Data Lakehouse Execution Engine

4 posts · Apr 2026 – Sep 2026

What this trail covers

SingleStore's Universal Storage architecture unifies rowstore and columnstore, enabling independent scaling of compute clusters. This post clarifies that while lakehouses provide flexible and cost-effective data storage in open formats, a robust execution engine is crucial for interactive workloads, real-time analytics, and AI systems. The engine, not just the storage, defines the system's performance, concurrency, and latency characteristics. SingleStore's engine is designed to handle ingestion, queries, and updates with low and predictable latency, supporting transactional and analytical workloads within the same system, moving beyond a simple data repository to support applications, real-time analytics, and AI systems.

All SingleStore trails

Sep 2026Apparently, Nothing Happened in Database History Between 1986 and MondayThis post elaborates on SingleStore's Universal Storage architecture, detailing how it directly addresses the demands of AI agents for real-time operational data and historical context. It explains the technical implementation of Universal Storage, which allows for seekable columnstore tables with hash indexes, subsegment access, and row-level locking, enabling efficient selective reads and fine-grained updates. The post highlights the ability of a single engine to handle transactions, analytics, and vector search simultaneously, eliminating the latency associated with data conversion and separate systems. It further details the benefits of the zero-copy data fabric and Smart Attach for scaling analytical and agent workloads independently via read-only compute clusters without full data duplication. The post also emphasizes the integration with open Apache Iceberg data, allowing unified access to governed lakehouse data alongside current operational data.
Aug 2026Enterprise AI Will Be a Network, Not a Monolith`This post argues for a shift from monolithic enterprise AI architectures to a network of specialized AI models (agent swarms). It highlights the critical role of a high-concurrency database in supporting these agent swarms, which generate enormous concurrency for reads and writes against continuously changing enterprise data. The post emphasizes that the database becomes part of the control plane, needing to sustain massive concurrency while keeping reads and writes synchronized at operational speed. It positions SingleStore's distributed SQL database, designed for high-concurrency transactional and analytical workloads, as a foundational component for this composable AI architecture. The post also reinforces the importance of open data formats like Apache Iceberg for data freedom within this evolving AI landscape.
Aug 2026Why ad models need fresh context at decision timeThis post highlights the critical need for data freshness at decision time for AI models, particularly in ad tech. It explains how stale inputs lead to mispricing and missed opportunities, and how traditional offline metrics fail to capture this "freshness gap." The post argues against bolting on specialized systems like feature stores and vector databases, advocating instead for a unified engine that serves vectors alongside SQL filters on real-time data. It emphasizes that the lakehouse is for training, while the core engine serves real-time decisions, consolidating feature serving, vector retrieval, and live state within a single system to address the architectural gaps previously patched in the series.
Aug 2026Advertiser dashboards at thousands of concurrent usersThis post details the architectural challenges of serving real-time analytics dashboards to thousands of concurrent users in AdTech, contrasting the limitations of traditional data warehouses with the benefits of a unified serving layer like SingleStore. It highlights the problem of "Monday morning concurrency spikes" and the data staleness and product roadmap costs associated with caching and pre-aggregation mitigation strategies. The post argues for augmenting warehouses with a real-time analytics engine that can handle high concurrency and low latency for customer-facing tasks, while retaining the warehouse for deep historical exploration. It also touches on the increasing demand from AI models and agents for low-latency data access.
Jul 2026Iceberg Needs an Open Catalog, Not a Walled GardenThis post argues that for open table formats like Iceberg to deliver on their promise of customer leverage, the associated catalog must also be open and not a walled garden. It draws a parallel to ERP suites, warning that a proprietary catalog can lead to vendor lock-in, similar to how older systems trapped data. The author emphasizes that the catalog defines the working contract around Iceberg tables, impacting permissions, trusted versions, schema evolution, policy application, and engine coordination. Real-time AI and operational applications, which require fresh data, low latency, high concurrency, and transactional updates, raise the stakes, making an open catalog essential for building these applications and adopting new engines without integration headaches. The post concludes that the next lakehouse battle will be over control, and preserving customer freedom after Iceberg adoption is key.
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Jun 2026One Copy, One Engine, No SeamsThis post critiques Databricks' LTAP approach, arguing that shared storage with multiple engines creates seams and latency, contrasting it with SingleStore's unified engine and Universal Storage. It emphasizes that SingleStore's single format provides immediate queryability after writes, supports multi-model data natively within one engine, and efficiently handles in-place updates and deletes, which are crucial for operational workloads and agentic systems. The post highlights the importance of a unified engine for real-time data access for agents and challenges the notion that HTAP failed, positioning SingleStore's architecture as the true solution.
Jun 2026SingleStore and HPE Enable Enterprises to Scale the Next Generation of AI ApplicationsThis 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.
Jun 2026The Hi-Tech Data Convergence Migration Playbook | SingleStoreThis post details a practical migration playbook for consolidating fragmented data stacks into a unified platform, specifically addressing the challenges of hi-tech workloads and agentic AI. It introduces diagnostic questions to assess readiness for migration, highlighting issues like multi-system query touches, data disagreement between operational and analytical views, scalability for AI traffic, and GPU compute proximity to data. It provides field examples from customers (internet connection diagnostics, Epigen for real-time vector search, Heap for full-fidelity analytics) and outlines a stage-gated migration strategy: landscape assessment, identifying high-value hot path use cases, replacing systems incrementally, retaining the warehouse, and avoiding new silos. The core message is to collapse the hot path by integrating continuous ingestion, multi-pattern queries, high concurrency, multimodal data, and compute-close-to-data capabilities.
Apr 2026The Lake Is Not the Database. The Engine Is.This post emphasizes that the execution engine, not just the storage layer (like object storage in a lakehouse), is the defining characteristic of a database system. It highlights the trade-offs of a storage-first model, where frequent writes lead to fragmentation and performance degradation, necessitating background optimization and caching layers. The post argues that a lakehouse requires a robust engine designed for interactive workloads to achieve low and predictable latency, handle concurrency, and support transactional and analytical processing simultaneously, enabling it to function as more than just a data repository.