This page provides you with instructions on how to extract data from Amazon Aurora and analyze it in Power BI. (If the mechanics of extracting data from Amazon Aurora 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 Amazon Aurora?
Amazon Aurora is a MySQL-compatible relational database employed by organizations that are looking for better performance than they can get from MySQL at cost-effective price points. Aurora is best used as a transactional or operational database and not for analytics.
What is Power BI?
Power BI is Microsoft’s business intelligence offering. It's a powerful platform that includes capabilities for data modeling, visualization, dashboarding, and collaboration. Many enterprises that use Microsoft's other products can get easy access to Power BI and choose it for its convenience, security, and power.
With high-value use cases across analysts, IT, business users, and developers, Power BI offers a comprehensive set of functionality that has consistently landed Microsoft in Gartner's "Leaders" quadrant for Business Intelligence.
Getting data out of Amazon Aurora
Aurora provides several methods for extracting data; the one you use may depend upon your needs and skill set.
The most common way to get data out of any database is simply to write queries. SELECT queries allow you to pull the data you want. You can specifying filters and ordering, and limit results.
If you’re looking to export data in bulk, there may be an easier way. A handy command-line tool called mysqldump allows you to export entire tables and databases in a format you specify (i.e. delimited text, CSV, or SQL queries that would restore the database if run).
Preparing Amazon Aurora data
For every table in your Amazon Aurora database, you'll need a corresponding table in your destination database. Make sure you've pinpointed all of the fields that will be inserted into your destination, and determined the datatypes for each object (i.e. INTEGER, DATETIME, etc.) to make sure they are mapped properly when they get inserted into the new table.
Loading data into Power BI
You can analyze any data in Power BI, as long as that data exists in a data warehouse that's connected to your Power BI account. The most common data warehouses include Amazon Redshift, Google BigQuery, and Snowflake. Microsoft also has its own data warehousing platform called Azure SQL Data Warehouse.
Connecting these data warehouses to Power BI is relatively simple. The Get Data menu in the Power BI interface allows you to import data from a number of sources, including static files and data warehouses. You'll find each of the warehouses mentioned above among the options in the Database list. The Power BI documentation provides more details on each.
Analyzing data in Power BI
In Power BI, each table in the data warehouse you connect is known as a dataset, and the analyses conducted on these datasets are known as reports. To create a report, use Power BI’s report editor, a visual interface for building and editing reports.
The report editor guides you through several selections in the course of building a report: the visualization type, fields being used in the report, filters being applied, any formatting you wish to apply, and additional analytics you may wish to layer onto your report, such as trendlines or averages. You can explore all of the features related to analyzing and tracking data in the Power BI documentation.
Once you've created a report, Power BI lets you share it with report "consumers" in your organization.
Keeping Amazon Aurora 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. But how will 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 and resource-intensive.
Instead, identify key fields that your script can use to bookmark its progression through the data and use to 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 Aurora.
And remember, as with any code, once you write it, you have to maintain it. If Aurora sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.
From Amazon Aurora to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Amazon Aurora data in Power BI 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 Amazon Aurora to Redshift, Amazon Aurora to BigQuery, Amazon Aurora to Azure SQL Data Warehouse, Amazon Aurora to PostgreSQL, Amazon Aurora to Panoply, and Amazon Aurora to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data from Amazon Aurora to Power BI automatically. With just a few clicks, Stitch starts extracting your Amazon Aurora data via the API, 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 Power BI.