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What is streaming analytics?

Streaming analytics is the processing and analyzing of data records continuously rather than in batches. Generally, streaming analytics is useful for the types of data sources that send data in small sizes (often in kilobytes) in a continuous flow as the data is generated.

Learn about Dataflow, Google Cloud’s unified stream and batch data processing service.

Streaming analytics overview

Streaming analytics may include a wide variety of data sources, such as telemetry from connected devices, log files generated by customers using web applications, ecommerce transactions, or information from social networks or geospatial services. It’s often used for real-time aggregation and correlation, filtering, or sampling.

Data traditionally is moved in batches. Batch processing often processes large volumes of data at the same time, with long periods of latency. For example, a process may be run every 24 hours. While this can be an efficient way to handle large volumes of data, it doesn’t work with time-sensitive data that’s meant to be streamed, because that data can be stale by the time it’s processed.

How to optimize streaming analytics

When companies are collecting data to the tune of hundreds of thousands or even millions of events per second, absolutely massive datasets are the result. Traditional systems can take days to deliver insights from data at this scale. 

To generate real-time actions, you need real-time data processing and analysis. This can be accomplished with the right data-streaming platform and infrastructure. Stream analytics built on Google Cloud products and services, for example, enable companies to ingest, process, and analyze data streams in real time.

Streaming analytics use cases

Companies use streaming analytics to analyze data in real time and provide insights into a wide range of activities, such as metering, server activity, geolocation of devices, or website clicks. Likely use cases include:

Ecommerce

Analyze user clickstreams to optimize the shopping experience with real-time pricing, promotions, and inventory management.

Financial services

Analyze account activity to detect anomalous behavior in the data stream and generate a security alert for abnormal behavior.

Investment services

Track market changes and adjust settings to customer portfolios based on configured constraints, such as selling when a certain stock value is reached.

News media

Stream user click records from various news source platforms and enrich the data with demographic information to better serve articles that are relevant to the targeted audience.

Utilities

Monitor throughput across a power grid and generate alerts or initiate workflows when established thresholds are reached.

Stream analytics from Google Cloud makes data more organized, useful, and accessible from the instant it’s generated. Built on the autoscaling infrastructure of Pub/Sub, Dataflow, and BigQuery, stream analytics from Google Cloud provisions the resources you need to ingest, process, and analyze fluctuating volumes of real-time data for real-time business insights and actions. This abstracted provisioning reduces complexity and makes stream analytics accessible to both data analysts and data engineers.