Real-time Data Ingestion and Querying

8/3/2026

This post introduces the concept of continuous identity resolution for AdTech platforms, contrasting it with traditional nightly batch processing. It highlights the engineering challenges and business impacts of inter-rebuild latency, including inaccurate attribution, delayed frequency capping, and GDPR compliance issues. The post proposes an architectural shift to a distributed SQL engine like SingleStore, capable of handling transactional updates and analytical joins, to enable incremental resolution of identity graphs. This allows for real-time updates to the graph, resolving incoming signals within seconds and providing a defensible audit trail for compliance.

7/22/2026

This post argues for the importance of an open catalog for Apache Iceberg to prevent vendor lock-in, drawing parallels to past ERP system consolidation. It highlights that the catalog, not just the storage format or compute engine, becomes the critical control layer. The post emphasizes that for real-time AI and operational applications, an open catalog is essential for fresh data, low-latency access, and transactional updates, ensuring customers retain freedom and avoid proprietary control layers.

7/20/2026

This post identifies the problem of significant latency in AdTech data pipelines, where data freshness is critical due to rapid value decay. It explains how cumulative latency across multiple stages, even with healthy individual components, leads to delayed insights and impacts critical functions like budget pacing and launch verification. The post proposes an architectural shift towards low-latency engines that make events queryable immediately upon arrival, contrasting this with traditional batch processing and lakehouse architectures for archival and model training.

7/2/2026

Introduced third generation cluster architecture with a zero-copy data fabric, enabling direct compute on Apache Iceberg tables without data duplication. This allows for read, write, and reasoning operations on open data formats, supporting agentic applications. New features include Smart Read Replicas for independent scaling of read workloads, Database Branching for creating isolated database environments without data duplication, and Smart DR for optimized disaster recovery using the zero-copy fabric.

6/22/2026

This post argues for the critical need of real-time data in enterprise AI, emphasizing that AI's effectiveness hinges on Information, Context, and Action, all of which must be current. It highlights that stale or fragmented data leads to compromised intelligence and poor decision-making, especially as AI systems become more agentic and take actions. SingleStore is positioned as the core data engine that provides the necessary live, relevant, and high-performance context for AI systems operating within the business's real-time environment.

6/19/2026

This post critically analyzes the competitor's approach to unifying operational and analytical data, arguing that separate engines bolted onto shared storage create seams and introduce latency, especially for agentic workloads that require immediate consistency between writes and reads. It highlights SingleStore's 'Universal Storage' as a unified engine with a single data format (in-memory rowstore over columnstore) that provides immediate queryability for writes, handles updates and deletes efficiently, and supports multi-model data (relational, JSON, vectors) within a single engine. The post questions the competitor's 'LTAP' approach regarding its ability to handle updates and deletes with low latency and its readiness for write workloads.

6/17/2026

This post elaborates on the real-time data ingestion and querying capability by directly addressing and differentiating SingleStore's architecture from Databricks' LTAP and Lakehouse//RT announcements. It critiques the two-engine approach of LTAP, emphasizing SingleStore's single, unified distributed SQL engine for both transactional and analytical workloads. The post highlights the challenges of horizontally scaling Postgres-compatible instances for high-concurrency operational loads and contrasts this with SingleStore's inherent distributed design. It also analyzes and questions the validity of benchmarks presented for LTAP solutions, particularly concerning their ability to handle complex, concurrent workloads involving joins and writes. The core technical contribution is the reinforcement and detailed explanation of SingleStore's advantage in providing a single, horizontally scalable engine for real-time operational and analytical data needs, particularly in the context of agent-driven applications.

6/4/2026

This post details a migration playbook for consolidating fragmented data stacks into a converged architecture, focusing on practical field examples and a stage-gated migration approach. It highlights diagnostic questions to identify readiness for change, provides case studies (internet connection diagnostics, Epigen for real-time vector search, Heap for full-fidelity analytics), and outlines a five-step migration process: landscape assessment, identifying hot path use cases, replacing systems one at a time, keeping the warehouse, and avoiding new silos. The core technical argument is collapsing the 'hot path' by integrating continuous ingestion, multi-pattern queries, and compute close to data to handle demanding AI and real-time workloads, rather than adding more systems.

