Generate near real time insights through streaming data ingestion into your data warehouse and data visualizations.
Natively integrating with Amazon streaming engines, Amazon Redshift Streaming Ingestion ingests hundreds of megabytes of data per second so you can query data in near real time and drive your business forward with analytics. With this zero-ETL approach, Amazon Redshift Streaming Ingestion enables you to, connect to multiple Amazon Kinesis Data Streams or Amazon Managed Streaming for Apache Kafka (MSK) data streams and pull data directly to Amazon Redshift without staging data in Amazon Simple Storage Service (S3). Define a schema or choose to ingest semi-structured data with SUPER data type. Derive insights with data visualizations using a business intelligence solution like Amazon QuickSight.
Benefits
High throughput, low latency
Process large volumes of streaming data from multiple sources with low latency and high throughput to derive insights in seconds.
Simplified ingestion process
Directly ingest streaming data into your data warehouse from Kinesis Data Streams and MSK without the need to stage in Amazon S3.
Easy to get started managing downstream processing
Perform rich analytics on streaming data within Amazon Redshift using familiar SQL. Define and build materialized views on top of streams directly. Create and manage downstream ELT pipelines by creating MV on MVs, using user-defined functions and stored procedures in Amazon Redshift.
Visualize your data in near real-time
Generate insights by visualizing your streaming data within a business intelligence solution of your choice. Build charts and other visuals within a solution like Amazon QuickSight, a unified serverless business intelligence solution with native ML integrations, to enable data driven decision making in your organization. Use machine learning powered Amazon QuickSight Q to ask conversational questions of your data and receive answers through relevant visualizations.
Use cases
Improve gaming experience
Increase in-game conversion, player retention, and optimize gaming experience by analyzing real-time data from gamers.
Analyze IoT data in real-time
Analyze data from thousands of IoT devices and use machine learning (ML) within Amazon Redshift to improve operations, predict customer churn, and grow your business.
Analyze clickstream user data
The average customer visits dozens of websites in a single session, yet marketers typically analyze only their own websites. Analyze authorized clickstream data ingested into the warehouse to assess your customers’ footprint and behaviors.
Conduct real-time troubleshooting
By accessing and analyzing streaming data from application log files and network logs, developers and engineers can conduct real-time troubleshooting, deliver better products, and alert systems for preventative measures.
Near real-time retail analytics on streaming POS data
Access and visualize in near real time all POS retail sales transaction data for real-time analytics, reporting, and visualization.
Customer Quotes
”LiveMe is a live broadcast app that attracts more than 1 million anchors from over 220 countries. Our app powers more than 100k hours of live broadcasts every day. We use Amazon Redshift's streaming ingestion and other Amazon services for risk control over users' financial activity such as recharge, refund, and rewards. With Amazon Redshift, we are able to view risk control reports and data in near real time, instead of on an hourly basis. This significantly improved our business efficiency.”
PengBo Yang, CTO, Joyme (parent company of LiveMe)
