Stream analytics and real-time insights

Ingest, process, and analyze event streams in real time

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Real-time gets real easy

Stream analytics from Google Cloud can make data organized, useful, and accessible from the instant it's generated. Built on the autoscaling infrastructure of its core components—Cloud Pub/Sub, Cloud Dataflow, and BigQuery—Google's streaming solution reduces complexity by provisioning the exact resources needed to ingest, process, and analyze fluctuating volumes of real-time data. With provisioning abstracted, Google Cloud makes stream analytics accessible to both data analysts and data engineers through simple and familiar tools.

stream analytics from Google Cloud

Scale up infrastructure and scale down headaches

Google Cloud’s streaming infrastructure autoscales to match the exact needs of your job, even if you're not sure what those needs are. That means you can offload the challenges of variable data volumes, performance tuning, resource provisioning, and more to Google, while you focus on real-time analysis and insights. No need to plan ahead or overprovision, and no need to overpay for unused resources.

infrastructure scale up

Adopt simple ingestion for complex events

Cloud Pub/Sub, Google Cloud's stream ingestion service, can ingest and deliver hundreds of millions of events each second. With Cloud Pub/Sub, once an event is published to a topic, any number of data pipelines can receive it. Global topics make ingestion seamless across your choice of geographies, either directly from servers or from connected devices through IoT Core. BigQuery's streaming API provides direct stream ingestion into the data warehouse for SQL-based ELT use cases. For Apache Kafka users, Confluent and Google Cloud partner to deliver Kafka as a native service.

stream ingestion service

Unify stream and batch processing without lock-in

Cloud Dataflow is designed to handle real-life streaming, where the data you need to enrich and transform for analysis comes in batch, stream, and stream-of-files modes. Engineers can reuse code across these modes through Apache Beam, Cloud Dataflow's open-source SDK. Beam provides pipeline portability (to Apache Flink, Samza, and other frameworks) for hybrid or multi-cloud environments, and delivers language flexibility that includes Python, SQL, and Java. Dataflow automatically handles resource management and ensures exactly once processing, making your streaming pipelines more reliable and consistent.

real-life streaming and batch processing

Keep your current tools while exploring next-generation AI

Existing on-premises and cloud streaming architectures often deploy Apache Kafka and Apache Spark. Google Cloud can bridge, migrate, or extend those solutions through Confluent Cloud and Cloud Dataproc. When these services combine with Cloud Data Fusion's GUI, data analysts and engineers alike can build streaming pipelines. No matter how you choose to implement real-time analytics, Google Cloud's extensive portfolio of accessible AI products can deepen your streaming analysis and speed up action, with or without machine learning experience.

exploring next-generation AI


Service Use Case for Stream Analytics
Cloud Pub/Sub For large-scale ingestion of streaming data originating anywhere in the world. (Open source alternative in this solution: Apache Kafka)
Cloud Dataflow For transforming and enriching ingested data in streaming and batch modes with equal reliability and expressiveness. (Open source alternative in this solution: Spark on Cloud Dataproc)
BigQuery Fully-managed data warehouse service that supports 100,000 streaming row inserts per second and allows ad hoc analysis on real-time data with standard SQL.
Apache Beam Unified development framework for programming streaming and batch pipelines. Shipped by Google as Cloud Dataflow SDK 2.x.
Cloud Machine Learning Add an extra layer of intelligence to your pipeline by running the event streams through custom (Cloud Machine Learning Engine) or pre-built (Cloud APIs) TensorFlow-based machine-learning models.
Cloud Bigtable Low-latency wide-column key-value store, ideal for high-volume time series and read latency-sensitive applications.

Additional Resources

Exactly-once Processing

Learn the meaning of “exactly once” processing in Cloud Dataflow.

View Blog Post

Cloud Dataflow: Sample Pipelines

Understand how pipelines work through mobile gaming examples.

View Documentation

Codelab: NYC Taxi Tycoon

Step through a guided hands-on coding experience on how to process streaming data with Dataflow and Pub/Sub.

Explore Sample App

Financial Services Solution

Build a near real-time analytics system that can scale to thousands of simultaneous data streams.

Read Solution Paper

Architecture Diagram

Review the architecture for optimizing large-scale analytics ingestion on Google Cloud Platform.

Read Article

Streaming 101

Read Tyler Akidau’s seminal paper on the world beyond batch.

Read Paper