The difference:
A data dictionary holds the metadata for your database. In simple terms, a data dictionary is documentation for your database. It helps engineers and analysts get the context behind tables, columns, and data fields. Without data dictionaries, one has to rely on people or read through codebases and SQL queries.
A data catalog is a tool that helps index, inventory, and classify data assets across multiple data sources in your enterprise. A data catalog adds a context layer with a focus on discovery, search, metadata management, lineage, collaboration, and governance.
Let’s explore in detail the differences between a data dictionary and a data catalog and how both of them plays a role in adding context to data.
What is a data dictionary?
A data dictionary provides information and insights about your database. Consider a data dictionary to be a documentation for databases.
A data dictionary provides insights on
- Tables names and descriptions
- Table relationships
- Column name and descriptions
- Permissible values for a field
- Data types
- Column nullability
- Referential constraints — foreign keys and primary keys
- Column statistics — missing values, min-max values, and histogram distribution.
- Data and time when the property was created or changed
- Data owner
A data dictionary provides information about the database’s structure, data elements, constraints and relationships. Image by Atlan
What are the benefits of a data dictionary?
- Enables quick detection of anomalies and errors and hence helps keep a check on data quality.
- Check how and where a field is referenced across the entire database.
- Provides a framework for programming and database standards to maintain data integrity.
- Helps evaluate data consistency during security and compliance audits.
- A data dictionary acts as self-serve documentation for new engineers/analysts.
- Data dictionaries can be accessed externally through APIs for reporting and cataloging purposes.
What is a data catalog?
A data catalog is an inventory of data assets across all your data sources in your enterprise. It helps organizations discover, understand, and consume data better — all in one place.
A data catalog helps finding answers to:
- What data do we have?
- Where does it come from?
- Who is the owner?
- How clean is the data are there any gaps?
- How it is classified?
- Is the data good enough for running analysis?
Features of a data catalog
Fundamentally, what does a data catalog do? A data catalog reduces the time to insight for data users. It ensures:
- Data is made readily accessible
- Context is provided
- Data lifecycle is visible
- Access permissions are defined
Catalogs make data accessible
A data catalog automatically crawls, identifies, inventories, and classifies data assets from multiple sources. Data catalog tools allow you to run a search across data lakes, data warehouses, databases, tables, columns, SQL queries, and business glossaries.
Modern data catalogs have google-like search interfaces that respond to text based search for data assets. Image by Atlan
Catalogs provide context
People with no context of the data can learn more about it to ascertain that they have the right data.
Modern data catalogs come with in-built business glossaries to ensure common understanding of data asset and its usage across the organization. Image by Atlan
Catalogs helps visualize data lifecycle
Data catalogs enable you to visualize the complete lifecycle of a data asset, its transformation, and dependency both upstream and downstream.
The best of modern data catalogs auto-construct visual lineage of data to give an understanding of how data has evolved through its lifecycle and how changing the data will impact downstream. Image by Atlan
Catalogs enable data governance
A data catalog helps enforce robust access control policies as guard rails to help you protect the confidentiality and comply with various data protection regulations.
Modern data catalogs help deploy best-in-class data access governance without compromising on data democratization. Image by Atlan
When does an organization need a data catalog?
It's safe to say, deploying a data catalog is a right of passage for an organization to be truly data-driven. As a business, you can collect all the data that you want, set up best-in-class infrastructure to store that data, but data in itself is nothing. Just numbers.
You need the right data to reach the right person at the right time - for it to really move the needle on your business. Modern Data Catalogs are being designed to ensure that the complexities and scale of data do not deter "non-data" folks from using data in their day-to-day work.
Read in-depth about data catalogs.
Data catalogs vs. data dictionaries in the real world
As more and more data teams are feeling the need and adopting data catalogs, the difference between a catalog and a dictionary is fast vanishing — and becoming more complementary — because catalog tools now crawl and inventory data dictionaries for metadata. Data dictionaries are now an integral part of a data catalog.
Just in case if you are evaluating a data catalog, data dictionary, and metadata management for your team do take Atlan for a spin.
Atlan is a modern data catalog built on the premise of embedded collaboration that is key in today’s modern workplace, borrowing principles from GitHub, Figma, Slack, Notion, Superhuman, and other modern tools that are commonplace today.
Data dictionary vs. Data catalog: Related reads
- Business glossary vs. Data catalog: The definitions, differences, and examples.
- Data inventory vs. Data catalog: Understand the difference.
- What is a data dictionary, and why do you need one?
- Modern data catalogs: 5 essential features and evaluation guide.
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