This page provides you with instructions on how to extract data from Db2 and analyze it in Google Data Studio. (If the mechanics of extracting data from Db2 seem too complex or difficult to maintain, 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 Data Studio?
Google Data Studio is a simple dashboard and reporting tool. It's free and easy to use, but it lacks the sophisticated features of higher-end reporting software. Many of the connectors it supports are for Google products, but third parties have written partner connectors to a wide variety of data sources. Its drag-and-drop report editor lets users create about 15 types of charts.
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 Data Studio
Google Data Studio uses what it calls "connectors" to gain access to data. Data Studio comes bundled with 17 connectors, mostly to pull in data from other Google products. It also supports connectors to MySQL and PostgreSQL databases, and offers 200 connectors to other data sources built and supported by partners.
Using data in Google Data Studio
Google Data Studio provides a graphical canvas onto which users drag and drop datasets. Users can set dimensions and metrics, specify sorting and filtering, and tailor the way reports and charts are displayed.
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.
From Db2 to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Db2 data in Google Data Studio is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Db2 to Redshift, Db2 to BigQuery, Db2 to Azure Synapse Analytics, Db2 to PostgreSQL, Db2 to Panoply, and Db2 to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Db2 with Google Data Studio. 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 a data warehouse that can be easily accessed and analyzed by Google Data Studio.