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To automate this process, you can add this REFRESH command as a part of your ETL script’s initialization: To ensure materialized views are updated with the latest changes, you must refresh the materialized view before executing an ETL script.

Redshift materialized views update#

Amazon Redshift uses only the new data to update the materialized view it does not update the entire table. Materialized views refresh much faster than updating a temporary table because of their incremental nature. Once you create a materialized view, to get the latest data, you only need to refresh the view. However, as the underlying tables get updated with INSERTS, UPDATES, DELETES, or COPY from Amazon S3 options, the temporary table would get stale, and you would need to recreate the temporary table to keep the data fresh. Before materialized views, you would create a temporary table using CTAS (CREATE TABLE AS SELECT). Amazon Redshift Materialized ViewsĪmazon Redshift materialized views contain precomputed results sets that have been queried from one or more tables. We recommend you launch your Amazon Redshift clusters in the same virtual private cloud (VPC) or region as the Matillion AMI on Amazon Elastic Compute Cloud (Amazon EC2), as shown in Figure 1.įigure 1 – Matillion ETL for Amazon Redshift architecture.ĭetailed setup instructions are available with AWS CloudFormation templates on the Matillion site. You can launch Matillion ETL for Amazon Redshift either as an Amazon Machine Image (AMI), or by fitting it into your AWS CloudFormation template, which is also available through AWS Quick Starts. Matillion ETL transforms the data in the same way, regardless of source, by creating stream batches to a staging file in Amazon Simple Storage Service (Amazon S3), and then using the Amazon Redshift copy command to load the data. Once the orchestration job is set up, Matillion ETL first loads and then transforms the data to make it consumable by analytics tools such as Amazon Quicksight, Looker, Tableau, Power BI, and others. Matillion ETL uses orchestration jobs to handle data using pre-built connectors for software-as-a-service (SaaS) applications, NoSQL, files, on-premises and cloud databases, as well as from any RESTful API source system. In this post, we’ll show you how to get those results. We found that job runtimes were consistently 9.75 x faster when using materialized views than when using standard views.

Matillion is an AWS Advanced Technology Partner with the AWS Data & Analytics Competency and Amazon Redshift Ready designation.īy using Matillion ETL with the new materialized views in Amazon RedShift, you can improve the performance of an extract, transform, and load (ETL) job and simplify your data pipeline. Matillion ETL for Amazon Redshiftprovides comprehensive enterprise-grade features to simplify and speed up building and maintaining these pipelines. That, in turn, reduces the time to deliver the datasets you need to produce your business insights. Powering these dashboards requires building and maintaining data pipelines with complex business logic.Īmazon Redshift recently announced support for materialized views, which lead to significantly faster query performance on repeatable query workloads.

These decisions are based on analytical dashboards that provide a point-in-time view of a specific business vertical. In modern business environments and data-driven organizations, decisions are rarely made without insights. By Dilip Rajan, Partner Solution Architect at AWS