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What is Data streaming?

Data streaming is the continuous processing of data as it is generated. It is also known as event processing or stream processing. The benefit of data streaming is that analytics, insights and actions can occur immediately. Data streaming is used across industries like financial services, retail, media and entertainment, and telecommunications.


Data streaming explained

Data streams are never ending flows of data that can be transformed, analyzed or otherwise processed in real time whilst in motion. Data streams are generated by all sorts of events and systems, from network elements to financial transactions, website clicks and more.


Overview
of stream data processing

Data streams can come from many sources and can be processed in different ways. Some example sources and forms of processing are listed below.


Sources

Sources for data streams can include:


  • IoT sensors
  • Server logs
  • Applications
  • Third party services
  • Network elements

Processing

Processing performed on data streams can include:


  • Ingestion
  • Transformation
  • Aggregation
  • Combining data streams

Batch processing
versus data streaming

Batch processing is about processing sets of data. Batch processes are typically run on a schedule, for example once an hour, or once a week, which is different to data streaming, where processing typically takes place continuously as data arrives.

Batch processing can be an efficient way to process data in bulk, but might not offer as timely results versus data streaming.


Streaming use cases

Data streaming is widely used wherever immediate actions or insights are needed. Use cases are often found in online retail, financial fraud analysis, advertising and investment.



Real time advertising auctions


High frequency trading


Credit card fraud detection


Network intrusion detection and prevention


Security operations automated response (SOAR)


Predictive maintenance


Machine learning model optimization


Canonical solutions for data streaming


Apache Spark®

Canonical offers a sophisticated solution for large scale streaming data processing founded on Apache Spark, a free and open source framework for data processing from the Apache Software Foundation.

Canonical's Charmed Spark solution includes containerized images for Apache Spark with up to 10 years of security maintenance and best-in-class support from Canonical, and advanced deployment and operations automation to help you get the most from deploying Apache Spark on Kubernetes.


Apache Kafka®

Canonical offers an advanced solution for deploying and operating Apache Kafka, a free and open source event data processing hub developed by the Apache Software Foundation.

Canonical's Charmed Kafka solution includes support for deploying, configuring, securing, managing, maintaining and monitoring Kafka on cloud VMs or on Kubernetes and includes packages for Apache Kafka maintained by Canonical, with up to 10 years of security maintenance and SLA-backed support available.


Questions? Get answers

Do you have a data streaming project in mind and want to get advice on implementing Kafka or Spark? Contact us now to discuss your needs.


Contact us


Apache®, Apache Kafka, Kafka®, and the Kafka logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.

Apache®, Apache Spark, Spark®, and the Spark logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.