In-memory databases for IOT

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The use of memory in computing is not new. But while memory is faster than disk by an order of magnitude, it is also an order of magnitude more expensive. That has for the most part left memory relegated to acting as a caching layer, while nearly all of the data is stored on disk. However in recent years, the cost of memory has been falling, making it possible to put far larger datasets in memory for data processing tasks, rather than use it simply as a cache.
It’s not just that it is now possible to store larger datasets in memory for rapid analytics; it is also that it is highly desirable. In the era of IoT, data often streams into the data centre or the cloud – the likes of sensor data from anything from a production line to an oilrig. The faster the organization is able to spot anomalies in that data, the better the quality of predictive maintenance. In-memory technologies are helping firms see those anomalies close to, or in, real-time. Certainly much faster than storing data in a disk-based database and having to move packets of data to a cache for analytics.
It is expected of data processing to accelerate dramatically, as companies come to grips with their data challenges and move beyond more traditional data analytics in the era of IoT. In-memory databases are 10 to 100 times faster than traditional databases, depending on the exact use case. When one considers that some IoT use cases involve the collection, processing and analysis of millions of events per second, you can see why in-memory becomes so much more appealing.
There’s another big advantage with in-memory databases. Traditionally, databases have been geared toward one of two main uses: handling transactions, or enabling rapid analysis of those transactions – analytics. The I/O limitations of disk-based databases meant that those handling transactions would slow down considerably when also being asked to return the results of data queries. That’s why data was often exported from the transactional database into another platform – a data warehouse – where it could more rapidly be analyzed without impacting the performance of the system.

Renu Yadav
Assistant Professor (BCA Dept.)



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