Introduction to Databricks Delta

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A component of the Databricks Unified Analytics Platform, Databricks Delta is an analytics engine that provides a powerful transactional storage layer built on Apache Spark. It helps users build robust production data pipelines at scale, giving end users a consistent view of the data.

Advantages of Databricks Delta

There are multiple benefits of using databricks delta which is following

  • Query performance
  • Data reliability
  • System complexity
  • Simple transition from Spark to Delta
  • Time travel

Query performance

Delta uses the following techniques to provide 10x to 100x faster query performance on Parquet than Apache Spark.

  • Delta creates and maintains indexes on the tables.
  • Delta manages file sizes of the underlying Parquet files for the most efficient use.
  • Delta automatically caches frequently accessed data to improve runtime for frequently executed queries.
  • Delta creates and maintains indexes on the tables.

Data reliability

To achieve data reliability, delta uses various techniques likes –

  • Delta follows ACID transactions. This implies an all-or-nothing approach to data consistency.
  • Allows multiple authors to write to a dataset concurrently without interrupting the dataset read job.
  • Schema enforcement is another technique to improve data integrity.
  • If there are multiple incoming and outgoing streams, delta ensures that the data is delivered and the checkpoint is read only once.

System complexity

  • Delta can write batch and streaming data to the same table, allowing for a simpler architecture and faster data ingestion into query results.
  • Delta provides the ability to infer the schema of data input, reducing the overhead of managing schema changes.

Switching from Parquet to Delta is as easy as replacing code that points to “Paquet” with “Delta”. Delta tables contain metadata and additional features such as upserts, so less custom coding is required.

Time Travel

Delta Lake time travel allows us to query older snapshots of Delta Lake tables. Time travel has many use cases, including:

  • Time travel facilitates the rollback of bad writes and plays a vital role in correcting errors in data.
  • Useful for recreating analyses, reports, or outputs.
  • It also simplifies time-series analytics.

Drawbacks of Databricks Delta

  • Only available as part of the Databricks ecosystem.
  • Delta does not support multi-table transactions and foreign keys.
  • Available on AWS and Azure, but not on GCP.
  • The transaction logs used to achieve atomicity are only available through Databricks.

Some more features of delta

Schema management :

Delta Lake automatically checks whether the schema of the DataFrame being written is compatible with the schema of the table. Columns that exist in the table but not in the DataFrame are set to null.

This operation throws an exception if the DataFrame has additional columns that are not present in the table. Delta Lake has DDL to explicitly add new columns and the ability to automatically update the schema.

Record update and deletion

Delta Lake supports merge, update, and delete DML commands. This allows engineers to easily update and delete records in the data lake, simplifying change data capture and GDPR use cases.

Delta Lake tracks and modifies data at the file level, making it much more efficient than
reading and overwriting entire partitions or tables.


In this blog, we’ve learned about the databricks delta and its advantages and drawbacks. We also explored some others features of databricks delta.
Hope you enjoyed the blog. Thanks for reading.