![]() ![]() We recommend using RA3 nodes so you can size compute and storage independently to achieve improved price and performance. When you create a cluster on the Amazon Redshift console, you can get a recommendation of your cluster configuration based on the size of your data and query characteristics (see the following screenshot).Īmazon Redshift offers different node types to accommodate your workloads. You can create materialized views based on one or more source tables using filters, inner joins, aggregations, grouping, functions, and other SQL constructs. Subsequent queries referencing the materialized views use the pre-computed results to run much faster. With materialized views in Amazon Redshift, you can store the pre-computed results of queries and efficiently maintain them by incrementally processing the latest changes made to the source tables. Vertica also has aggregate projection, which acts like a synchronized materialized view. If necessary, use materialized views in Amazon Redshift. Vertica users typically create a projection on a Vertica table to optimize for a particular query. You create external tables in Amazon Redshift within an external schema. You can use external tables to query JSON data stored on Amazon S3 directly from Amazon Redshift. You don’t need to load the JSON data to Amazon Redshift. Vertica has Flex tables to handle JSON data. For tables that are frequently accessed from a business intelligence (BI) reporting or dashboarding interface and for tables frequently joined with other Amazon Redshift tables, it’s optimal to have tables loaded in Amazon Redshift. Amazon Redshift Spectrum is good for heavy scan and aggregate work. This has a positive impact on concurrency. ![]() Also, streaming data coming from Kafka and Amazon Kinesis Data Streams can add new files to an existing external table by writing to Amazon S3 with no resource impact to Amazon Redshift. 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. 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. Amazon Redshift powers analytical workloads for Fortune 500 companies, startups, and everything in between. ![]()
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