Google Cloud Data flow is well-known for its unified stream and batch processing, which comes with serverless, fast and cost-effective features. This service provides fully managed data processing services that provide automated provisioning and management for processing resources. There is horizontal autoscaling for worker resources to maximize resource utilization.
Google Cloud Data Flow also offers OSS community-driven innovation using Apache Beam SDK. This allows for consistent, exact-once processing and reliable data.
We will cover the features and how you can get started with Data Flow services in this blog to help you understand how they work. Let’s get started with the features!
What are the Google Cloud Data Flow features and what do they mean?
Data Flow services include in-built features that make them more efficient and advanced. The features include:
1. Dynamic work rebalancing and autoscaling of resource resources
Data Flow services reduce pipeline latency, maximize resource utilization, and lower processing costs per data record by using data-aware resource scaling. The data inputs are automatically partitioned and continuously rebalanced to balance worker resource utilization and reduce the impact of “hotkeysā€¯ on pipeline performance.
2. Flexible pricing and scheduling for batch processing
Flexible resource scheduling (FlexRS), offered by Google Data Flow, allows for batch processing at a lower cost and flexible job scheduling. These jobs can be placed in a queue and are available for execution within six hours.
3. Real-time AI patterns
Dataflow’s real time AI capabilities allow for real-time responses with near-human intelligence to large events. Customers can use this to create intelligent solutions that range from predictive analytics and anomaly detection, to real-time personalization, and other advanced analytics.
4. Right fitting
Right fitting creates stage-specific pools of resources that can be optimized to reduce resource wastage at each stage.
5. Streaming Engine
Streaming Engine is used to separate compute from state storage. It travels pipeline execution out of worker VMs to the Dataflow service backend, improving data latency and autoscaling.
6. Horizontal autoscaling
Horizontal autoscaling allows the Dataflow service, to automatically select the appropriate number of worker instances for running a job. The Dataflow service dynamically allocates more or less workers during runtime to take into account the job characteristics.
7. Vertical autoscaling
Vertical autoscaling can be used in conjunction with horizontal autoscaling to scale workers to meet the needs of the pipeline.
8. Dataflow Shuffle
The Service-based Dataflow Shuffle shifts shuffle operations, which are used to group and join data, from worker VMs into the Dataflow service backend for batch pipelines. Batch pipelines can scale easily to hundreds of terabytes without any tuning.
9. Dataflow SQL
Dataflow SQL allows you to use SQL skills to build streaming Dataflow pipelines using the BigQuery web interface. You can access:
First, join streaming data from Pub/Sub to files in Cloud Storage or tables on BigQuery
Secondly, you can write your results into BigQuery
Finally, you can create real-time dashboards with Google Sheets and other BI tools.
10. Dataflow templates
Dataflow templates can be used to share pipelines with colleagues and across the organization. They can also use many Google-provided templates to implement useful data processing tasks. This includes Change Data Capture templates that can be used for streaming analytics. Flex Templates allow you to create a template from any Dataflow pipeline.
11. Monitoring inline
Dataflow inline monitoring is a tool that allows you to troubleshoot streaming and batch pipelines.