Cloud-based, Real-Time Data Integration

Striim for Google BigQuery

Unify your data in Google Cloud with a full suite of real-time data integration solutions. Whether it's automated database migrations to Google Cloud or data integration for BigQuery, Striim will help you get there faster.

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Real-time Data Pipelines to Google BigQuery

Striim enables real-time data integration to Google BigQuery for continuous access to timely, pre-processed data from on-premises or cloud-based data sources. Striim ingests, processes, and delivers real-time data from a wide variety of on-premise sources, including relational databases, data warehouses, log files, messaging systems, Hadoop and NoSQL solutions.

With Striim, BigQuery customers can easily use their modern data warehouse for operational decision making and gain more value. 

Why Striim for Google BigQuery

Available also as a PaaS solution, Striim offers a secure, reliable, and scalable service for real-time ingestion, preparation, and delivery of structured, semi-structured, and unstructured data into Google BigQuery. With change data capture capabilities, it minimizes any impact on source database systems.

Unlike traditional ETL, Striim transforms in-flight data, reducing latency and supporting operational workloads. Striim uses SQL-based continuous queries and UI-based operators to filter, aggregate, transform, mask, and enrich streams of real-time data in-memory before delivering to Google BigQuery.

With wizards-based, drag-and-drop UI, Striim simplifies setting up continuous data flows into Google BigQuery.

Real-time Operational Data Store and Data Warehousing

  • Continuously move data from databases, data warehouses, and AWS to Google BigQuery for analytical workloads and Cloud Spanner for operational purposes
  • Perform in-line denormalizations and transformations to maintain low latency and reduce ETL workload
  • Integrate with Cloud Dataflow for long-running transformations
  • Run reports on transactional data without any impact on source databases
  • Deliver partial data sets for reporting without the inefficiencies of batch processing
  • Continuously monitor your data pipelines with real-time alerts

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