
Amazon Redshift streaming ingestion enables near-real-time analytics by directly ingesting DynamoDB changes through Kinesis Data Streams, using materialized views and PartiQL to transform nested JSON into fact and dimension tables for dashboard visualization.


<div><p>This article demonstrates how to build near-real-time analytics by streaming DynamoDB changes through Kinesis Data Streams into Amazon Redshift for dashboard visualization.</p><ul><li>Stream DynamoDB item-level changes to Kinesis Data Streams automatically</li><li>Create streaming materialized views in Redshift to consume live Kinesis data</li><li>Use PartiQL and dot notation to unnest complex JSON documents into columns</li><li>Build fact and dimension tables with transformation logic via stored procedures</li><li>Maintain metadata audit tables to track incremental data processing</li><li>Use window functions to identify latest non-null values per transaction</li><li>Schedule refreshes via EventBridge or Query Editor v2 for continuous updates</li><li>Support cross-account scenarios with IAM role assumption</li></ul><p>This solution enables continuous data warehouse refresh with minimal latency, allowing real-time analytics dashboards without complex ETL infrastructure.</p></div>


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