Google Cloud Dataflow
About Google Cloud Dataflow
Google Cloud Dataflow is a fully managed platform for batch and streaming data processing. It scales to 4K workers per job and processes petabytes of data.
Products
- Dataflow ML: Simplifies deployment and management of complete ML pipelines.
- Dataflow GPU: Enhances MLOps and ML job efficiency.
- Dataflow templates: Pre-designed blueprints for stream and batch processing optimized for CDC and BigQuery data integration.
- Dataflow job builder: A visual UI for building and running Dataflow pipelines in the Google Cloud console without writing code.
- Dataflow Insights: Offers recommendations for job improvements.
Used for
- Powering gen AI and ML use cases with real-time data
- Delivering personalized experiences to customers
- Integrating real-time ETL and data into BigQuery
- Bringing in streaming data for real-time analytics and operational pipelines
- Reversing ETL processed data from BigQuery to OLTP stores for fast lookups and serving end users
From Google Cloud Dataflow's own website, read 2026-10-23. How we build listings.
How to evaluate Streaming & Real-Time Analytics
This is how the CIOPages Research Team evaluates this category. It is not an assessment of Google Cloud Dataflow. It comes from our Streaming Data & Event Processing buyer guide.
25%
Throughput, Latency & Durability
Sustained throughput and tail latency at your partition count, ordering and exactly-once / at-least-once delivery guarantees, replication and multi-AZ durability, retention limits, and behavior under broker failure and rebalancing
20%
Operating Model & Architecture
Self-managed vs. fully managed vs. serverless vs. bring-your-own-cloud, KRaft (post-ZooKeeper) operations, diskless / object-storage-backed options, autoscaling, partition rebalancing, and upgrade and patching burden on your team
20%
Kafka-API Compatibility & Portability
Fidelity of the Kafka protocol (consumer groups, transactions, compaction), reuse of existing clients, connectors and tooling, lock-in to a single cloud, and the realistic effort to migrate in or out
15%
Stream Processing & Ecosystem
Native or integrated processing (Flink, Kafka Streams, Spark Structured Streaming, ksqlDB), connector catalog (CDC, sinks, sources), schema registry and governance, and direct landing into Iceberg / Delta lakehouse tables
10%
Security, Governance & Compliance
Encryption in transit and at rest, mTLS / SASL / OAuth and RBAC, audit logging, private networking, data residency and BYOC data-boundary control, and SOC 2 / ISO 27001 / GDPR coverage
10%
Cost Model & Economics at Scale
Pricing unit (capacity / throughput / consumption), inter-AZ and egress charges, storage and retention cost, processing billed separately, and how the bill behaves as volume, partitions, and connectors grow
Other Streaming & Real-Time Analytics Vendors
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Quick Facts
cloud.google.com/dataflow Pricing pageCategoryData & Analytics
SubcategoryStreaming & Real-Time Analytics
PricingUsage-based
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