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Data Lakehouse Execution Engine

Data Lakehouse Execution Engine

2
posts
2026

This post introduces the concept of a dedicated execution engine as a critical component for realizing the full potential of data lakehouses. It argues that while object storage and open table formats provide a foundation for data centralization and accessibility, the performance, concurrency, and transactional capabilities required for operational workloads and real-time analytics are determined by the execution layer. The post highlights the limitations of storage-first models in handling frequent updates and complex queries. SingleStore and HPE Alletra Storage MP X10000 enable enterprises to scale AI and analytics applications outside traditional cloud-only environments by providing a high-performance, S3-compatible object storage platform that serves as the storage layer for SingleStore's disaggregated compute and storage architecture. This allows for running modern HTAP workloads while keeping data in self-managed environments, offering scalable storage for growing datasets, predictable infrastructure costs, and greater control over data location. Performance testing showed SingleStore on HPE Alletra Storage MP X10000 performing comparably to public cloud deployments for OLTP workloads and outperforming them by over 30% for analytical workloads on a 10 TB dataset.

2026

SingleStore and HPE Enable Enterprises to Scale the Next Generation of AI Applications

6/9/2026

This post details the integration of SingleStore with HPE Alletra Storage MP X10000 to enable scalable AI and analytics applications on self-managed infrastructure. It highlights the architectural separation of compute and storage in SingleStore, leveraging HPE Alletra as an S3-compatible object storage layer. Performance benchmarks for OLTP (TPC-C) and analytical (TPC-H) workloads are presented, demonstrating competitive and superior performance compared to public cloud deployments, respectively. The post emphasizes the value proposition of storing more data without scaling compute, running HTAP workloads, and avoiding data movement for AI initiatives.

The Lake Is Not the Database. The Engine Is.

4/26/2026

The post argues that the effectiveness of a lakehouse architecture is critically dependent on its execution engine, not just the underlying object storage. It contrasts the characteristics of object storage (durability, scalability, cost-efficiency) with the requirements of a database (query speed, concurrency, transactional integrity, predictable latency). It details how storage-first models break down under frequent writes and high concurrency due to file fragmentation and metadata overhead. The post advocates for an execution engine designed for interactive workloads that operates directly on data, handles ingestion, queries, and updates efficiently, and supports both transactional and analytical workloads within a unified system, thereby enabling lakehouses to support applications and real-time analytics without compensatory layers.