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Delta Lake 4.0 is documented for Apache Spark 4.0.x. Its major changes include Delta Connect, preview support for catalog-managed tables, and improvements to table features, transaction-log metadata, and file skipping. Delta Kernel is the Java and Rust library layer for building Delta readers and writers without reimplementing protocol details in every connector. The key upgrade decision is not just whether your Spark version matches: check the features enabled on each table and whether every client that uses it can support them.
What Delta Lake 4.0 adds
Delta Lake adds transaction and table-management capabilities to data stored in lake storage systems such as S3, ADLS, GCS, and HDFS. Its documented capabilities include ACID transactions, scalable metadata, batch and streaming processing, schema enforcement, time travel, upserts, and deletes. Version 4.0 builds on that foundation with changes to catalog integration, Spark Connect workflows, connector development, and transaction-log and table features.
New and expanded capabilities
- Delta Connect: Delta-specific operations for Spark Connect’s decoupled client-server model.
- Catalog-managed tables (preview): A foundation for integrating Delta tables with catalogs. Filesystem-managed tables remain supported; the preview does not mean existing tables must be converted.
- Transaction-log improvements: Version-checksum and log-compaction reads and writes.
- Table-feature work: Row tracking, clustered tables, improved handling of table features, and writing support for several advanced features.
- Scan efficiency: Enhanced file statistics to help skip files that do not match a query.
The 4.0 preview overview also highlighted UniForm interoperability and an expanding connector ecosystem using Delta Kernel, including DuckDB, Apache Druid, Apache Flink, and Delta Sharing. Those ecosystem examples do not mean every connector supports every Delta feature; connector capabilities depend on the connector and its version.
Is Delta Lake 4.0 compatible with Spark 4.0?
Yes. The Delta Lake compatibility documentation pairs Delta Lake 4.0.x with Apache Spark 4.0.x. It pairs the listed Delta Lake 3.x lines—3.0.x through 3.3.x—with Spark 3.5.x. Treat these as the documented version pairings, not as a guarantee that any arbitrary combination of patch releases or client libraries will work.
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| Delta Lake line | Documented Apache Spark line |
|---|---|
| 4.0.x | 4.0.x |
| 3.0.x, 3.1.x, 3.2.x, and 3.3.x | 3.5.x |
The Delta Lake 4.0.0 quick start also documents Java 8, 11, or 17 as supported setup choices. Check the setup instructions for the precise Spark or PySpark package and environment you are installing; a matching Spark major version is necessary, but does not by itself establish compatibility for every connector or table feature.
What Delta Kernel is and when to use it
Delta Lake documentation describes it simply: “Delta Kernel is a library for operating on Delta tables.” It is a set of Java and Rust libraries that provides a common abstraction for connector authors to read and write Delta tables without implementing every detail of the Delta protocol themselves.
Use Kernel when building or maintaining an engine connector
Kernel is aimed at developers integrating Delta tables into another execution engine or connector. Its documented use cases include single-process scans, multithreaded scans, connectors for distributed engines, and table inserts. Instead of each engine duplicating protocol logic, a connector can use Kernel and adopt supported behavior as it upgrades Kernel.
This can reduce duplicated implementation work and help different engines behave more consistently, but it is not a promise that all connectors expose identical features or performance. Verify a connector’s own feature support and compatibility before relying on a specific table capability.
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If your workload already uses a supported Delta Lake and Spark combination, Delta Kernel is not a separate Spark upgrade requirement. It is principally a library for connector development. For advanced Delta reads and writes outside Spark, the Delta API documentation marks Delta Standalone as deprecated in favor of Delta Kernel.
How Delta Connect and catalog-managed tables fit
Delta Connect
Spark Connect separates a client application from the Spark server. Delta Connect brings Delta-specific operations into that client-server model, so users can work with Delta capabilities through Spark Connect rather than treating Delta as available only in a locally embedded Spark session. The 4.0 announcement identifies it as a headline feature; the exact operations available depend on the Delta Connect and Spark setup in use.
Catalog-managed tables
Catalog-managed tables are a 4.0 preview foundation for catalog integration. They are distinct from filesystem-managed tables, which remain supported. Since the catalog-managed capability is marked preview, evaluate it as a developing option rather than assuming it has the same operational maturity as established filesystem-managed workflows. The 4.0 announcement does not imply that existing tables are automatically migrated or that catalog management is mandatory.
What to check before upgrading older Delta clients
Delta Lake features are enabled at the table level. A feature being present in Delta Lake 4.0 does not mean it is automatically enabled on every table, or that every older client can use a table after the feature is enabled. The versioning guidance warns that some table features break forward compatibility: workloads referencing an upgraded table may all need to use a compliant Delta Lake version.
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- Confirm the runtime pairing. For a Spark-based deployment, align the Delta Lake and Spark versions with the documented compatibility matrix. Delta Lake 4.0.x is paired with Spark 4.0.x.
- Inventory every reader and writer. Include scheduled jobs, streaming workloads, ad hoc tools, and non-Spark connectors that touch the same tables. A table can be used by clients other than the job performing an upgrade.
- Identify table features in use or planned. Check feature-specific requirements before enabling a feature such as row tracking or clustered tables. Feature enablement and client compatibility are table-specific, not merely cluster-wide settings.
- Upgrade all affected clients before enabling breaking features. If a feature requires compliant clients, move every workload that references the table to a compatible version first; otherwise an older workload may no longer be able to read or write it.
- Validate connector support separately. Kernel-based or other engine connectors can have different feature coverage from Spark. Confirm support for the exact table features your workload depends on.
Delta Lake 4.0 also adds version-checksum and log-compaction support, plus changes to feature handling and file statistics. These improvements do not remove the need to assess table-level compatibility when enabling features that affect older clients.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is there a Delta Lake book or connector guide?
Delta Lake: The Definitive Guide is a technical book whose cited material presents Delta Kernel as a common interface for interoperability across the Delta ecosystem. It may provide useful background, but confirm that the edition and guidance you consult match the Delta Lake version you are deploying; the existence of the book does not establish that its examples cover 4.0.
For connector implementation, the most version-relevant starting point is Delta Kernel’s official documentation and API documentation, alongside the documentation for the target engine. Kernel provides the abstraction; connector-specific support and deployment instructions still come from the connector project.
How to choose between staying on 3.x and moving to 4.0
- Stay on a 3.x line for now if your Spark deployment remains on 3.5.x and you do not need 4.0-specific functionality. The documented 3.x pairings in the compatibility matrix are with Spark 3.5.x.
- Plan a 4.0 move if your Spark environment is moving to 4.0.x or you need 4.0 additions such as Delta Connect or the new log and table-feature work. Plan the Spark and Delta client upgrade together.
- Evaluate catalog-managed tables separately if catalog integration is your reason to move. That capability is a preview, while filesystem-managed tables remain an option.
- Consider Delta Kernel when developing an engine connector or needing advanced Delta reads and writes outside the deprecated Standalone path—not as a generic requirement for every Delta user.
The Delta Lake project announced the final 4.0.0 release in 2025. The project reported that more than 70 individuals contributed to that community release.
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