Data Catalog vs. Data Warehouse: Differences, and How They Work Together?

What comes first: the warehouse or the catalog? A data stack chicken and egg #

Once organizations are convinced of the importance of both a data warehouse and a data catalog, the next question is:

“Do we build the data warehouse or the data catalog first?”

That’s a classic chicken-and-egg situation for the data stack. The answer: build them simultaneously.

Here’s why. If you wait until the warehouse setup is complete, you’ll have to collect numerous requirements and map all the assets painstakingly — a process that could take months, even years. Even after all that effort, you might not have accounted for all possible scenarios or use cases.

Then comes the enormous knowledge debt to be documented — another seemingly endless task.

On the other hand, if you begin with the catalog, you’ll have to weed through countless data sources before narrowing down on the essential assets. Moreover, you won’t see the results of the cataloging until you’ve ingested, transformed, and made the assets analytics-ready in a warehouse.

That’s why the best way forward is to set up the data catalog while rolling out your data warehouse, which brings us to the next question: “how do they work together?”

Data catalog and data warehouse: How do they work together? #

Generally, data warehouses maintain an internal data catalog. So, your data catalog should extract the system-maintained schemas using SQL queries or API connectors. These schemas contain all the metadata you need to lend granular context to your data assets.

Most data warehouses provide access using a JDBC/ODBC connection, native connectors, or low-level client APIs.

So, the first step is to outline the data sources you need for each use case and document them to decide upon the necessary architectural choices.

At this point, it’s also vital to note that connecting to a data source and pulling metadata puts extra load on that source and affects its read-write performance. So, make sure that you consider these aspects while designing the architecture.

After setting up the architecture, connect the data sources to a modern data catalog. The data catalog will serve as the collaboration, knowledge-capture, and governance layer for your modern data platform that informs your teams about the right assets from the relevant warehouses in real time.

Lastly, optimize the entire process to get the desired results for your pilot use cases, and then set up scalable processes to accommodate more use cases.

Now let’s look at a case study to see the impact of a cloud data warehouse and data catalog on the data team.

A Guide to Building a Business Case for a Data Catalog

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How a fashion retailer leveraged their warehouse and catalog to build a data-centric business #

In 2020, fashion retailer Techstyle began an overhaul of its data stack by moving to Snowflake for its data warehousing needs.

However, when Techstyle started rolling out the new system, they encountered a bottleneck — most of their team couldn’t understand the data or use it independently. In addition, only a few long-standing employees possessed all the knowledge required to extract value from the Snowflake data. The main issue was little documentation for their data.

So, Techstyle embarked on a new mission — documenting the warehouse data using Atlan’s modern data catalog. The process was an iterative and collaborative approach involving all the stakeholders.

As a result, Techstyle is now a data-driven business with a centralized data platform and embedded analysts in each brand team. Analysts embedded in different brand teams across the company can find the data they need and make sense of how to use it. Getting new users up to speed has also become much quicker.

Since Techstyle rolled out its modern data warehouse and used a data catalog to make data discoverable and understandable to everyone, they were able to experience the value of the data being collected by the business.

Are you looking for a data catalog to document and add context to data assets in your data warehouse? — you might want to check out Atlan.