real-time Kafka to Bigquery in minutes
Apache Kafka is a popular open-sourced event-streaming platform.
It’s focused on allowing enterprises to use real-time data as the backbone of their operations. Kafka provides an event-based backbone for many Fortune 500 companies.
Google Bigquery is a cloud-based data warehouse that offers highly scalable and distributed SQL querying over large datasets. Using OLAP (Online Analytical Processing), Bigquery offers the ability to rapidly answer multi-dimensional analytic database queries with potentially large reporting views by breaking a query up between many worker nodes and reassembling the finalized answer.
Estuary builds free, open-source connectors to extract data from Kafka in real-time, allowing you to offload data to various systems for both analytical and operational purposes. Kafka data exists in a stream and often benefits from being organized into a data lake or placed into a warehouse for analysis with history.
Data can then be directed to Bigquery 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 allowing Bigquery to receive data with under 10-second latency.
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Contact UsEstuary helps move data from
Kafka to Bigquery in minutes with millisecond latency.
Estuary helps move data fromKafka to Bigquery in minutes with millisecond latency.
About Estuary
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.