Delta Lake: Enhancing Data Lakes with ACID Transactions and Performance

What is a Data Lake?

A Data Lake is a storage repository that holds vast amounts of raw data in its native format.

Advantages:

• Cost-efficient and scalable.
• Can store any kind of data (structured, unstructured, semi-structured).

Challenges:

• No ACID guarantees.
• No data validation, leading to data quality issues.
• Difficult to maintain historical versions.
• No DML operations (updates, deletes).

What is Delta Lake?

Delta Lake is an improvement on data lakes, providing a transactional layer to solve the challenges mentioned above.

• Delta Lake is an open-source storage layer that works with Apache Spark. It acts like a small utility installed on SparkCluster.
• Combines Parquet (storage format) with transaction logs to enable ACID transactions.

Delta Lake = Parquet + Transaction Logs:

• Transaction logs ensure consistency and reliability. They are updated only after a successful write operation.

Key Operations in Delta Lake:

Write Operation:

1. Data is written to part files.
2. A transaction log is updated after successful writes.

Example:

• Write → Part files + Transaction log (e.g., 0000.json).

Append Operation:

1. New data is added to part files.
2. The transaction log is updated with each append operation.

Example:

• Append → Transaction log (e.g., 0000.json, 0001.json).

Handling Failures in Delta Lake:

Scenario 1: Job Failing While Appending Data

• Data Lake: No atomicity; partial data may remain.
• Delta Lake: Uses transaction logs to ensure consistency and prevent reading partial data.

Scenario 2: Job Failing While Overwriting Data

• Data Lake: If a job fails during overwrite, previous data is lost, leading to an inconsistent state.
• Delta Lake: Old files are not deleted initially. New files are written first. Only after a successful write is the transaction log updated, ensuring consistency. If the job fails, previous data remains intact, avoiding data loss.

Scenario 3: Simultaneous Reads and Writes

• Delta Lake: Transaction logs ensure that only successfully written data is available for reading, preventing inconsistent reads.

Updates and Deletes in Delta Lake:

Updates:

1. A new part file is created with updated records.
2. Unchanged records are copied from the old part file to the new one.
3. The transaction log is updated to add the new file and remove the old one.

Deletes:

1. A new part file is created without the deleted records.
2. Remaining records are copied to the new part file.
3. The transaction log is updated to add the new file and remove the old one.

Scenario 4: Appending Data with a Different Schema

• Schema Evolution: Delta Lake handles schema changes smoothly, allowing different schema versions to coexist.
• Version History: Delta Lake supports Time Travel, enabling you to query previous versions of data.
• Data Quality: Commit logs ensure data quality through validation.
• Performance: Delta Lake optimizes performance using commit logs and data statistics.

Summary of Key Benefits:

• ACID Transactions: Guarantees consistency and reliability.
• Schema Enforcement and Evolution: Handles schema changes effectively.
• Time Travel: Ability to query historical versions.
• Support for Updates/Deletes: DML operations are possible.
• Data Quality: Ensures data integrity with commit logs.