Decodable offers a fully managed, SQL‑native streaming platform that enables data teams to create continuous data pipelines using ANSI‑SQL, while the service automatically handles scaling, state management, and fault tolerance. The platform provides a wide connector catalog for ingesting from event hubs and exporting to data warehouses, APIs, and analytics tools, and includes built‑in observability, lineage, and enterprise‑grade security features.
Funding
$20M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Enterprises that need to extract actionable insights from high‑velocity event streams often must provision and operate complex streaming stacks (e.g., Kafka, Flink) and write custom code in specialized languages. This operational burden creates latency, requires deep engineering expertise, and hampers the ability to iterate on real‑time analytics quickly.
Solution
Decodable delivers a fully managed, SQL‑native streaming platform that lets data teams define continuous data transformations using familiar ANSI‑SQL statements. The service abstracts the underlying streaming infrastructure, automatically handling scaling, state management, and fault tolerance so pipelines run with sub‑second latency without manual cluster administration. Pipelines are version‑controlled and can be deployed through standard CI/CD workflows, enabling rapid iteration and rollback. A library of pre‑built connectors streams data from cloud event hubs, message queues, and object storage, while outputs integrate directly with data warehouses, BI tools, and micro‑services. Real‑time observability—including metrics, lineage graphs, and alerting—is available in a unified web console. Security is enforced via role‑based access control, end‑to‑end encryption, and compliance certifications, allowing organizations to meet governance requirements while focusing on business logic.
Target Audience
Primary users are data engineering, analytics, and product operations teams at mid‑size to large enterprises that require low‑latency, continuous data processing without managing streaming infrastructure.
Features
- SQL‑based pipeline authoring with full ANSI‑SQL support for joins, windowed aggregations, and user‑defined functions
- Serverless execution engine that auto‑scales compute and manages state, eliminating cluster provisioning
- Built‑in fault tolerance and exactly‑once processing guarantees across all connectors
- Extensive connector catalog for ingesting from Kafka, Kinesis, Pub/Sub, Azure Event Hubs, and cloud storage services
- Seamless output integrations with Snowflake, BigQuery, Redshift, Elasticsearch, and real‑time APIs
- Integrated observability suite offering latency dashboards, data lineage, and automated anomaly alerts
- Enterprise‑grade security: RBAC, TLS encryption in transit and at rest, and SOC‑2 / ISO‑27001 compliance