This page provides you with instructions on how to extract data from Db2 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 Db2?
Db2 is IBM's relational DBMS. IBM provides versions of Db2 that run on-premises, hosted by IBM, or in the cloud. The on-premises version runs on System z mainframes, System i minicomputers, and Linux, Unix, and Windows workstations.
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 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 Db2
The most common way to get data out of any relational database is to write SELECT queries. You can specifying filters and ordering, and limit results. You can also use the EXPORT command to export the data from a whole table.
Loading data into Google BigQuery
Google Cloud Platform offers a helpful guide you can follow to begin loading data into BigQuery. Use the
bq command-line tool, and in particular the
bq load command, to upload files to your datasets. The syntax is documented in the Quickstart guide for bq. You can supply the table or partition schema, or, for supported data formats, you can use schema auto-detection. Iterate through this process as many times as it takes to load all of your tables into BigQuery.
Keeping Db2 data up to date
So you've written a script to export data from Db2 and load it into your data warehouse. That should satisfy all your data needs for Db2 – 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 Db2.
Other data warehouse options
BigQuery is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, PostgreSQL, Snowflake, or Microsoft Azure Synapse Analytics, 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 Redshift, 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 Db2 to Google BigQuery automatically. With just a few clicks, Stitch starts extracting your Db2 data, structuring it in a way that's optimized for analysis, and inserting that data into your Google BigQuery data warehouse.