Continuous Real-Time Data Integration to Google BigQuery

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

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

Data flows continuously and in the right format into Google BigQuery from cloud and on-premises systems

BROAD RANGE OF DATA SOURCES

Ingest data from databases, data warehouses, log files, sensors, messaging, Hadoop, and NoSQL

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

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

STRIIM FOR GOOGLE BIGQUERY

Cloud-Based, Real-Time Data Integration

Real-time data pipelines to Google BigQuery

WHY STRIIM FOR GOOGLE BIGQUERY

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

See how streaming data integration can work for you.