Many organizations use batch data and real-time data streaming reports to gain strategic and actionable insights into long-term business trends. A growing number of customers use streaming data processing with new and dynamic data generated on a continual basis in big data use cases. The streaming data is used to produces reports, perform actions based on thresholds or perform more sophisticated forms of data analysis, like applying machine learning algorithms.
The Real-Time Analytics with Spark Streaming guidance automatically configures the AWS services necessary to easily ingest, store, process, and analyze both real-time and batch data using functions from business intelligence architecture and big data architecture. This guidance deploys a highly available, secure, flexible, cost-effective streaming data analytics architecture on the AWS Cloud that leverages Apache Spark Streaming and Amazon Kinesis.
The diagram below presents the architecture you can build using the example code on GitHub.
Real-Time Analytics with Spark Streaming Guidance architecture
This guidance deploys an Amazon Virtual Private Cloud (Amazon VPC) network with one public and one private subnet. The public subnet contains a NAT gateway and a bastion host. The private subnet hosts the Amazon EMR cluster with Apache Zeppelin.
Amazon Kinesis Data Streams collects data from data sources and sends the data through the NAT gateway to the Amazon EMR cluster. After the Spark Streaming application processes the data, it stores the data in an Amazon S3 bucket.
Real-Time Analytics with Spark Streaming reference implementation
Apache Zeppelin support
Spark Streaming application
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