![]() The Parquet format is up to two times faster to unload and consumes up to six times less storage in S3, compared to text formats. Data lake exportĪmazon Redshift now supports unloading the result of a query to your data lake on S3 in Apache Parquet, an efficient open columnar storage format for analytics. The following diagram shows how the Concurrency Scaling works at a high-level:įor more information, see New – Concurrency Scaling for Amazon Redshift – Peak Performance at All Times. When the workload demand subsides, Amazon Redshift automatically shuts down Concurrency Scaling resources to save you cost. Concurrency Scaling resources are added to your Amazon Redshift cluster transparently in seconds, as concurrency increases, to serve sudden spikes in concurrent requests with fast performance without wait time. Using Concurrency Scaling, Amazon Redshift automatically and elastically scales query processing power to provide consistently fast performance for hundreds of concurrent queries. Because the data stored in S3 is in open file formats, the same data can serve as your single source of truth and other services such as Amazon Athena, Amazon EMR, and Amazon SageMaker can access it directly from your S3 data lake.įor more information, see Amazon Redshift Spectrum Extends Data Warehousing Out to Exabytes-No Loading Required. Redshift Spectrum supports a variety of structured and unstructured file formats such as Apache Parquet, Avro, CSV, ORC, JSON to name a few. This enables you to independently scale your compute resources and storage across your cluster and S3 for various use cases. Redshift Spectrum is a native feature of Amazon Redshift that enables you to run the familiar SQL of Amazon Redshift with the BI application and SQL client tools you currently use against all your data stored in open file formats in your data lake ( Amazon S3).Ī common pattern you may follow is to run queries that span both the frequently accessed hot data stored locally in Amazon Redshift and the warm or cold data stored cost-effectively in Amazon S3, using views with no schema binding for external tables. It uses a distributed, MPP, and shared nothing architecture. This pattern is powerful because it uses the highly optimized and scalable data storage and compute power of MPP architecture.Īmazon Redshift is a fully managed data warehouse service on AWS. The second diagram is ELT, in which the data transformation engine is built into the data warehouse for relational and SQL workloads. ![]() This pattern allows you to select your preferred tools for data transformations. In the following diagram, the first represents ETL, in which data transformation is performed outside of the data warehouse with tools such as Apache Spark or Apache Hive on Amazon EMR or AWS Glue. The second pattern is ELT, which loads the data into the data warehouse and uses the familiar SQL semantics and power of the Massively Parallel Processing (MPP) architecture to perform the transformations within the data warehouse. The first pattern is ETL, which transforms the data before it is loaded into the data warehouse. This also determines the set of tools used to ingest and transform the data, along with the underlying data structures, queries, and optimization engines used to analyze the data. The primary difference between the two patterns is the point in the data-processing pipeline at which transformations happen. ![]() ![]() There are two common design patterns when moving data from source systems to a data warehouse. Part 2 of this series, ETL and ELT design patterns for modern data architecture using Amazon Redshift: Part 2, shows a step-by-step walkthrough to get started using Amazon Redshift for your ETL and ELT use cases. You also learn about related use cases for some key Amazon Redshift features such as Amazon Redshift Spectrum, Concurrency Scaling, and recent support for data lake export. Part 1 of this multi-post series discusses design best practices for building scalable ETL (extract, transform, load) and ELT (extract, load, transform) data processing pipelines using both primary and short-lived Amazon Redshift clusters. New: Read Amazon Redshift continues its price-performance leadership to learn what analytic workload trends we’re seeing from Amazon Redshift customers, new capabilities we have launched to improve Redshift’s price-performance, and the results from the latest benchmarks. ![]()
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