SingleStore Distributed SQL Database

SingleStore Distributed SQL Database

www.singlestore.com

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SingleStore is a distributed SQL database designed for ultra-low latency queries, unifying transactional processing, real-time analytics, and AI workloads in a single engine without the data movement pipelines required by separate OLTP and OLAP systems. Trusted by enterprises including Adobe, Goldman Sachs, Morgan Stanley, NVIDIA, Sony, Hulu, Comcast, FedEx, Cisco, Cigna, Dell, IMAX, Kroger, Palo Alto Networks, SiriusXM, and Siemens, the platform handles petabyte-scale datasets with consistent millisecond query performance under high user concurrency. The Helios cloud platform runs on AWS, Google Cloud, and Azure with elastic auto-scaling that separates compute from storage, allowing independent scaling of read and write workloads. The unified engine processes HTAP transactions, analytical aggregations, full-text search, and vector similarity queries through a single SQL interface, eliminating the need for separate Elasticsearch, Pinecone, or data warehouse deployments. The Aura product line integrates AI capabilities directly into SQL workflows: Aura Analyst accepts natural language questions and generates optimized SQL queries with instant result visualization, AI Functions embed LLM models for sentiment analysis, summarization, and text classification within SELECT statements, and ML Functions provide built-in anomaly detection, classification, and regression model training without external ML platforms. Data ingestion supports Kafka, Kinesis, S3, GCS, Azure Blob Storage, and Fivetran pipelines with sub-second change data capture. Integrations include dbt for transformations, Terraform for infrastructure as code, Django ORM, Hasura GraphQL, AWS Bedrock for LLM access, and standard JDBC and ODBC connectors. The platform holds SOC 2 Type II and ISO 27001 certifications with columnstore compression achieving 10:1 ratios. Heap by Contentsquare reported cost-effective scaling, and Ant Money praised the Postgres-compatible data modeling with big data performance.

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