3 Best Practices of Amazon Redshift SQL


Amazon Redshift is unlike most SQL database systems. To truly experience the perks of its architecture, you must specifically build, design, and load tables to utilize massively parallel processing, columnar data compression, and columnar data storage. Key information may be overlooked if the query execution and data loading take too long. Knowing the best practices for Redshift SQL is essential, even if you are a seasoned database developer. That way, you can discover the important principles and how to effectively implement them.

When considering the best practices for Amazon Redshift SQL, keep in mind that no single practice is standard to every application. So, be sure to evaluate all options before you finalize the database design.

Here are the top three best practices you should know:

1. Use a web-based Amazon Redshift SQL workbench

A web-based client should come with all the database management and business intelligence tools you will need to quickly connect, perform Redshift database tasks, load data, generate dashboards and charts, and build visual SQL queries with only a few clicks. Make sure it’s an intuitive and collaborative platform that you can access from your browser, so it is easier to access and share visualizations and queries.

2. Designing tables

When planning a database, specific key table design decisions will have a significant impact on the overall query performance. Your design choices can also affect storage requirements, which are likely to affect query performance by minimizing the amount of I/O operations and reducing the memory required to process the queries. Here’s what you can do:

  • Pick the best sort key
  • Choose an appropriate distribution style
  • Let COPY pick the compression encodings
  • Define primary key and foreign key constraints
  • Utilize the smallest possible size for the column
  • Use date/time data types for the date columns

3. Loading data

Loading massive datasets take a lot of time and require plenty of computing resources. How data is loaded can affect query performance, too. By knowing the best practices for loading data efficiently in Redshift SQL, the process can be smoother. Use COPY commands, stage tables, and bulk inserts.



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