Create a Kinesis data stream called demo-data-stream.To configure your Kinesis data stream, complete the following steps: An existing DynamoDB table with an active workload.For instructions, refer to Create a workgroup with a namespace. An Amazon Redshift workgroup if you are using Amazon Redshift Serverless.For instructions, refer to Create a sample Amazon Redshift cluster. An Amazon Redshift cluster if you are using Amazon Redshift Provisioned.Establish connectivity between a QuickSight dashboard and Amazon Redshift to deliver visualization and insights.Create fact and dimension tables in the Amazon Redshift cluster and keep loading the latest data at regular intervals from the staging table using transformation logic.Use a combination of a PartiQL statement and dot notation to unnest the JSON document into data columns of a staging table in Amazon Redshift. The streaming data gets ingested into a JSON payload.Create a streaming materialized view in your Amazon Redshift cluster to consume live streaming data from the data stream.Create a Kinesis data stream and turn on the data stream from DynamoDB to capture item-level changes in your DynamoDB table.The process flow includes the following steps: The solution uses Kinesis Data Streams to capture item-level changes from an application DynamoDB table.Īs shown in the following reference architecture, DynamoDB table data changes are streamed into Amazon Redshift through Kinesis Data Streams and Amazon Redshift streaming ingestion for near-real-time analytics dashboard visualization using Amazon QuickSight. We also walk through using PartiQL in Amazon Redshift to unnest nested JSON documents and build fact and dimension tables that are used in your data warehouse refresh. We walk through an example pipeline to ingest data from an Amazon DynamoDB source table in near-real time using Kinesis Data Streams in combination with Amazon Redshift streaming ingestion. In this post, we discuss a solution that uses Amazon Redshift streaming ingestion to provide near-real-time analytics. With this capability in Amazon Redshift, you can use SQL (Structured Query Language) to connect to and directly ingest data from data streams, such as Amazon Kinesis Data Streams or Amazon Managed Streaming for Apache Kafka (Amazon MSK) data streams, and pull data directly to Amazon Redshift. Amazon Redshift streaming ingestion simplifies data pipelines by letting you create materialized views directly on top of data streams. You can use the Amazon Redshift streaming ingestion capability to update your analytics databases in near-real time. You can run and scale analytics in seconds on all your data without having to manage your data warehouse infrastructure. Tens of thousands of customers rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries, making it the widely used cloud data warehouse. Amazon Redshift is a fully managed, scalable cloud data warehouse that accelerates your time to insights with fast, easy, and secure analytics at scale.
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