Delta Lake
Delta Lake is an open-source storage framework that brings ACID transactions, schema evolution, and time travel to data lakes at petabyte scale.
Delta Lake is a versatile, open-source storage framework that transforms data lakes into reliable lakehouses with full transactional guarantees.
Key Features:
- ACID Transactions - Uses optimistic concurrency control for writes and consistent snapshots for readers on supported storage.
- Time Travel - Access and revert to earlier data versions for audits, rollbacks, and reproducibility.
- Schema Evolution - Safely evolve table schemas without rewriting existing data.
- UniForm - Read Delta tables with Iceberg and Hudi clients for supported lakehouse clients.
- Unified Batch and Streaming - Ingest and query data with exactly-once semantics across both modes.
- Multi-Engine Support - Works with Spark, Flink, Trino, Snowflake, BigQuery, and more.
Whether you're building ETL pipelines, unifying batch and stream processing, or managing petabyte-scale lakes, Delta Lake provides production-ready lakehouse storage.
Web scraping workflow
How to use Delta Lake in a web scraping pipeline
Delta Lake adds transactional table behavior, schema enforcement and version history to data-lake storage. It can manage cleaned scraping outputs when recurring crawls update a shared analytical dataset.
A practical workflow
- A separate scraper produces validated records with source identifiers and collection timestamps.
- A compatible processing engine writes or merges those records into a Delta table under an explicit entity-key and schema policy.
- Downstream queries read the table or a retained version to compare dataset states and build reports.
When it fits
Use it when batch or streaming collection outputs need one managed table rather than unrelated files. Define whether a new observation replaces current state or belongs in a separate history table before choosing merge behavior.
Boundaries
Delta Lake does not acquire pages or supply extraction logic. Compatible storage and processing engines, retention and maintenance remain part of the system. Schema enforcement can reject structural problems, but it cannot tell whether a price or article field was extracted correctly; retain source captures and apply domain-level validation separately.
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Our productRepository
delta-io/delta
Stars
9KForks
2.2KLast commit
2026-10-15License
Apache-2.0Language
Scala
Metrics recorded 2026-10-16
Repository details
License source: Repository license file
Frequently asked questions
What is Delta Lake used for?
Delta Lake adds a table layer to data-lake storage, including transactions, schema handling and versioned data. It is used to build reliable batch and streaming tables accessed through compatible engines.
Sources (checked ): Delta Lake introduction
How does Delta Lake work?
Delta Lake combines data files with a transaction log that records table changes. Compatible engines use that log to read a consistent table state and perform supported updates. The log and metadata add table semantics beyond a directory of files.
Sources (checked ): Delta Lake introduction; Delta Lake quickstart
Is Delta Lake only for Databricks?
No. Delta Lake is an open-source project with integrations for multiple processing engines. Databricks is a commercial platform that uses and supports Delta Lake; the platform and the table project are separate.
Sources (checked ): Delta Lake introduction
What are the key differences between Delta Lake, Iceberg, and Parquet file formats?
Delta Lake and Apache Iceberg add table metadata, transaction handling and schema evolution around data files. Parquet is a columnar file format that those table formats can use. A Parquet file alone does not provide the table-level coordination of Delta Lake or Iceberg.
Sources (checked ): Delta Lake introduction; Apache Iceberg documentation; Apache Parquet overview
What is the difference between Azure Data Lake and Delta Lake?
Azure Data Lake Storage provides cloud storage features on Azure Blob Storage. Delta Lake provides a table format and transaction layer used over storage through compatible engines. They can be used together; one is a storage service and the other defines table behavior.
Sources (checked ): Delta Lake introduction; Azure Data Lake Storage
Sources
Transaction description checked against Delta Lake concurrency-control documentation on 2026-10-01; engine and storage support vary.