SQL. Three simple letters.
Ess Queue Ell.
/ˌɛs kjuː ˈɛl/.
In the data world they bind us together, yet separate us.
As the saying goes, England and America are two countries divided by the same language, and the same goes for the batch and streaming world and some elements of SQL.
The UK Government publishes a lot of its data as open feeds. One that I keep coming back to is the Environment Agency’s flood-monitoring API that gives access to an estate of sensors that provide information about data such as river levels and rainfall.
The data is well-structured and provided across three primary API endpoints. In this blog article I’m going to show you how I use Flink SQL to explore and wrangle these into the kind of form from which I am then going to build a streaming pipeline using them.
There was a useful question on the Apache Flink Slack recently about joining data in Flink SQL:
How can I join two streams of data by id in Flink, to get a combined view of the latest data?
Let’s imagine we’ve got a source of data with a nested array of multiple values. The data is from an IoT device. Each device has multiple sensors, each sensor provides a reading.
So far I’ve plotted out a bit of a map for my exploration of Apache Flink, looked at what Flink is, and run my first Flink application. Being an absolutely abysmal coder—but knowing a thing or two about SQL—I figure that Flink SQL is where my focus is going to lie (I’m also intrigued by PyFlink, but that’s for another day…).
