We cover the second step, reconfiguring pipelines, in a later post. In this post, we show you how to migrate data from Snowflake to Amazon Redshift. Reconfigure data pipelines to move data from sources to Amazon Redshift and Amazon S3, which provide a unified, natively integrated storage layer of our Lake House Architecture.Migrate raw, transformed, and prepared data from Snowflake to Amazon Simple Storage Service (Amazon S3).To take advantage of these capabilities and future innovation, you need to migrate from your current data warehouse, like Snowflake, to Amazon Redshift, which involves two primary steps: For the latest feature releases for AWS services, see What’s New with AWS? The machine learning (ML) based self-tuning capability to set sort and distribution keys for tables significantly improves query performance that was previously handled manually. ![]() The new distributed and hardware accelerated cache with AQUA (Advanced Query Accelerator) for Amazon Redshift delivers up to10 times more performance than other cloud warehouses. For example, concurrency scaling, the new RA3 instance types, elastic resize, materialized views, and federated query, which allows you to query data stored in your Amazon Aurora or Amazon Relational Database Service (Amazon RDS) Postgres operational databases directly from Amazon Redshift, and the SUPER data type, which can store semi-structured data or documents as values. ![]() Several new features of Amazon Redshift address a wide range of data requirements and improve performance of extract, load, and transform (ELT) jobs and queries. Data warehouses play a key role in storing, transforming, and making data easily accessible to enable a wide range of use cases, such as data mining, business intelligence (BI) and reporting, and diagnostics, as well as predictive, prescriptive, and cognitive analysis. The need to derive meaningful and timely insights increases proportionally with the amount of data being collected.
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