This page provides you with instructions on how to extract data from PostgreSQL and load it into Redshift. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is PostgreSQL?
PostgreSQL, also called Postgres, is an open source object-relational database management system that runs on all major operating systems. It's known for its stability and its ability to handle high volumes of transactions.
What is Redshift?
When it was released in 2013, Amazon Redshift was the first cloud data warehouse. It uses defined schemas, columnar data storage, and massively parallel processing (MPP) architecture to provide a base for analytics reporting.
Getting data out of PostgreSQL
Most people retrieve data from relational databases by writing SQL queries. If you're just looking to export data in bulk, however, you can use the command-line tool
pg_dump to export data from a PostgreSQL database as a CSV file or a script that you can run to restore the database on any PostgreSQL server.
Loading data into Redshift
Once you've identified all the columns you want to insert, you can use the CREATE TABLE statement in Reshift to set up a table to receive your data.
With the table built, you might think that the easiest way to migrate your data (especially if there isn't much of it) would be to build INSERT statements to add data to your Redshift table row by row. Think again! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, we suggest moving the data into Amazon S3 and then using the COPY command to load it into Redshift.
Keeping PostgreSQL data up to date
The script you have now should satisfy all your data needs for PostgreSQL – right? Not yet. How do you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow; if latency is important to you, it's not a viable option.
Instead, you can identify some key fields that your script can use to bookmark its progression through the data, and pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in PostgreSQL.
Other data warehouse options
Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure SQL Data Warehouse, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To BigQuery, To Postgres, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.
Easier and faster alternatives
If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.
Thankfully, products like Stitch were built to move data from PostgreSQL to Redshift automatically. With just a few clicks, Stitch starts extracting your PostgreSQL data, structuring it in a way that's optimized for analysis, and inserting that data into your Redshift data warehouse.