Delta Lake

Databricks implemented the data Lakehouse architecture through its open-source project Delta Lake.

Delta Lake is a storage layer format open-sourced by Databricks, built on top of Parquet files, and provides features such as ACID transactions, version rollback, and Schema evolution.

The Lakehouse architecture was proposed by Databricks in 2020, aiming to combine the flexibility of data lakes with the transactional capabilities of data warehouses.

Lakebase is a new type of database form proposed within the Databricks ecosystem, whose core concept is to build Postgres-compatible transactional database capabilities on top of a data lakehouse architecture.

Apache Iceberg and Apache Hudi, together with Delta Lake, are collectively known as the three major open formats of the lakehouse era.

Lakehouse resolves the contradiction between the lack of ACID transactions in data lakes and vendor lock-in by data warehouse vendors by building a transactional metadata layer (such as Delta Lake, Apache Iceberg, and Apache Hudi) on top of object storage.

Lakehouse is a converged architecture proposed by Databricks that simultaneously supports transactional processing and analytical queries on the same storage layer, with its underlying ACID transaction capabilities implemented via the Delta Lake format.

Delta Lake's time travel feature allows users to query the state of a table at any historical point in time using the VERSION AS OF or TIMESTAMP AS OF syntax.

Databricks' Delta Lake adds a transaction log on top of Parquet files, giving the data lake ACID properties and time travel capabilities.

Delta Lake queries historical snapshots through the transaction log and time travel, providing underlying support for the traceability of models and data.

Structured Streaming supports event-time-based tumbling window aggregation, watermarking to handle late data, and continuously writing aggregation results to Delta tables.

Delta Lake, Iceberg and Hudi are the three mainstream open table format implementations today.

Z-ORDER is a data layout optimization method of OPTIMIZE that clusters data of related columns together at the file level, allowing queries filtering on these columns to skip more files, and is generally recommended for high-frequency filter columns.

Delta Lake is an open-source storage layer built on top of the data lake, providing ACID transactions, schema evolution, time travel and efficient upsert/delete capabilities for Spark and Databricks.

OPTIMIZE is a Delta Lake command used to merge small files (compaction), reducing metadata overhead and improving scan efficiency.

Delta Lake uses a transaction log (_delta_log), Apache Iceberg uses manifest files and a metadata tree, and Apache Hudi uses a timeline (Timeline) as the mechanism for implementing the metadata layer.

Atlassian added transformation logic to its data ingestion pipeline, mapping raw logs into OCSF format before writing them to Delta Lake tables in Databricks.

Traditional data lakes lack transactional guarantees, and a failed write leaves dirty data behind, whereas Delta Lake solves this problem through a log-first, atomic-commit mechanism.

Document Intelligence is not a standalone tool, but is embedded in the workflow of the Databricks data intelligence platform, where parsed document data can directly enter Delta Lake and participate in subsequent SQL analysis, feature engineering or model training.

Delta Lake shares permissions and lineage systems with governance components such as Unity Catalog, allowing document data and business data to flow under the same security and audit framework.