🎯Fadi Maali - Collected Quotes

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

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

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

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

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

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

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

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

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

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

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

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

💫🗒️ERP Systems: Microsoft Dynamics 365's Business Process Catalog (BPC) [Notes]

Disclaimer: This is work in progress intended to consolidate information from the various sources and not to provide a complete overview of all the features. Please refer to the documentation for a complete overview!

Last updated: 25-Apr-2025

Business Process Catalog - End-to-End Scenarios

[Dynamics 365] Business Process Catalog (BPC)

• {def} lists of end-to-end processes that are commonly used to manage or support work within an organization [1]
• agnostic catalog of business processes contained within the entire D365 solution space [3]
• {benefit} efficiency and time savings [3]
• {benefit} best practices [3]
• {benefit} reduced risk [3]
• {benefit} technology alignment [3]
• {benefit} scalability [3]
• {benefit} cross-industry applicability [3]
• stored in an Excel workbook
• used to organize and prioritize the work on the business process documentation [1]
• {recommendation} check the latest versions (see [R1])
• assigns unique IDs to 
• {concept} end-to-end scenario
• describe in business terms 
• not in terms of software technology
• includes the high-level products and features that map to the process [3]
• covers two or more business process areas
• {purpose} map products and features to benefits that can be understood in business contexts [3]
• {concept} business process areas
• combination of business language and basic D365 terminology [3]
• groups business processes for easier searching and navigation [1]
• separated by major job functions or departments in an organization [1]
• {purpose} map concepts to benefits that can be understood in business context [3]
• more than 90 business process areas defined [1]
• {concept} business processes
• a series of structured activities and tasks that organizations use to achieve specific goals and objectives [3]
• efficiency and productivity
• consistency and quality
• cost reduction
• risk management
• scalability
• data-driven decision-making
• a set of tasks in a sequence that is completed to achieve a specific objective [5]
• define when each step is done in the implementation [5] [3]
• define how many are needed [5] [3]
• covers a wide range of structured, often sequenced, activities or tasks to achieve a predetermined organizational goal
• can refer to the cumulative effects of all steps progressing toward a business goal
• describes a function or process that D365 supports
• more than 700 business processes identified
• {goal} provide a single entry point with links to relevant product-specific content [1]
• {concept} business process guide
• provides documentation on the structure and patterns of the process along with guidance on how to use them in a process-oriented implementation [3]
• based on a catalog of business process supported by D365 [3]
• {concept} process steps 
• represented sequentially, top to bottom
• can include hyperlinks to the product documentation [5] 
• {recommendation} avoid back and forth in the steps as much as possible [5]
• can be
• forms used in D365 [5]
• steps completed in LCS, PPAC, Azure or other Microsoft products [5]
• steps that are done outside the system (incl. third-party system) [5]
• steps that are done manually [5]
• are not 
• product documentation [5]
• a list of each click to perform a task [5]
• {concept} process states
• include
• project phase 
• e.g. strategize, initialize, develop, prepare, operate
• configuration 
• e.g. base, foundation, optional
• process type
• e.g. configuration, operational
• {concept} patterns
• repeatable configurations that support a specific business process [1]
• specific way of setting up D365 to achieve an objective [1]
• address specific challenges in implementations and are based on a specific scenario or best practice [6]
• the solution is embedded into the application [6]
• includes high-level process steps [6]
• include the most common use cases, scenarios, and industries [1]
• {goal} provide a baseline for implementations
• more than 2000 patterns, and we expect that number to grow significantly over time [1]
• {activity} naming a new pattern
• starts with a verb
• describes a process
• includes product names
• indicate the industry
• indicate AppSource products
• {concept} reference architecture 
• acts as a core architecture with a common solution that applies to many scenarios [6]
• typically used for integrations to external solutions [6]
• must include an architecture diagram [6]
• {concept} process governance
• {benefit} improved quality
• {benefit} enhanced decision making
• {benefit} agility adaptability
• {benefit{ Sbd alignment
• {goal} enhance efficiency 
• {goal} ensure compliance 
• {goal} facilitate accountability 
• {concept} policy
• {concept} procedure
• {concept} control
• {concept} scope definition
• {recommendation} avoid replicating current processes without considering future needs [4]
• {risk} replicating processes in the new system without re-evaluating and optimizing [4] 
• {impact} missed opportunities for process improvement [4]
• {recommendation} align processes with overarching business goals rather than the limitations of the current system [4]
• {concept} guidance hub
• a central landing spot for D365 guidance and tools
• contains cross-application documentations
• {purpose} provide considerations and best practices for implementation [6]
• {purpose} provide technical information for implementation [6]
• {purpose} provide link to product documentation to achieve the tasks in scope [6]

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References:

