Processing time series data
Time series data is nothing but data recorded continuously over time. Examples of time series data could include stock prices recorded over time, IoT sensor values, which show the health of machinery over time, and more. Time series data is mostly used to analyze historic 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.
Time series data is a perfect candidate for real-time processing. The stream processing solutions that we discussed earlier in this chapter, in the Developing a stream processing solution using ASA, Azure Databricks, and Azure Event Hubs section, would perfectly work for time series data. Let's look at some of the important concepts of time series data.
Types of timestamps
The central aspect of any time series data is the time attribute. There are two types of time in time series data:
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