Databricks is a data and AI platform that allows users to query data in various ways. Here’s a breakdown of querying in Databricks:

Databricks Lakehouse:

• Default Storage: Databricks primarily store data in a lakehouse, which combines the best features of data lakes and data warehouses. Data in a lakehouse is:
  • They are stored in the Delta Lake format for reliability and performance.
  • For scalability, they are stored in cloud object storage (like AWS S3 or Azure Blob Storage).
  • Unity Catalog governs it for centralized access control and security.
• Querying Lakehouse Data: You can query lakehouse data using standard SQL syntax through Databricks notebooks, Databricks SQL, or partner tools.

External Data:

• Querying by Path: Using file paths, you can also query structured, semi-structured, and unstructured data located in external sources. Databricks recommends configuring access to external data sources through the Unity Catalog for better security and governance.

Methods for Querying:

• Databricks Notebooks: Interactive environments where you can write and execute code (including SQL queries) in various languages (Python, R, Scala, SQL).
• Databricks SQL: A serverless data warehouse optimized for SQL analytics on the lakehouse.
• Partner Tools: Databricks integrates with various BI and data visualization tools, allowing you to query and analyze data using familiar interfaces.
• REST API, JDBC/ODBC Connectors: For programmatic access to Databricks data and querying from custom applications.

Getting Started:

1. Create a Notebook: Start by creating a new notebook in Databricks.
2. Connect to Compute: Choose or create a compute resource (cluster) to execute your queries.
3. Write Your Query: Use standard SQL syntax to query your data. For example: SQL
4. SELECT * FROM my_table WHERE column1 > 10;
5. Execute and Visualize: Run your query and explore the results within the notebook using built-in visualizations or external tools.