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In this tutorial, you learn how to use Amazon Redshift Spectrum to query data directly from files on Amazon S3. If you already have a cluster and a SQL client, you can complete this tutorial with minimal setup.
- Set Up The Test Environment
- Best Practices For Storage
- Best Practices For Choosing Compute Capacity
- Best Practices For When to Use Redshift Spectrum
- Best Practices For Query Cost Control
- Conclusion
To perform tests to validate the best practices we outline in this post, you can use any dataset. Amazon Redshift Spectrum supports many common data formats: text, Parquet, ORC, JSON, Avro, and more. You can query data in its original format or convert data to a more efficient one based on data access pattern, storage requirement, and so on. For ex...
For storage optimization considerations, think about reducing the I/O workload at every step. That tends toward a columnar-based file format, using compression to fit more records into each storage block. The file formats supported in Amazon Redshift Spectrum include CSV, TSV, Parquet, ORC, JSON, Amazon ION, Avro, RegExSerDe, Grok, RCFile, and Sequ...
This section offers some recommendations for choosing the right compute capacity to get optimal performance in Amazon Redshift Spectrum.
With Amazon Redshift Spectrum, you can run Amazon Redshift queries against data stored in an Amazon S3 data lake without having to load data into Amazon Redshift at all. Doing this not only reduces the time to insight, but also reduces the data staleness. Under some circumstances, Amazon Redshift Spectrum can be a higher performing option.
As mentioned earlier in this post, partition your data wherever possible, use columnar formats like Parquet and ORC, and compress your data. By doing so, you not only improve query performance, but also reduce the query cost by reducing the amount of data your Amazon Redshift Spectrum queries scan. You can also help control your query costs with th...
In this post, we provide some important best practices to improve the performance of Amazon Redshift Spectrum. Because each use case is unique, you should evaluate how you can apply these recommendations to your specific situations. We want to acknowledge our fellow AWS colleagues Bob Strahan, Abhishek Sinha, Maor Kleider, Jenny Chen, Martin Grund,...
Using Amazon Redshift Spectrum, you can efficiently query and retrieve structured and semistructured data from files in Amazon S3 without having to load the data into Amazon Redshift tables. Redshift Spectrum queries employ massive parallelism to run very fast against large datasets.
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This topic describes details for using Redshift Spectrum to efficiently read from Amazon S3. Amazon Redshift Spectrum resides on dedicated Amazon Redshift servers that are independent of your cluster.
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People also ask
When should I use Amazon Redshift spectrum?
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What is Amazon Redshift & how does it work?
Apr 28, 2021 · Lake House approach. As a modern data architecture, the Lake House approach is not just about integrating your data lake and your data warehouse, but it’s about connecting your data lake, your data warehouse, and all your other purpose-built services into a coherent whole.