[1] Microsoft Learn (2024) Dynamics 365: Overview of end-to-end scenarios and business processes in Dynamics 365 [link]
[2] Microsoft Dynamics 365 Community (2023) Business Process Guides - Business Process Guides [link]
[3] Microsoft Dynamics 365 Community (2024) Business Process Catalog and Guidance - Part 2 Introduction to Business Processes [link]

[4] Microsoft Dynamics 365 Community (2024) Business Process Catalog and Guidance - Part 3: Using the Business Process Catalog to Manage Project Scope and Estimation [link]

[5] Microsoft Dynamics 365 Community (2024) Business Process Catalog and Guidance - Part 4: Authoring Business Processes [link]

[6] Microsoft Dynamics 365 Community (2024) Business Process Catalog and Guidance - Part 5:  Authoring Business Processes Patterns and Use Cases [link]
[7] Microsoft Dynamics 365 Community (2024) Business Process Catalog and Guidance  - Part 6: Conducting Process-Centric Discovery [link]
[8] Microsoft Dynamics 365 Community (2024) Business Process Catalog and Guidance  - Part 7: Introduction to Process Governance [link]

Resources:

[R1] GitHub (2024) Business Process Catalog [link]

[R2] Microsoft Learn (2024) Dynamics 365 guidance documentation and other resources [link]
[R3] Dynamics 365 Blog (2025) Process, meet product: The business process catalog for Dynamics 365 [link]

Acronyms:
3T - Tools, Techniques, Tips

ADO - 
BPC - Business Process Catalog

D365 - Dynamics 365

LCS - Lifecycle Services

PPAC - Power Platform admin center
RFI - Request for Information
RFP - Request for Proposal

💎🏭SQL Reloaded: Microsoft Fabric's Delta Tables in Action - Data Objects Metadata

There seem to be four main sources for learning about the functionality available in the Delta Lake especially in what concerns the SQL dialect used by the Spark SQL: Databricks [1], Delta Lake [2], Azure Databricks [3], respectively the Data Engineering documentation in Microsoft Fabric [4] and the afferent certification material. Unfortunately, the latter focuses more on PySpark. So, until Microsoft addresses the gap, one can consult the other sources, check what's working and built thus the required knowledge for handling the various tasks. 

First of all, it's important to understand which the data objects available in Microsoft Fabric are. Based on [5] I could identify the following hierarchy:

According to the same source [5] the metastore contains all of the metadata that defines data objects in the lakehouse, while the catalog is the highest abstraction in the lakehouse. The database, called also a schema, keeps its standard definition - a collection of data objects, such as tables or views (aka relations) and functions. The tables, views and functions keep their standard definitions. Except the metastore, these are also the (securable) objects on which permissions can be set. 

One can explore the structure in Spark SQL by using the SHOW command:

-- explore the data objects from the lakehouse
SHOW CATALOGS;

SHOW DATABASES;

SHOW SCHEMAS;

SHOW CATALOGS;

SHOW VIEWS;

SHOW TABLES;

SHOW FUNCTIONS;

Moreover, one can list only the objects from an object from the parent (e.g. the tables existing in a database):

 

-- all tables from a database
SHOW TABLES FROM Testing;

-- all tables from a database matching a pattern
SHOW TABLES FROM Testing LIKE 'Asset*';

-- all tables from a database matching multiple patterns
SHOW TABLES FROM Testing LIKE 'Asset*|cit*';

Notes:
1) Same syntax applies for views and functions, respectively for the other objects in respect to their parents (from the hierarchy). 
2) Instead of FROM one can use the IN keyword, though I'm not sure what's the difference given that they seem to return same results.
3) For databases and tables one can use also the SQL Server to export the related metadata. Unfortunately, it's not the case for views and functions because when the respective objects are created in Spark SQL, they aren't visible over the SQL Endpoint, and vice versa.

4) Given that there are multiple environments that deal with delta tables, to minimize the confusion that might result from their use, it makes sense to use database as term instead of schema. 

References:
[1] Databricks (2024) SQL language reference (link)
[2] Delta Lake (2023) Delta Lake documentation (link)
[3] Microsoft Learn (2023) Azure Databricks documentation (link)
[4] Microsoft Learn (2023) Data Engineering documentation in Microsoft Fabric (link)
[5] Databricks (2023) Data objects in the Databricks lakehouse (link)

⛏️Data Management: Data Dictionary (Definitions)

"The system tables that contain descriptions of the database objects and how they are structured." (Karen Paulsell et al, "Sybase SQL Server: Performance and Tuning Guide", 1996)

"A set of system tables stored in a catalog. A data dictionary includes definitions of database structures and related information, such as permissions." (Anthony Sequeira & Brian Alderman, "The SQL Server 2000 Book", 2003)

"Software in which metadata is stored, manipulated and defined – a data dictionary is normally associated with a tool used to support software engineering." (Keith Gordon, "Principles of Data Management", 2007)

"A list of descriptions of data items to help developers stay on the same track." (Rod Stephens, "Beginning Database Design Solutions", 2008)

