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Time Series Databases: New Ways to Store and Access Data by Ellen Friedman, Ted Dunning

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Chapter 2. A New World for Time Series Databases

As we saw with the old ship’s logs described in Chapter 1, time series data—tracking events or repeated measurements as a function of time—is an old idea, but one that’s now an old idea in a new world. One big change is a much larger scale for traditional types of data. Differences in the way global business and transportation are done, as well as the appearance of new sources of data, have worked together to explode the volume of data being generated. It’s not uncommon to have to deal with petabytes of data, even when carrying out traditional types of analysis and reporting. As a result, it has become harder to do the same things you used to do.

In addition to keeping up with traditional activities, you may also find yourself exposed to the lure of finding new insights through novel ways of doing data exploration and analytics, some of which need to use unstructured or semi-structured formats. One cause of the explosion in the availability of time series data is the widespread increase in reporting from sensors. You have no doubt heard the term Internet of Things (IoT), which refers to a proliferation of sensor data resulting in wide arrays of machines that report back to servers or communicate directly with each other. This mass of data offers great potential value if it is explored in clever ways.

How can you keep up with what you normally do and plus expand into new insights? Working with time series data is obviously less laborious ...

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