Google Cloud Dataflow is a fully managed service for executing Apache Beam pipelines within the Google Cloud Platform ecosystem.
Benefits:
- Streaming data analytics with speed: Dataflow enables fast, simplified streaming data pipeline development with lower data latency.
- Simplify operations and management: Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads.
- Reduce total cost of ownership: Resource autoscaling paired with cost-optimized batch processing capabilities means Dataflow offers virtually limitless capacity to manage your seasonal and spiky workloads without overspending.
Features:
- Autoscaling of resources and dynamic work rebalancing: Minimize pipeline latency, maximize resource utilization, and reduce processing cost per data record with data-aware resource autoscaling. Data inputs are partitioned automatically and constantly rebalanced to even out worker resource utilization and reduce the effect of “hot keys” on pipeline performance.
- Flexible scheduling and pricing for batch processing: For processing with flexibility in job scheduling time, such as overnight jobs, flexible resource scheduling (FlexRS) offers a lower price for batch processing. These flexible jobs are placed into a queue with a guarantee that they will be retrieved for execution within a six-hour window.
- Ready-to-use real-time AI patterns: Enabled through ready-to-use patterns, Dataflow’s real-time AI capabilities allow for real-time reactions with near-human intelligence to large torrents of events. Customers can build intelligent solutions ranging from predictive analytics and anomaly detection to real-time personalization and other advanced analytics use cases.
For more info: https://cloud.google.com/dataflow