodology, resuming in choosing a functional key area and its essential business processes, building a data mart and starting from there building other data marts for the other functional key areas, eventually integrating and aligning them in a common solution – the data warehouse. On the other side when designing a software component or a module of one application you have also to consider the final goal, as the respective component or module will be part of a broader system, even if in some cases it could exist in isolation. Same can be said also about data marts’ creation, even if sometimes a data mart is rooted in the needs of a department, you have to look also at the final goal and address the requirements from that perspective or at least be aware of them.

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References:

[1] M. Ross R. Kimball, (2004) Fables and Facts: Do you know the difference between dimensional modeling truth and fiction? [Online] Available from: http://intelligent-enterprise.informationweek.com/info_centers/data_warehousing/showArticle.jhtml;jsessionid=530A0V30XJXTDQE1GHPSKH4ATMY32JVN?articleID=49400912 (Accessed: 18 March 2010)

[2] R. Kimball, J. Caserta (2004). The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Wiley Publishing Inc. ISBN: 0-7645-7923 -1

[3] Inmon W.H. (2005) Building the Data Warehouse, 4th Ed. Wiley Publishing. ISBN: 978-0-7645-9944-6 

🔢Saurabh Gupta - Collected Quotes

"A data warehouse follows a pre-built static structure to model source data. Any changes at the structural and configuration level must go through a stringent business review process and impact analysis. Data lakes are very agile. Consumption or analytical layer can be modified to fit in the model requirements. Consumers of a data lake are not constant; therefore, schema and modeling lies at the liberty of analysts and scientists." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data in the data lake should never get disposed. Data driven strategy must define steps to version the data and handle deletes and updates from the source systems." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"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)

"Data Lake induces accessibility and catalyzes availability. It warrants data discovery platforms to soak the data trends at a horizontal scale and produce visual insights. It largely cuts down the time that goes into data preparation and exhaustive data analysis." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data Lake is a single window snapshot of all enterprise data in its raw format, be it structured, semi-structured, or unstructured. Starting from curating the data ingestion pipeline to the transformation layer for analytical consumption, every aspect of data gets addressed in a data lake ecosystem. It is supposed to hold enormous volumes of data of varied structures." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data lake is an ecosystem for the realization of big data analytics. What makes data lake a huge success is its ability to contain raw data in its native format on a commodity machine and enable a variety of data analytics models to consume data through a unified analytical layer. While the data lake remains highly agile and data-centric, the data governance council governs the data privacy norms, data exchange policies, and the ensures quality and reliability of data lake." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data swamp, on the other hand, presents the devil side of a lake. A data lake in a state of anarchy is nothing but turns into a data swamp. It lacks stable data governance practices, lacks metadata management, and plays weak on ingestion framework. Uncontrolled and untracked access to source data may produce duplicate copies of data and impose pressure on storage systems." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data warehousing, as we are aware, is the traditional approach of consolidating data from multiple source systems and combining into one store that would serve as the source for analytical and business intelligence reporting. The concept of data warehousing resolved the problems of data heterogeneity and low-level integration. In terms of objectives, a data lake is no different from a data warehouse. Both are primary advocates of terms like 'single source of truth' and 'central data repository'." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Metadata is the key to effective data governance. Metadata in this context is the data that defines the structure and attributes of data. This could mean data types, data privacy attributes, scale, and precision. In general, quality of data is directly proportional to the amount and depth of metadata provided. Without metadata, consumers will have to depend on other sources and mechanisms." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"The quality of data that flows within a data pipeline is as important as the functionality of the pipeline. If the data that flows within the pipeline is not a valid representation of the source data set(s), the pipeline doesn’t serve any real purpose. It’s very important to incorporate data quality checks within different phases of the pipeline. These checks should verify the correctness of data at every phase of the pipeline. There should be clear isolation between checks at different parts of the pipeline. The checks include checks like row count, structure, and data type validation." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

🪙Business Intelligence: Data Lake (Just the Quotes)

"If you think of a Data Mart as a store of bottled water, cleansed and packaged and structured for easy consumption, the Data Lake is a large body of water in a more natural state. [...] The contents of the Data Lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples." (James Dixon, "Pentaho, Hadoop, and Data Lakes", 2010) [sorce] [first known usage]

"A data lake represents an environment that collects and stores large volumes of structured and unstructured datasets, typically in their original, unaltered forms. More than a data depository, the data lake architecture enables the various users and data science teams to conduct data exploration and related analytical activities." (EMC Education Services, "Data Science & Big Data Analytics", 2015)

"A data lake strategy supports the introduction of a separate analytics environment that off-loads the analytics being done today on your overly expensive data warehouse. This separate analytics environment provides the data science team an on-demand, fail-fast environment for quickly ingesting and analyzing a wide variety of data sources in an attempt to address immediate business opportunities independent of the data warehouse's production schedule and service level agreement (SLA) rules." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"At its core, it is a data storage and processing repository in which all of the data in an organization can be placed so that every internal and external systems', partners', and collaborators' data flows into it and insights spring out. [...] Data Lake is a huge repository that holds every kind of data in its raw format until it is needed by anyone in the organization to analyze." (Beulah S Purra & Pradeep Pasupuleti, "Data Lake Development with Big Data", 2015) 

"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)

