Processing Time Series Data

In the earlier section dedicated to windowed aggregates, you were taught that time series data is defined as a series of data over time, such as the measurements from sensors or the prices of a share. Indeed, time series data is mostly used to analyze historical trends and identify any abnormalities in data such as credit card fraud, real-time alerting, and forecasting. Time series data will always be appended heavily with very rare updates.

Note

This section primarily focuses on the Process time series data concept of the DP-203: Data Engineering on Microsoft Azure exam.

Time series data, or data processed over consistent intervals of time, often requires real-time processing applications such as financial market analysis. The stream processing solutions that you learned earlier in this chapter, in the Implementing a Streaming Use Case with Azure section, would perfectly work for time series data.

Now, you can look at some of the important concepts...