Decodable is a fully-managed, serverless platform that utilizes Apache Flink and Debezium for real-time ETL/ELT and stream processing, enabling users to efficiently ingest, transform, and deliver high-volume event data. The platform addresses the complexity of managing multiple tools by providing a unified solution that ensures data quality and compliance while minimizing operational overhead.
Funding
$25.5M 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
Real-time data integration often requires stitching together multiple tools, leading to data pipeline sprawl, latency issues, and increased operational overhead. Building and managing stream processing jobs with Apache Flink can be complex, requiring specialized technical expertise.
Solution
Decodable offers a fully managed, serverless platform for real-time ETL/ELT and stream processing, simplifying the ingestion, transformation, and delivery of high-volume event data. Built on Apache Flink and Debezium, the platform unifies data ingestion, processing, and delivery in a single environment. It provides pre-built connectors to capture data from databases, event streams, and APIs, enabling users to process and transform data using SQL or Flink APIs. Decodable eliminates the operational complexity of managing Apache Flink, allowing users to build and deploy Flink jobs more efficiently, ensuring data quality, consistency, and compliance.
Target Audience
The primary audience includes data engineers, data architects, and data scientists who need to build and manage real-time data pipelines for analytics, AI, and application integration.
Features
- Fully managed Apache Flink and Debezium service, eliminating infrastructure overhead.
- Simplified development using SQL, Java, and Python for building real-time pipelines.
- Built-in connectors and CDC (Change Data Capture) for reliable data movement.
- Stream processing capabilities for cleansing, transforming, and ingesting high-volume event data.
- Real-time database replication to data warehouses with minimal impact on production systems.
- Real-time updates to caches and search indexes for improved in-app user experience.
- Continuous data ingestion into vector databases for AI applications.