- AWS REDSHIFT POSTGRES HOW TO
- AWS REDSHIFT POSTGRES UPDATE
- AWS REDSHIFT POSTGRES SOFTWARE
- AWS REDSHIFT POSTGRES CODE
AWS REDSHIFT POSTGRES HOW TO
This blog will demonstrate how to use the Data Migration Service to move data from Amazon RDS PostgreSQL to Amazon Redshift.
AWS REDSHIFT POSTGRES SOFTWARE
These cloud computing web services provide distributed computing processing capacity and software tools via AWS server farms.
AWS REDSHIFT POSTGRES UPDATE
This probably won't be the first or the last time you need to insert and update data into a table, and it's nice to have options for how best to accomplish it.For those unfamiliar with Amazon Web Services (AWS), is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals and companies, on a metered pay-as-you-go basis. I hope you find it helpful to see how to create your own SQL UPSERT function. Not too shabby.Ĭheck back in a few weeks - I’ll be writing more about JavaScript, React, IoT, or something else related to web development. Lucky for me, creating UPSERT functionality wasn't as difficult as I thought it would be with the help of a temporary table and a couple of targeted DELETE and INSERT queries.
Since I needed to update a data table with new information and update existing data in that table, I needed something more than a simple SQL INSERT. ConclusionĪmazon Redshift is a popular, powerful data warehouse, but even though it's based on PostgreSQL, it lacks some of the nicer features like UPSERT, which is exactly the feature I needed to help out one of the teams at work. Delete the temporary table now that its job is done DROP TABLE temp_users Ĭreating our own UPSERT's not so bad once each of the steps are broken down and explained. In USER_UPDATES, I'll modify some existing data in the table (updates), and create some new data in the table (inserts), then we'll get to updating that newly modified data into the USERS table. It sounds complex (because it is a bit), but it's not as bad once each step in the process is broken down.įor this article, I'll set up a table (USER_UPDATES) that's a copy of a fictitious USERS table. There's more than one way to recreate UPSERT but the option I chose to go with essentially deletes all the existing rows in the production table that match rows in the temporary table, then inserts all the data from the temporary table into the production table. If there's no native UPSERT, we'll make our own. If you wish to reference any of the commands in the article, I recommend checking the Redshift SQL command docs. NOTE: This tutorial assumes you're familiar with basic SQL syntax.
AWS REDSHIFT POSTGRES CODE
So after I figured it out, I wanted to share it, with code examples, for each step. This sounds sensible, but the AWS documentation on how exactly to do this is a little sparse. No built in UPSERT functionalityĪmazon acknowledges this shortcoming readily enough, and suggests using a staging table to perform a merge. It's similar to Google's BigQuery or Microsoft's Azure cloud data warehouses, if you've ever used either of those in the past.Īnd although Redshift is based on PostgreSQL it does have some differences, one of those being: a lack of UPSERT functionality. Redshift is Amazon Web Service's PostgreSQL-based cloud data warehouse built with added functionality to manage very large datasets and support high-performance analysis and reporting of those data. Let's look at how to create our own version of an UPSERT SQL statement in a PostgreSQL-based Amazon Redshift database, updating data that already exists and adding new data as well. With some research and a lot of testing, I learned that by using a temporary data table and a few SQL statements strung together it was possible, and it wasn't as complicated as I thought it would be. Normally, I'd use an UPSERT SQL statement to do both things at once, but Redshift doesn't support UPSERT statements, so I had to get a little more creative to make this happen manually. While this would normally be a relatively straightforward INSERT SQL request if all the data was brand new, one thing that made it a little tricky was that I needed to update certain rows in the data table AND insert new rows as well. A few months back, the marketing team was working on improving our analytics reporting and wanted to store data in our Amazon Redshift database that could then be used to build more detailed reports. One of the many things I enjoy about working at a startup is the variety of people I get to work with across the company and the interesting problems I get to solve.
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