This page provides you with instructions on how to extract data from Postgres and load it into Google BigQuery. (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 Postgres?
Postgres, also called PostgreSQL, is an open source object-relational database management system that runs on all major operating systems.
What is Google BigQuery?
Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With all of that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.
Getting data out of Postgres
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 Google BigQuery
Google Cloud Platform offers a helpful guide for loading data into BigQuery. You can use the
bq command-line tool to upload the files to your awaiting datasets, adding the correct schema and data type information along the way. The
bq load command is your friend here. You can find the syntax in the bq command-line tool quickstart guide. Iterate through this process as many times as it takes to load all of your tables into BigQuery.
Keeping Postgres data up to date
At this point you’ve coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. Now you can set up a cron job or continuous loop to keep pulling new data as it appears. But as with any code, once you write it, you have to maintain it, and you’ll be responsible for modifying it as users’ needs change.
Other data warehouse options
BigQuery is really great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Postgres or Redshift, which are two RDBMSes that use similar SQL syntax. If you're interested in seeing the relevant steps for loading this data into Postgres or Redshift, check out To Redshift and To Postgres.
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 solve this problem automatically. With just a few clicks, Stitch starts extracting your Postgres data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Google BigQuery data warehouse.