Data formats in Databricks: A guide to Parquet, Delta Lake, and alternatives
- Databricks
- Databricks
- 3 min reading time
Dr. Andreas Wagner
Choosing the right data format is a critical but often underestimated factor for performance and efficiency in Databricks. The wrong choice can slow down queries and drive up storage costs. But what is the difference between a file format like Parquet and a technology like Delta Lake?
Table of contents
1. The core: Why Parquet is the preferred file format
Databricks is based on Apache Spark and supports a wide range of formats. However, the clear recommendation and de facto standard for analytical workloads is Apache Parquet.
As a column-oriented format, it stores data in column blocks rather than rows. This structure greatly speeds up analytical queries, as only the columns that are actually needed for analysis are read (this is known as "column pruning"). Parquet is also highly compressed, making it very storage-efficient.
2. The intelligence layer: Delta Lake on Parquet
This often leads to a misunderstanding: Delta Lake is not a file format, but rather an open metadata layer that builds on Parquet files. You can think of it like this:
- The Parquet files are the physical backbone. They store the actual data efficiently and in compressed form.
- Delta Lake is the "brain" that sits above it and gives Parquet files warehouse-like capabilities.
This intelligence is implemented through a JSON-based transaction log. It stores the "rules of the game" and the history of all changes. The four central components of this protocol are:
- ACID transactions: Guarantee data consistency, even with multiple simultaneous write operations.
- Schema validation: Prevents inconsistent data (e.g., incorrect data types) from being written and ensures clean tables.
- Time Travel: Enables access to previous versions of data, which is essential for audits or rolling back errors.
- Upserts and Deletes: Provides efficient methods for updating (merging) and deleting records.
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3. Alternatives and their use cases
Simple text formats (JSON, CSV): These are flexible and are often used for fast data ingestion from APIs or for uncomplicated exchange. However, due to their inefficiency with large amounts of data, they are not suitable for long-term storage or complex analyses.
Apache Iceberg & Apache Hudi: Similar to Delta Lake, these are not file formats, but rather "table formats" based on Parquet. They are open-source alternatives and also bring warehouse functions to the data lake.
- Iceberg is particularly strong at optimizing the performance of huge analytical tables.
- Hudi excels at incremental data processing and scenarios involving very frequent updates (upserts).
4. conclusion
In summary, technologies such as Delta Lake, Iceberg, and Hudi transform a simple data lake into a reliable, high-performance data lakehouse.
Your data strategy is individual - your consulting should be too
Choosing the right format and technology level depends heavily on your specific use cases—whether streaming data, large batch processing, or complex analysis workloads.
Let's talk about which architecture is right for your data and goals, with no obligation. Contact us for a personal consultation.
