Databricks Unity Catalog Explained for SQL Server Folks

If you’re coming from a SQL Server background, think of Databricks Unity Catalog as a centralized metadata management system that simplifies how data is organized, governed, and shared across different data platforms. It’s designed for modern data lakes (such as those in Azure and AWS) but incorporates familiar concepts for SQL users.

Let’s break it down step by step:

1. Databases and Schemas in Unity Catalog

• In SQL Server, you have databases and within those databases, you have schemas that organize your tables.
• Similarly, in Unity Catalog:
  • Catalogs are like databases.
  • Inside each Catalog, you have Schemas (similar to schemas in SQL Server), which contain Tables.

Example: In SQL Server, you might query like this:

SELECT * FROM SalesDB.dbo.Customers;

In Databricks Unity Catalog, it would look like this:

SELECT * FROM main.sales.customers;

• Here, main is the catalog, sales is the schema, and customers is the table.

2. Table Management

• Just like in SQL Server, you can create tables, update data, or delete tables. However, Unity Catalog manages tables in a data lake, allowing you to treat files (like Parquet, Delta, etc.) as if they are tables.
• It supports different data formats such as Delta Lake (Databricks’ optimized data format), which provides ACID transactions similar to a traditional relational database.

Example:

• Creating a table in Unity Catalog:

CREATE TABLE main.sales.customers (id INT, name STRING, email STRING);

3. Data Governance and Access Control

• In SQL Server, you assign roles and permissions at the database, schema, or table level using GRANT or DENY commands.
• In Unity Catalog, you do something similar but with more fine-grained control. You can assign permissions (like SELECT, INSERT, UPDATE) to users, groups, or service principals for tables, views, and even columns.

Example:

• Granting permission in SQL Server:

GRANT SELECT ON dbo.Customers TO [User];

• In Unity Catalog, it would be:

GRANT SELECT ON main.sales.customers TO 'user@example.com';

4. Collaboration and Sharing

• Unity Catalog allows easy sharing of data across teams, departments, or even external partners. Think of it as a way to manage “shared databases” but in the cloud.
• You can share data with other organizations or within your company securely, without moving the data around.

Example:

• Sharing data with an external partner in Unity Catalog could be done by defining external tables or data shares.

5. Data Lineage

• Unity Catalog offers data lineage tracking, which shows how data flows from one table or dataset to another. This is like using SQL Server’s Extended Events or Data Audits to track changes or activity, but it’s more visual and automatic.

6. Data Lakehouse Concept

• SQL Server is typically used with structured data in a data warehouse. Unity Catalog fits into the data lakehouse model, which is a mix of the traditional data warehouse and a data lake.
• This means that Unity Catalog can handle structured (like tables) and unstructured (like files or blobs) data all in one place, providing more flexibility.

Key Differences vs SQL Server:

• Decentralized Storage: Unlike SQL Server where everything is stored in a single database engine, Unity Catalog is built on top of data lakes (cloud-based storage like Azure Data Lake or AWS S3).
• Scalability: Unity Catalog is designed to scale out for big data scenarios, handling petabytes of data, much more than a traditional SQL Server setup.
• Cloud-Native: Unity Catalog integrates deeply with cloud storage, making it more versatile for handling both structured and unstructured data.

Summary Table

In Summary:

Databricks Unity Catalog allows SQL users to manage and govern data in a data lake just like they would in a database, with added capabilities for big data, data sharing, and data lineage tracking. The concept of Catalogs and Schemas makes it feel familiar, and its cloud-native architecture supports the flexibility and scalability required for modern data platforms.