To do this, go to AWS Glue and add a new connection to your RDS database. Configure the AWS Glue Crawlers to collect data from RDS directly, and then Glue will develop a data catalog for further processing. What you want to do first is establish ETL runtime for extracting data stored in Amazon RDS.
Bookmarks act as points to which you can rewind your Glue jobs.
#Redshift mysql plus#
You can run multiple Spark ETL jobs in an efficient way, plus you have the ability to create bookmarks at any point. Glue is an ETL service that can also perform data enriching and migration with predetermined parameters, which means you can do more than copy data from RDS to Redshift in its original structure.įor example, Glue supports FindMatches ML Transform, and it works with Apache Spark. Moving data to and from Amazon Redshift is something best done using AWS Glue. NOTE: It can read and write data from the following AWS services. AWS Glue is a fully managed ETL service (extract, transform, and load) for moving and transforming data between your data stores. Regardless of the size of the data set, Amazon Redshift can provide fast query performance by using other SQL-based tools and business intelligence applications.Īmong those tools, to help you fully take advantage of the data warehouse platform, is AWS Glue which you can use to migrate your data from RDS to Redshift. So, it works optimally in handling petabytes of structured and semi-structured data.
Redshift integrates well with other AWS services and is itself a fully managed, petabyte-scale data warehouse service in the cloud. If you are building a data lake, for example, moving from Amazon RDS to Amazon Redshift is a logical decision to make. However, sticking with a more traditional way of storing data or running database services isn’t efficient either. Moving large amounts of data is always a cumbersome task to do, especially when there are adjustments to be made along the way.
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