- August 4, 2022
- Posted by: Vaibhavi Tamizhkumaran
- Categories: Data Analytics, Data engineering, Data Migration
Data is the king of today’s world. At the rate at which it is growing, enterprises will find it difficult and complex to manage. Traditional data warehousing systems are unable to keep up with the increasing demands. Organizations are unable to make the optimum use of their data because of rigid architectures that need significant expenditure to manage, update, and secure.
Do you want to build a corporate culture that embrace’s insights from the available data, here is the best practices to follow while migrating to Amazon Redshift:
While preparing for an AWS data migration solution, have answers for the following questions:
A clear vision for your company: To construct a successful data warehouse on Redshift, you must first understand the business and existing business logic involved in data conversion.
The terms “volume” and “velocity” are used interchangeably. Perform a volumetric study of the existing data warehouse’s hot and cold data system. There are a variety of migration solutions for moving large amounts of data from on-premises to Redshift.
Make a list of all the items that were created on-premises and reproduce them in Redshift. Most existing SQL client apps will run on Redshift with minor modifications.
Building a good data warehouse logic that removes duplicates while loading the data is key. As Redshift does not insist on a unique, primary key, or foreign key, a good logic that avoids duplicate is much needed. It is essential to check for duplicates before loading as removal of duplicates is an expensive affair.,
Redshift does not offer to index; however, it does have a useful feature called the sort key. Choosing the correct sort key column and type will improve query efficiency. Please keep in mind that once you’ve created a table, you won’t be able to change the sort key.
To increase join performance between tables the distribution key field and type of distribution key needs to be defined before creating a table. Once you’ve created a table, you can’t change the distribution key. The join performance will improve as a result of this.
You might be interested in reading this: Breezing through data migration for a Big Data Pipeline
Cloud-native monitoring tools can be used to keep an eye on the environment. There are numerous monitoring tools from which you can choose and employ. They aid in the discovery of performance insights and ensure optimal use of the cloud environment.
Redshift migration is supported by Amazon Web Services, which makes it dependable. Even after the migration is complete, you can contact them with any queries.
As more businesses migrate to cloud data warehouses, they’re discovering the need to optimize in order to get the most out of their ETL operations. Amazon Redshift has the speed, performance, and scalability you need to keep up with the exponential growth in data quantities you’re seeing. To get the most out of Amazon Redshift and ETL, look over the following Amazon Redshift best practices.
Make your workload management more efficient
Workload management queues in Amazon Redshift allow you to build various queues for different workloads and manage the execution times of queries. You can construct different queues for ETL, reporting, and superusers using the workload management (WLM) tool. Amazon suggests keeping the WLM tool’s parallelism to around 15, which can help keep your ETL execution times consistent.
Make an effort to strengthen your COPY skills
The different compute nodes divide the effort of ingesting data due to the way massively parallel processing (MPP) databases are configured. Depending on the cluster’s node type, each node contains a specific number of slices. It’s recommended to COPY from numerous files of equivalent size rather than a single huge source file to assist split the work of loading data evenly across the slices. When copying from a single file or files of radically different sizes, some slices are required to perform more work than others, slowing down the entire copy process.
Reduce the amount of commits in your ETL operations as much as possible
ETL jobs usually entail a number of phases. Because commits are resource-intensive, executing one after each step can slow down your job. You can perform a single commit after all of the steps of your ETL operation if you surround them with a BEGIN…END statement.
Make a plan for data loading
Using Amazon Simple Storage Service, you can load data from numerous source systems into Amazon Redshift (Amazon S3). Amazon S3 is a cloud-based object storage service provided by Amazon Web Services.
Loads from many sources will run more smoothly if you use a manifest file. A manifest is a JSON-formatted text file that lists the URLs of all files uploaded to Amazon S3. Temporary tables can also be used in a manifest if you need to conduct small transformations before loading.
Use Amazon Redshift Spectrum to your advantage
Amazon Redshift Spectrum is an Amazon Redshift data warehouse functionality that allows you to conduct SQL queries on data stored in Amazon S3. You can use Amazon Redshift Spectrum to do analytics on data that isn’t stored in the Amazon Redshift database.
Spectrum is well-suited to handle spikes in your data storage needs, which can slow down ETL processing times, particularly when staging data on Amazon S3. You can use Amazon Redshift Spectrum to query the data as if it were in Redshift.
Adjust the size of your cluster as appropriate
If you’ve evaluated the health of your queries and they’re still not performing properly, it’s conceivable that the problem is due to a lack of resources. The advantage of cloud data warehouses is that they can easily be scaled up to provide more computer power. Consider scaling up your cluster to meet your response time needs with a more powerful cluster. You can scale to respond to more and more data as volumes expand, allowing you to take advantage of pay-as-you-go cloud economics.
It’s also possible to reduce the size of the project
Scaling works in both directions, and scaling down is a terrific method to save money. You can reduce the size of an existing cluster so that it uses fewer resources and costs less. It’s possible that your database developers will perform this at night or on weekends when traffic is lighter.
Use change data collecting to your advantage
Change data capture (CDC) is a mechanism for identifying and capturing changes made to your source databases before replicating them on Redshift. The COPY function in Amazon Redshift is used to move data into Redshift; however it can be resource-intensive, especially when importing data that has already been loaded. The quantity of data you move around with CDC methods is reduced, which greatly increases the speed of your ETL programme.