November 2025 Summaries

Onehouse has introduced the Quanton engine, which significantly enhances Apache Iceberg workloads on Apache Spark by offering up to three times better performance on industry-standard benchmarks without altering existing Spark jobs or SQL. Quanton's unique approach combines faster runtime and smarter data processing, outperforming industry leaders like Databricks Photon in terms of price-performance without additional cluster costs. Designed to maintain compatibility with existing workflows and open table formats, Quanton optimizes execution runtime and storage engine layers to address inefficiencies in traditional Spark environments. It accelerates data operations through techniques such as SIMD vectorized execution and storage-aware optimizations, offering substantial improvements in ETL tasks, including join, filter, and update operations. The engine achieves better performance for mixed workloads by integrating seamlessly with Iceberg's metadata and employing advanced techniques like asynchronous indexing and compaction. Onehouse provides tools like the Cost Analyzer for Apache Spark to help users assess potential savings and performance gains with Quanton, which is fully compatible with existing Spark infrastructures.

Data lakehouses are becoming essential for modern analytics by combining the scalability of data lakes with the performance of data warehouses, but they come with significant security challenges. These challenges include managing structured and unstructured data securely, preventing unauthorized access, and ensuring compliance with regulations like GDPR, HIPAA, and CCPA. Key security measures include implementing strong encryption strategies, using key management systems such as AWS KMS, Azure Key Vault, and Google Cloud KMS, and enforcing robust access controls through role-based and attribute-based methods. Compliance is critical, requiring capabilities for precise data deletion and audit logging, which are supported by modern table formats like Apache Hudi, Iceberg, and Delta Lake. To enhance observability and compliance, tools such as Onehouse LakeView, AWS CloudTrail, and data cataloging solutions like Apache Atlas are used. Data sovereignty is also a primary concern, necessitating a platform that respects data residency laws, with Onehouse offering a privacy-first architecture that ensures all data processing occurs within a user’s virtual private cloud. By integrating these security and compliance practices, organizations can secure their data lakehouses, protect sensitive information, and meet global regulatory requirements efficiently.