Welcome to Decodable Decodable is a real-time data processing platform powered by Apache Flink and Debezium. In this context, real time means sub-second end-to-end latency. It provides automated provisioning and management of compute resources and delivers consistent, reliable, and at-least-once or exactly-once processing of your data. Decodable is a cloud service, meaning there are no nodes, clusters, or services for you to manage and Decodable runs using the same cloud providers and regions as you in order to keep costs down, reduce latency, and keep traffic private. Using Decodable, you can stream the data that you want, in the formats that you need, and send it to wherever it needs to go. This happens all with real-time speed, and you can transform or enrich that streaming data using SQL or a JVM-based programming language of your choosing. Decodable features Decodable provides many features that help you use the power of Apache Flink’s data processing capabilities. You’ll be able to deploy a pipeline in minutes, without having to deal with complex infrastructure. This section outlines just some of the things that you can do with Decodable. Extract, transform, and load data from databases into data warehouses like Snowflake Perform extract, transform, and load (ETL) operations from databases into Snowflake, without needing to spin-up extra infrastructure like Amazon S3. See Snowflake sink connector for more information about creating connections to Snowflake. Ingest clickstream, orders, or other application events into your data warehouse, data lake, or OLAP databases. Quickly get time series and event-oriented data into your data warehouse for analytics. Replicate data from a database to your data warehouse or data lake using Debezium-powered Change Data Capture Use Decodable Change Data Capture (CDC) Connectors to easily convert and ingest table changes into a stream of CDC records. Decodable’s CDC Connectors uses Debezium to capture data changes made upstream. You can replicate the contents of a database like MySQL so they’re available for analytics in your data warehouse or data lake. Transform streaming data with pipelines You can create SQL-based pipelines that parse, normalize, aggregate, sanitize, or filter data before that data is sent to a downstream system. For example, you can cleanse data of personally identifiable information (PII) and personal health information (PHI) before it leaves or enters a region.