If you have a huge historical dataset being shared by multiple compute platforms, then it’s a good candidate to keep on Amazon Simple Storage Service (Amazon S3) and utilize Amazon Redshift Spectrum. In a Vertica data warehouse, you plan the capacity for all your data, whereas with Amazon Redshift, you can plan your data warehouse capacity much more efficiently. Data placementĪmazon Redshift powers the lake house architecture, which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights not possible otherwise. We also look at the Vertica schema and decide the best data distribution and sorting strategies to use for Amazon Redshift, if you choose to do it manually. In this section, we discuss how to size the Amazon Redshift cluster based on the size of the Vertica dataset that you’re moving to Amazon Redshift. Your business use case drives what data gets loaded to Amazon Redshift and what data remains on the data lake. When planning your migration, start with where you want to place the data. Finally, we cover how cluster management on Amazon Redshift differs from Vertica.Ģ023 Linux Foundation TAB election call for nominees ![]() We also see how to speed up the data migration to Amazon Redshift based on your data size and network connectivity. We look at the tools for schema conversion and see how to choose the right keys for distributing and sorting your data. We discuss how to plan for the migration, including sizing your Amazon Redshift cluster and strategies for data placement. In this post, we discuss the best practices for migrating from a self-managed Vertica cluster to the fully managed Amazon Redshift solution. Amazon Redshift is a fully managed cloud solution you don’t have to install and upgrade database software and manage the OS and the hardware. ![]() When you use Vertica, you have to install and upgrade Vertica database software and manage the cluster OS and hardware. With Amazon Redshift, you can query petabytes of structured and semi-structured data across your data warehouse, operational database, and your data lake using standard SQL. Post Syndicated from Seetha Sarma original Īmazon Redshift powers analytical workloads for Fortune 500 companies, startups, and everything in between.
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