Upserting a Large Dimension Table in AWS Redshift
Question
Allianz Financial Services (AFS) is a banking group offering end-to-end banking and financial solutions in South East Asia through its consumer banking, business banking, Islamic banking, investment finance and stock broking businesses as well as unit trust and asset administration, having served the financial community over the past five decades. AFS uses Redshift on AWS to fulfill the data warehousing needs and uses S3 as the staging area to host files.
AFS uses other services like DynamoDB, Aurora, and Amazon RDS on remote hosts to fulfill other needs.
In Redshift, There is a large dimension table that needs to be upserted.
How can this be achieved? Select 2 options.
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer : B, D.
Option A is incorrect -Amazon Redshift doesn't support a single merge statement (update or insert, also known as an upsert) to insert and update data from a single data source.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-upsert.htmlOption B is correct -Efficiently update and insert new data by loading your data into a staging table first.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-upsert.htmlOption C is incorrect -Amazon Redshift doesn't support a single merge statement (update or insert, also known as an upsert) to insert and update data from a single data source.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-upsert.htmlOption D is correct -load your data into a staging table and then join the staging table with your target table for an UPDATE statement and an INSERT statement.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-upsert.htmlRedshift is a fully-managed data warehousing service provided by AWS. It is used to store and analyze large amounts of data using SQL-based business intelligence tools. Redshift is designed to handle petabyte-scale data warehouses, making it a popular choice for large enterprises.
Upsert is a combination of two operations, i.e., insert and update, where if the record already exists, then it gets updated, and if it doesn't exist, then it gets inserted into the database. Here are the options to achieve upsert in Redshift:
A. Use UPSERT operation to perform upserts to records: Redshift provides a MERGE command that can be used to perform upserts to records. This command is also known as UPSERT. The UPSERT command can be used to either update the existing record or insert a new record, depending on whether the record already exists or not. This command is useful when dealing with large dimension tables that need to be upserted.
B. Efficiently update and insert new data by loading your data into an intermediate table first: Another option is to load the data into an intermediate table first, and then use the SQL statements to perform the upsert operation. This method involves loading the data into a temporary table and then joining it with the target table to perform the update and insert operations. This method is useful when the data being loaded is not very large and can be managed efficiently.
C. Use INSERT_UPDATE operation to perform upserts to records: The INSERT_UPDATE command is similar to the UPSERT command, but it can only be used to update or insert a single row at a time. This command is not recommended for large dimension tables that need to be upserted as it can cause performance issues.
D. Load your data into a staging table and then join the staging table with your target table for an UPDATE statement and an INSERT statement: This method involves loading the data into a staging table and then joining it with the target table to perform the update and insert operations. This method is useful when dealing with large dimension tables that need to be upserted. However, this method can be complex and time-consuming, especially when dealing with a large amount of data.
In summary, the best options to achieve upsert in Redshift are A and B. Option A provides a simple and efficient way to perform upserts to records, whereas option B provides more control and flexibility over the process, making it suitable for managing more complex scenarios.
