LakeSail

No JVM. 10x faster. 98% cost reduction.

Sail 0.7.3 is out.

This release expands what you can do with Delta Lake and Apache Iceberg in Sail. We’ve added OPTIMIZE, VACUUM, and table history inspection. Iceberg gains SQL support for branches and tags, metadata tables, and procedures for compaction and snapshot management.

There are also new SQL functions for vectors and input file metadata access.

We also fixed many query planning issues found via differential testing against JVM Spark. Time zone handling, type coercion, scalar subqueries, set operations, aggregates, joins, and sorts now behave correctly in many complex edge cases.

Thanks to everyone who contributed, including the eight first-time contributors in this release.

Release notes: github.com/lakehq/sail/re…

pip install pysail

LakeSail and Microsoft OneLake.

Microsoft just shared a closer look at the integration, including how teams can work with OneLake data through familiar Spark SQL and DataFrame interfaces.

community.fabric.microsoft.com/blog/fbc_fabri…

LakeSail announced a strategic partnership with Oxide Computer Company to help enterprises modernize data and AI infrastructure while maintaining control over security, performance, and cost.

The partnership follows the successful validation of Sail on Oxide’s platform. Sail, LakeSail’s open-source, Rust-native, Spark-compatible compute engine, ran on Oxide’s integrated, rack-scale cloud platform, establishing a validated foundation for secure, customer-controlled AI environments.

Oxide delivers a cloud-like experience within a customer’s data center through an integrated platform and unified control plane. Its architecture includes a hardware root of trust and mechanisms for verifying the integrity of the software stack.

Sail preserves Spark APIs, SQL, and DataFrames while replacing the traditional JVM execution layer with a Rust-native architecture. It supports Python, Apache Iceberg, and Delta Lake, allowing enterprises to modernize without rewriting existing applications.

By eliminating the JVM execution layer, Sail reduces costs and performance bottlenecks associated with cluster startup, garbage collection, memory tuning, idle capacity, and overprovisioning. Depending on workload and configuration, LakeSail has demonstrated performance improvements of up to 10x and infrastructure-cost reductions of up to 98% compared with equivalent Spark and JVM-based deployments.

Enterprises can use Sail as open-source software or purchase the commercial LakeSail Platform for management, governance, security, and support capabilities.

“We believe data processing and AI belong in the same engine rather than in separate systems customers must stitch together,” said Shehab Amin, Co-Founder and CEO of LakeSail. “Combining Sail’s efficiency with Oxide’s customer-owned cloud infrastructure gives enterprises more freedom to build without inheriting more complexity or traditional JVM economics.”

“As data management and processing become more central to AI, we're excited to see LakeSail running on Oxide," said Steve Tuck, Co-Founder and CEO of Oxide Computer Company. "Running LakeSail's modern execution engine on Oxide's on-prem Cloud Computer allows customers to gain insights from their data faster while maintaining the control over where that data lives."

Read the full press release: lakesail.com/blog/oxide-par…