Google Cloud Dataflow

What is Google Cloud Dataflow?

Google Cloud Dataflow is a fully managed, serverless Google Cloud service for unified stream and batch data processing based on Apache Beam. It autoscales workers to reduce latency, processing time and cost, and is used to power real-time analytics and data pipelines.

Key features of Google Cloud Dataflow

• Unified stream and batch processing with Apache Beam
• Fully managed, serverless operation with autoscaling
• Real-time data processing to reduce latency
• Pipelines written with Beam SDKs
• Integration with other Google Cloud data services
• Monitoring of jobs through the Google Cloud console

Who uses Google Cloud Dataflow?

Dataflow is used by data engineers and analytics teams building streaming and batch pipelines on Google Cloud, such as event ingestion, ETL and real-time analytics for data warehouses and machine learning.

Google Cloud Dataflow pricing

Dataflow uses pay-as-you-go pricing, charged for the compute, memory and data processing resources a job uses. Google lists rates on its pricing page, and new customers receive $300 in free credits to spend on Dataflow, according to the product page.

Google Cloud Dataflow alternatives

Alternatives to Google Cloud Dataflow include Apache Flink, Apache Spark on Databricks, AWS Glue, Azure Stream Analytics and Confluent. Flink and Spark are open source engines, while the cloud services offer managed equivalents on other providers.