Unlocking the Power of Databricks SQL – Querying Across Multiple Data Lakes with Ease

Published On - May 23, 2025

Satya Pal Databricks

In the ever-evolving world of big data and cloud-native analytics, Databricks SQL has emerged as a game-changer for data teams aiming to simplify access, accelerate insights, and reduce infrastructure friction — especially when dealing with multiple data lakes.

Modern organizations often work with data sprawled across:

  • AWS S3 (raw event logs)

  • Azure Data Lake (processed ETL outputs)

  • Google Cloud Storage (ML model outputs, partner data)

This fragmentation, while inevitable in multi-cloud or hybrid-cloud setups, often creates barriers to unified analytics.

Databricks SQL is a serverless, high-performance query engine built on Delta Lake. It allows analysts and engineers to run SQL queries across large datasets stored in cloud object stores — all without moving or transforming the data upfront.

Key benefits:

  • Serverless execution – No cluster headaches

  • Delta Lake optimization – ACID transactions, time travel, schema enforcement

  • BI connectivity – Native integration with Tableau, Power BI, and JDBC

  • Multi-cloud support – Query data in S3, ADLS, GCS seamlessly

 

Common Use Cases for Cross-Data Lake Queries

1. Customer 360 from Disparate Sources

Imagine user logs stored in AWS, CRM data in Azure, and product interaction logs in GCP.

With Databricks SQL, you can:

SELECT u.user_id, u.email, c.last_purchase_date, p.last_page_view
FROM aws_s3_logs.users u
JOIN azure_crm.customers c ON u.user_id = c.user_id
JOIN gcs_data.product_interactions p ON u.user_id = p.user_id
WHERE c.status = ‘Active’;

2. Multi-Region Sales Analytics

Sales data stored in regional lakes (e.g., India in ADLS, US in S3)

SELECT region, SUM(sales_amount) as total_sales
FROM (
SELECT ‘India’ as region, * FROM adls_sales.india_sales
UNION ALL
SELECT ‘US’ as region, * FROM s3_sales.us_sales
)
GROUP BY region;

3. Time-Travel for Audits and ML Drift Detection

SELECT *
FROM delta.`s3://sales-data/delta_table`
VERSION AS OF 112;

Perfect for reproducibility and audit trails.

Why Databricks SQL Wins in Multi-Lake Environments

  • Unified Metadata Layer via Unity Catalog: Define and manage data once across all clouds.

  • No-Code to Pro-Code: Analysts can use SQL, engineers can plug in Spark or Python.

  • Scalability: Designed for petabyte-scale queries with built-in caching and optimization.

With Databricks SQL, querying across disparate data lakes is no longer a messy integration challenge. It brings:

  • Simplicity for analysts

  • Speed for data teams

  • Scale for enterprises

As organizations continue to adopt multi-cloud strategies, Databricks SQL becomes the bridge between storage silos and actionable insights.