We will maintain the current state of the ELT process (e.g., Running or Ready) in an audit table that will be maintained at Amazon DynamoDB. As part of the ELT process, we will refresh the dimension and fact tables at regular intervals from staging tables, which ingest data from the source. In this solution, we will orchestrate an ELT process using AWS Step Functions. This post explains how to use AWS Step Functions, Amazon DynamoDB, and Amazon Redshift Data API to orchestrate the different steps in your ELT workflow and process data within the Amazon Redshift data warehouse. It can ensure that the long-running, multiple ELT jobs run in a specified order and complete successfully instead of manually orchestrating those jobs or maintaining a separate application.Īmazon DynamoDB is a fast, flexible NoSQL database service for single-digit millisecond performance at any scale. It also ensures the timely and accurate refresh of your data warehouse.ĪWS Step Functions is a low-code, serverless, visual workflow service where you can orchestrate complex business workflows with an event-driven framework and easily develop repeatable and dependent processes. When you adopt an ELT pattern, a fully automated and highly scalable workflow orchestration mechanism will help to minimize the operational effort that you must invest in managing the pipelines. With Amazon Redshift, you can load, transform, and enrich your data efficiently using familiar SQL with advanced and robust SQL support, simplicity, and seamless integration with your existing SQL tools. ![]() This eliminates the need to rewrite relational and complex SQL workloads into a new framework. ![]() When you use an ELT pattern, you can also use your existing SQL workload while migrating from your on-premises data warehouse to Amazon Redshift. Extract, Load, and Transform (ELT) is a modern design strategy where raw data is first loaded into the data warehouse and then transformed with familiar Structured Query Language (SQL) semantics leveraging the power of massively parallel processing (MPP) architecture of the data warehouse.
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