6/3/2026

This post details how SingleStore addresses the scaling limitations of PostgreSQL for fast-growing fintechs, particularly those reaching the $100M ARR mark. It highlights the core technical constraint of PostgreSQL being a single-writer system and the fragility introduced by 'bolt-on' specialized tools for analytics, time-series, and vector indexing. The post positions SingleStore as an HTAP system that natively combines rowstore and columnstore for transactional and analytical workloads on the same data, eliminating ETL and synchronization layers. It emphasizes SingleStore's capabilities in distributed writes, horizontal scaling, native vector and full-text search, compliance, multi-tenancy, and predictable operational overhead. It also outlines a service-by-service migration strategy from PostgreSQL, leveraging tools like Pipelines and SingleStore Flow.

6/1/2026

This post details the disaster recovery mechanisms for SingleStore Helios, including RPO/RTO definitions, failure scenarios (node, AZ, region, accidental deletion), and the technical implementation of Smart DR and backup/restore options. It elaborates on the replication of not just data but also workspace topology, user accounts, permissions, pipelines, firewall settings, and configuration metadata for regional failover. It also specifies the RPO for the Control Plane's backing database and compute.

5/27/2026

This post details a real-time data convergence architecture for agentic AI, defining it as a platform where ingestion, operational analytics, real-time analytics, and AI retrieval operate on the same live data. It highlights the challenges AI introduces to traditional architectures due to increased query volume and the need for low-latency, accurate data. The post outlines five key building blocks for this architecture: continuous ingestion with no staging delays, multi-pattern query support on a single engine, concurrency designed for machines, multimodal data without cross-system joins, and compute close to data. It then positions SingleStore within this architecture, emphasizing its HTAP engine, Universal Storage, hybrid search capabilities, and Aura's GPU-aware compute services (Aura Analyst, GPU-powered notebooks, model hosting, Cloud Functions, Python UDFs) as solutions that directly address these building blocks. The post clarifies that SingleStore acts as the convergence layer above the data warehouse, removing the warehouse from the hot path to reduce latency, drift, and cost.

5/21/2026

This post emphasizes the critical role of real-time data processing in EdTech platforms, specifically for enabling adaptive learning, AI tutors, and proactive student intervention. It provides concrete examples of how SingleStore's capabilities facilitate these use cases, citing Curriculum Associates' implementation for near-instant teacher feedback (sub-200ms) and GoGuardian's real-time analytics for 18 million students. The post argues that batch-era data pipelines are insufficient for modern AI-driven educational experiences and highlights the need for systems that can process and act on data within the same session or immediately after.

5/20/2026

This post details how SingleStore's architecture addresses the challenges of real-time pre-trade risk and intraday margin analytics in financial services. It highlights the need for high-throughput ingestion of streaming market data and low-latency, high-concurrency analytical queries, contrasting SingleStore's compute-optimized approach with storage-optimized platforms like Snowflake and Databricks. The post also discusses the limitations of legacy systems (mainframes, Vertica, Oracle, DB2) and specialized technologies (KDB) for these workloads, advocating for an augmentation approach where SingleStore is integrated to serve the real-time query layer without replacing existing infrastructure.

5/14/2026

This post details the impact of a physical AWS data center thermal event on critical services like Coinbase, FanDuel, and CME Group, emphasizing that multi-AZ high availability is insufficient for regional outages. It reinforces the necessity of cross-region disaster recovery and positions SingleStore Smart DR as a solution that provides continuous asynchronous replication with an RPO of up to 10 minutes, no idle compute cost by default, full topology replication, and simplified failover/failback, contrasting it with traditional active-passive DR and highlighting its importance for mission-critical workloads.

5/6/2026

This post identifies a critical infrastructure problem stalling AI adoption in the energy sector, stemming from two layers: physical data center power limitations and, more significantly, energy data architectures not supporting real-time, cross-system coordination. It argues that existing architectures, designed for human-paced decisions and batch processing, introduce unacceptable latency and inconsistency for AI-driven operations. The post advocates for a shift towards operational data systems, specifically highlighting HTAP architectures like SingleStore, which ingest, store, process, and serve data within a single operational loop, enabling immediate queryability, ACID guarantees across mixed workloads, and predictable performance under high concurrency. It provides examples of performance improvements in data ingestion time and query latency achieved with SingleStore.

4/23/2026

This post argues for the necessity of real-time data ingestion and querying in healthcare analytics, highlighting the limitations of legacy systems and batch processing. It contrasts the typical millisecond-level real-time processing in other industries with the life-or-death stakes in healthcare, where real-time alerts can prevent critical patient conditions. The author advocates for a unified healthcare data management platform that can handle structured, unstructured, and semi-structured health data, ingest data in real-time, and support streaming and operational analytics on the same system to enable analytics at the point of care.