STRIIM FOR GOOGLE BIGQUERY

Cloud-Based, Real-Time Data Integration

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Real-time data pipelines to Google BigQuery

Striim enables real-time data integration to Google BigQuery to allow 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

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 low-impact 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.

CONTINUOUS, REAL-TIME
CONTINUOUS, REAL-TIME
Data flows continuously and in the right format into Google BigQuery from cloud and on-premises systems
BROAD RANGE OF DATA SOURCES
BROAD RANGE OF DATA SOURCES
Ingest data from databases, data warehouses, log files, sensors, messaging, Hadoop, and NoSQL
EASY TO USE
EASY TO USE
Easily build and modify streaming data pipelines using a wizards-based drag-and-drop UI and a SQL-based language
Use Case: Cloud-Native Data Warehousing

REAL-TIME OPERATIONAL DATA STORE AND REAL-TIME DATA WAREHOUSING

  • Continuously move data from databases, data warehouses, and AWS to Google BigQuery for analytical workloads and Cloud Spanner for operational data store
  • 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 avoiding batch inefficiencies
  • Continuously monitor your data pipelines with real-time alerts

 

 

 

 

Striim for Google BigQuery


Real-Time Data Integration from Heterogeneous Data Sources, On-Premises or in the Cloud


In-Flight Filtering, Masking, Transformation, and Enrichment Before Delivery


SQL-based Language, and Drag-and-Drop UI, to Easily Build and Modify Data Pipelines