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. Users can also easily shred the semi-structured data by creating materialized views and can achieve orders of magnitude faster analytical queries, while keeping the materialized views automatically and incrementally maintained.Amazon Redshift is a fully managed, scalable cloud data warehouse that accelerates your time to insights with fast, easy, and secure analytics at scale. These make ingesting and querying schemaless data much easier now that users do not have to pre-discover data types for each ingested source, handle evolving schemas or write complex SQL to account for different types when querying the data. PartiQL features that facilitate ELT include schemaless semantics, dynamic typing and type introspection abilities in addition to its navigation and unnesting. Data engineers can achieve simplified and low latency ELT (Extract, Load, Transform) processing of the inserted semi-structured data directly in their Redshift cluster without integration with external services. This enables new advanced analytics that discover combinations of structured and semi-structured data. PartiQL allows access to schemaless and nested SUPER data via efficient object and array navigation, unnesting, and flexibly composing queries with classic analytic operations such as JOINs and aggregates. PartiQL is an extension of SQL that is adopted across multiple Amazon Web Services offerings. Amazon Redshift supports the parsing of JSON data into SUPER and up to 5x faster insertion of JSON/SUPER data in comparison to inserting similar data into classic scalar columns. The SUPER data type is schemaless in nature and allows for storage of nested values that could consist of Redshift scalar values, nested arrays or other nested structures.
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