"[...] the real power of the data lake is to enable advanced analytics or data science on the detailed and complete history of data in an attempt to uncover new variables and metrics that are better predictors of business performance." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"The data lake is not an incremental enhancement to the data warehouse, and it is NOT data warehouse 2.0. The data lake enables entirely new capabilities that allow your organization to address data and analytic challenges that the data warehouse could not address." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"Unfortunately, some organizations are replicating the bad data warehouse practice by creating special-purpose data lakes - data lakes to address a specific business need. Resist that urge! Instead, source the data that is needed for that specific business need into an 'analytic sandbox' where the data scientists and the business users can collaborate to find those data variables and analytic models that are better predictors of the business performance. Within the 'analytic sandbox', the organization can bring together (ingest and integrate) the data that it wants to test, build the analytic models, test the model's goodness of fit, acquire new data, refine the analytic models, and retest the goodness of fit." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"A data lake is a storage repository that holds a very large amount of data, often from diverse sources, in native format until needed. In some respects, a data lake can be compared to a staging area of a data warehouse, but there are key differences. Just like a staging area, a data lake is a conglomeration point for raw data from diverse sources. However, a staging area only stores new data needed for addition to the data warehouse and is a transient data store. In contrast, a data lake typically stores all possible data that might be needed for an undefined amount of analysis and reporting, allowing analysts to explore new data relationships. In addition, a data lake is usually built on commodity hardware and software such as Hadoop, whereas traditional staging areas typically reside in structured databases that require specialized servers." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)

"A data warehouse follows a pre-built static structure to model source data. Any changes at the structural and configuration level must go through a stringent business review process and impact analysis. Data lakes are very agile. Consumption or analytical layer can be modified to fit in the model requirements. Consumers of a data lake are not constant; therefore, schema and modeling lies at the liberty of analysts and scientists." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data in the data lake should never get disposed. Data driven strategy must define steps to version the data and handle deletes and updates from the source systems." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"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)

"Data Lake induces accessibility and catalyzes availability. It warrants data discovery platforms to soak the data trends at a horizontal scale and produce visual insights. It largely cuts down the time that goes into data preparation and exhaustive data analysis." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data Lake is a single window snapshot of all enterprise data in its raw format, be it structured, semi-structured, or unstructured. Starting from curating the data ingestion pipeline to the transformation layer for analytical consumption, every aspect of data gets addressed in a data lake ecosystem. It is supposed to hold enormous volumes of data of varied structures." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data swamp, on the other hand, presents the devil side of a lake. A data lake in a state of anarchy is nothing but turns into a data swamp. It lacks stable data governance practices, lacks metadata management, and plays weak on ingestion framework. Uncontrolled and untracked access to source data may produce duplicate copies of data and impose pressure on storage systems." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data warehousing, as we are aware, is the traditional approach of consolidating data from multiple source systems and combining into one store that would serve as the source for analytical and business intelligence reporting. The concept of data warehousing resolved the problems of data heterogeneity and low-level integration. In terms of objectives, a data lake is no different from a data warehouse. Both are primary advocates of terms like 'single source of truth' and 'central data repository'." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"At first, we threw all of this data into a pit called the 'data lake'. But we soon discovered that merely throwing data into a pit was a pointless exercise. To be useful - to be analyzed - data needed to (1) be related to each other and (2) have its analytical infrastructure carefully arranged and made available to the end user. Unless we meet these two conditions, the data lake turns into a swamp, and swamps start to smell after a while. [...] In a data swamp, data just sits there are no one uses it. In the data swamp, data just rots over time." (Bill Inmon et al, "Building the Data Lakehouse", 2021)

"Data lake architecture suffers from complexity and deterioration. It creates complex and unwieldy pipelines of batch or streaming jobs operated by a central team of hyper-specialized data engineers. It deteriorates over time. Its unmanaged datasets, which are often untrusted and inaccessible, provide little value. The data lineage and dependencies are obscured and hard to track." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"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)

"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)

"When it comes to data lakes, some things usually stay constant: the storage and processing patterns. Change could come in any of the following ways: Adding new components and processing or consumption patterns to respond to new requirements. […] Optimizing existing architecture for better cost or performance" (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)

"A Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is without having first to structure the data and run different types of analytics - from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"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)

"The allure of Data Lakes was their ability to store vast amounts of raw data. However, this advantage can become counterproductive without stringent governance and management protocols. In their zeal to harness the power of Big Data, some organizations indiscriminately dump data into their lakes. Without proper classification, curation, and quality checks, these lakes can become swamps - murky repositories filled with valuable data, redundant information, and outdated datasets. Navigating these data swamps becomes a significant challenge, leading to prolonged data retrieval times, increased chances of using obsolete or incorrect data, and a decline in the agility and efficiency of data-driven decision-making processes rather than facilitating quick and insightful analytics." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"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 data lake is a distributed repository of raw and unprocessed data stored in its original format, with-out 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 democratiza-tion, 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 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)

"Data Lakes embrace a schema-on-read approach, storing vast volumes of raw or lightly processed data in native formats with minimal upfront constraints. This design significantly enhances ingestion velocity and accommodates diverse, unstructured, or semi-structured datasets. However, enforcing data quality at scale becomes more complex, as traditional static constraints are absent." (William Smith, "Great Expectations for Modern Data Quality: The Complete Guide for Developers and Engineers", 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)

"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)