AWS Redshift Best Practices for

Warehousing is the process of collecting data from multiple sources and effectively managing the company's data to derive useful business insights from it. It is one of the most crucial ways of dealing with large volumes of data to extract value from it in the easiest way possible. So selecting the right data warehouse should be our topmost priority. For optimal performance, many firms have already started shifting to cloud technologies that help in reducing manual work hence reducing the chance of errors. AWS redshift is the most in-demand data warehouse that provides all these facilities in a cost-effective way. To make the most out of redshift its best practices should be adopted to use it to our advantage. To know more about redshift best practices check out this article.

What is Amazon Redshift?

To start with: let's get familiar with Redshift.

AWS Redshift, as Amazon defines it, is a fully managed data warehouse service in the cloud capable of scaling up to petabytes. Organizations and businesses that need to store and handle huge amounts of data should consider using AWS Redshift.

Amazon Redshift is ideal for vast data analysis, storage, and migrations due to its efficient massively parallel processing (MPP) technology, delivering fast and cost-effective results loading data everywhere.

Amazon Redshift data warehouses consist of clusters with data blocks from one or more nodes running the Redshift engine. It is an analytics database but also suitable for traditional relational databases. Furthermore, it is a column-oriented database that enables optimized read/write speeds due to its columnar data storage format.

Redshift is an OLAP-style (Online Analytical Processing) data warehouse that is fully managed, enabling users to scale up their data from gigabytes to petabytes.

Here are some Amazon Redshift best practices -

1. Deciding the appropriate data distribution style and selecting the correct distribution keys

Choosing the correct distribution style in AWS redshift can enhance the query performance for different operations and can be proven to be time-efficient. Once the data is loaded into the table redistribution of rows of the table to different compute nodes takes place in the redshift cluster according to the table distribution style. The main purpose of choosing the correct distribution style is to locate data regardless of the impact of redistribution steps in less time. 

           The four distribution styles used in amazon redshift are listed below :

A. Key Distribution style

As the name suggests, key distribution is defined for the table as something that is leveraged by the redshift cluster for efficient data distribution. A good practice for key distribution table creation style is to use this distribution style if your tables are laboriously used in joining queries. If you have a major query with a huge dimension table that you want to optimize then go for key distribution else go for other distribution styles as it can even make your queries slower.

B. Even Distribution style 

This is one of the simplest distribution styles in which data from same node is evenly distributed by a leader node of the cluster one after another in a round-robin fashion. This distribution style is basically used in tables where no joining conditions are used.

C. All Distribution style 

In this distribution style, the leader node harbors the replica of tables on all the computing nodes that are responsible for occupying more space as each and every node has a local copy of the data faster query operations are performed using this distribution style. Generally, this distribution style is preferred for the tables which are small in size and that do not change continually so that less storage on the redshift cluster is used.                   

D. Auto Distribution style

It is a default distribution style in which AWS redshift decides the distribution type for tables primarily depending on their size. The benefit of using this distribution style load data now is that in the future if the data will increase with time then the AWS redshift will automatically revamp the type of distribution.

    2. Proper utilization of query monitoring feature

AWS redshift uncovers the QMR capabilities that enable users to easily monitor queries and key metrics in a user-friendly and intuitive manner. Some automated suggestions are also provided by amazon redshift for optimal query performance. You can also define what actions to take if a query goes past the set limits. Along with this, you can even monitor the exceeding space utilization by each query and if needed you can easily eradicate such queries with the query monitoring feature in amazon redshift data warehouse.

   3. Vacuuming Tables 

Vacuuming tables is a crucial step for the health and maintenance of the AWS   redshift cluster. The main purpose is to reclaim the free space and use it more efficiently which will be responsible for enhancing the performance of the queries. Amazon redshift automatically does not reclaim the unused space that is free whenever a row is deleted. There is an acute need to regularly clean up tables after a bulk delete to free up the extra space and enhance query performance. 

The below query is performed in two steps first it will reclaim the space and then it will sort the rows. These steps occur in sequence first, AWS redshift will free the disk blocks take up the extra space and then it will sort the remaining data.

Vacuum <table_name>;

   4. Defining User Groups 

To ensure the safety of data in the data warehouse defining user groups can be the best practice for AWS redshift. The control of who can access the data stored in the warehouse is with administrators and selective permissions can be easily given to a specific group. With the user groups feature in AWS redshift data security and integrity can be ensured by only permitting specific users to access the data.

   5. Sorting Data using sort keys for efficient querying 

Sort Keys are a robust set of tools for optimizing query execution. They are used to sort the data and store it in a particular order in a node. If data is sitting across various nodes the way of storing and sorting the records totally depends on the preference of a sort key. So specifying sort keys in joining conditions and filters helps in boosting the query performance by lowering the number of iterations. Maximum query performance is acquired for any query execution by selecting the right sort key. Some ways for selecting the correct sort keys are : 

• The timestamp column can be used by the leader node as the sort key if recent data is queried most often.
• If the query involves frequent joining of tables then specifying the join columns as both sort key and distribution key is preferred.

This article mainly deals with the best practices for AWS redshift to optimize the query performance facilitating advanced level analytics. Following these practices can make your experience with redshift a lot smoother and more profitable. Apart from this AWS redshift provides several advantages such as speed, performance, and scalability to manage high data volumes creating queries generated daily.

TL;DR 

• The best practices cited in the article can actually improve your experience while using AWS redshift and can help you to make the most out of it.
• Practices such as  Sorting data using sort keys for efficient querying, defining user groups, vacuuming tables, proper utilization of query monitoring features, deciding the appropriate data distribution style, and selecting the correct distribution keys are considered for promoting security and giving the best performance  
• Read more to get updated with the best AWS redshift practices for being one step ahead of others.