Databricks is a powerful platform for big data analytics and machine learning. One of its key features is the ability to run transactional workloads over large-scale data lakes using Delta Lake. This post explores how transactions are supported in Databricks and how you can use them to ensure data consistency and integrity.

What Are Transactions in Databricks?

A transaction is a sequence of operations that is treated as a single unit of work. Transactions follow the ACID properties:

• Atomicity: All or nothing — either the whole transaction is committed, or none of it is.
• Consistency: The system moves from one valid state to another.
• Isolation: Concurrent transactions do not interfere with each other.
• Durability: Once committed, changes are persistent even in case of failures.

In Databricks, transactional support is provided only through Delta Lake, not through traditional file formats like Parquet or CSV.

Delta Lake: ACID for the Data Lake

Delta Lake is an open-source storage layer that brings ACID compliance to Apache Spark and Databricks. It does so by maintaining a transaction log (called _delta_log) that tracks changes to the table over time.

Features that enable transactions:

• Multi-table write support
• Optimistic concurrency control
• Schema enforcement and evolution
• Time Travel (versioned queries)
• Merge operations with automatic conflict resolution

Practical Example: Merge with Transactional Safety

Suppose you have a Delta table called sales and a new DataFrame new_sales that contains updated values. You can merge the data with a transactional operation:

MERGE INTO sales AS target
USING new_sales AS source
ON target.product_id = source.product_id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *

This operation:

• Executes atomically.
• Ensures that other users reading the table get a consistent snapshot.
• Writes are committed only if the full operation succeeds.

Transaction Control in Databricks SQL

Databricks SQL supports explicit transaction statements with Delta tables:

BEGIN TRANSACTION;
UPDATE sales SET amount = amount * 1.1 WHERE category = 'electronics';
DELETE FROM sales WHERE amount < 10;
COMMIT;

You can also use ROLLBACK if supported, although in practice most operations are managed atomically via DataFrame or SQL APIs without manual transaction demarcation.

Transaction Isolation and Concurrency

Delta Lake implements Snapshot Isolation, which means:

• Readers see a consistent snapshot of the data as of the start of their query.
• Writers use optimistic concurrency control, where a transaction will fail and retry if there’s a conflict with another write.

This allows high-performance concurrent reads and writes at scale, which is critical in multi-user, real-time environments.

When Transactions Don’t Work

If you’re using Parquet, CSV, or JSON tables without Delta Lake:

• No ACID guarantees are provided.
• Concurrent writes can corrupt data.
• Operations like MERGE, UPDATE, or DELETE are not supported.

Always convert your table to Delta format when transactionality is important.

df.write.format("delta").save("/path/to/delta/table")

Or use SQL:

CONVERT TO DELTA parquet.`/path/to/parquet/table`

Conclusion

Databricks offers strong transactional capabilities — but only when you’re working with Delta Lake. For scalable, reliable, and ACID-compliant data processing, Delta is essential. Whether you’re building data pipelines, running incremental updates, or cleaning records, Delta Lake ensures that your operations are safe, consistent, and traceable.

For production workloads involving multiple writes or critical updates, always validate that your tables are stored in Delta format and leverage the transactional features built into the platform.