Many systems exist today that were simply not optimised for time-series data analysis and machine learning. People may not even know the data is being collected regularly as part of day-to-day operations. This is often referred to as dark data. As the hunger for data increases, data engineers are tasked to make this dark data available.
You have too many apps on your phone already and many smart home devices each come with their own app as well. We are going to unify these notifications and create our own customised messages via a Telegram bot for our cleverHome.
Many times, you need to view the latest log entries generated by your app to check that everything is working or see what is currently broken. We will be creating a FastAPI app that will stream our latest log files entries over websockets.
Over this six plus one blog series we covered many different technologies and integrations, as we built our data pipeline. While there are standard patterns we can often use, sometimes we need to be able to flexible and use what is available.
In this part we set up our big data platform, Azure Data Explorer, and create ingestion events to continually ingest our transformed data. Finally, we add context to our data by creating a data model and exporting it to our data lake.