"The place where information about data that exists in the organization is stored. This should include both technical and business details about each data element." (Laura Reeves, "A Manager's Guide to Data Warehousing", 2009)

"Data dictionary are mini database management systems that manages metadata. It is a repository of information about a database that documents data elements of a database. The data dictionary is an integral part of the database management systems and stores metadata or information about the database, attribute names and definitions for each table in the database." (Vijay K Pallaw, "Database Management Systems" 2nd Ed., 2010)

"In the days of mainframe computers, this was a listing of record layouts, describing each field in each type of file." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"Software coupled with a data store for managing data definitions." (Craig S Mullins, "Database Administration", 2012)

"A database containing data about all the databases in a database system. Data dictionaries store all the various schema and file specifications and their locations. They also contain information about which programs use which data and which users are interested in which reports." (SQL Server 2012 Glossary, "Microsoft", 2012)

"A reference by which a team can understand what data assets they have, how those assets were created, what they mean, and where to find them." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"A repository of the metadata useful to the corporation" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"A comprehensive record of business and technical definitions of the elements within a dataset. Also referred to as a business glossary." (Jonathan Ferrar et al, "The Power of People", 2017)

"A database containing data about all the databases in a database system. Data dictionaries store all the various schema and file specifications and their locations." (BAAN)

"A read-only collection of database tables and views containing reference information about the database, its structures, and its users." (Oracle)

"A set of system tables, stored in a catalog, that includes definitions of database structures and related information, such as permissions." (Microsoft Technet)

"A set of tables that keep track of the structure of both the database and the inventory of database objects." (IBM)

"A specialized type of database containing metadata; a repository of information describing the characteristics of data used to design, monitor, document, protect, and control data in information systems and databases; an application system supporting the definition and management of database metadata." (TOGAF)

"Metadata that keeps track of database objects such as tables, indexes, and table columns." (MySQL)

🔹SQL Server: Full-Text Catalog (Definitions)

"A catalog that stores a database's full-text index." (Microsoft Corporation, "SQL Server 7.0 System Administration Training Kit", 1999)

"The full-text catalog stores all of the full-text indexes for tables within a database." (Anthony Sequeira & Brian Alderman, "The SQL Server 2000 Book", 2003)

"A Full-Text catalog is a special storage space used to house Full-Text indexes. By default, all Full-Text indexes are housed in a single catalog." (Thomas Moore, "EXAM CRAM™ 2: Designing and Implementing Databases with SQL Server 2000 Enterprise Edition", 2005)

"A special storage space used to house Full-Text indexes. By default, all Full-Text indexes are housed in a single catalog." (Thomas Moore, "MCTS 70-431: Implementing and Maintaining Microsoft SQL Server 2005", 2006)

"A full-text catalog is a logical grouping of SQL Server full-text indexes for management purposes." (Michael Coles, "Pro T-SQL 2008 Programmer's Guide", 2008)

"A collection of full-text index components and other files that are organized in a specific directory structure and contain the data that is needed to perform queries." (Microsoft, "SQL Server 2012 Glossary", 2012)

"A full-text catalog is a logical grouping of SQL Server full-text indexes for management purposes." (Jay Natarajan et al, "Pro T-SQL 2012 Programmer's Guide" 3rd Ed., 2012)

"A logical grouping of SQL Server full-text indexes for management purposes." (Miguel Cebollero et al, "Pro T-SQL Programmer’s Guide" 4th Ed., 2015)

🛢DBMS: Database Catalog [DBC] (Definitions)

"Tables provided in the Model database." (Owen Williams, "MCSE TestPrep: SQL Server 6.5 Design and Implementation", 1998)

"The set of system tables in a database that describes the database contents." (Microsoft Corporation, "SQL Server 7.0 System Administration Training Kit", 1999)

"A database catalog contains the definition of all the objects in the database, as well as the definition of the database." (Anthony Sequeira & Brian Alderman, "The SQL Server 2000 Book", 2003)

"This is an object in a database that is used to organize full-text indexes." (Joseph L Jorden & Dandy Weyn, "MCTS Microsoft SQL Server 2005: Implementation and Maintenance Study Guide - Exam 70-431", 2006)

"A directory storing metadata." (Rod Stephens, "Beginning Database Design Solutions", 2008)

"A group of schemas, usually composed of all schemas handled by a single DBMS." (Jan L Harrington, "SQL Clearly Explained 3rd Ed. ", 2010)

"The part of a database that contains the definition of all the objects in the database, as well as the definition of the database." (Microsoft, "SQL Server 2012 Glossary", 2012)

"Structured list made up of comparable objects, used as a reference." (Gilbert Raymond & Philippe Desfray, "Modeling Enterprise Architecture with TOGAF", 2014)

"A collection of tables and views that contains descriptions of objects such as tables, views, and indexes." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)