real-time Kinesis to Snowflake in minutes
Amazon Kinesis is a managed service offered by Amazon that aims to make it easy to collect, process, and analyze streaming data.
Kinesis is a popular choice for streaming backbones because it’s in the AWS ecostystem and relatively simple to set up compared to other data streaming options.
Snowflake is a cloud-based data warehouse that offers highly scalable and distributed SQL querying over large datasets. Using OLAP (Online Analytical Processing), Snowflake offers the ability to rapidly answer multi-dimensional analytic database queries with potentially large reporting views by breaking each query up between many worker nodes and reassembling the finalized answer.
Estuary builds free, open-source connectors to extract data from Kinesis with millisecond latency, allowing you to replicate that data to various systems for both analytical and operational purposes. Kinesis data exists in a stream and often benefits from being organized into a data lake or warehosue for analytics with history.
Data can then be directed to Snowflake using materializations that are also open-source. Connectors have the ability to keep warehouses as up-to-date as the warehouse can handle, without incurring costs. This allows Snowflake to receive data with 30 seconds to a minute of latency.
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Estuary helps move data from
Kinesis to Snowflake in minutes with millisecond latency.
Estuary helps move data fromKinesis to Snowflake in minutes with millisecond latency.
Estuary enables the first fully managed ELT service that combines both millisecond-latency and point-and-click simplicity. Flow empowers customers to analyze and act on both historical and real-time data across their analytics and operational systems for a truly unified and up-to-date view.
Flow is developed in the open and utilizes open source connectors that are compatible with a community standard. By making connectors interchangeable with other systems, the Estuary team hopes to expand the ecosystem for everyone’s benefit, empowering organizations of all sizes to build frictionless data pipelines, regardless of their existing data stack.