Data Analysis and In Memory Technologies, let’s connect the dots:


SPEED is one of the important aspect of Data Analysis. Wouldn’t it be great if you query a data source, you get your answers as soon as possible? Yes? Right! Of course, it depends on factors like the size of the data you are trying to query but wouldn’t it be great if it’s at “SPEED OF THOUGHT“?

So Here’s the Problem:

Databases are mostly disk based and so the bottleneck here is the speed at which can get access to data off the disks.

So what can you do?

Let’s put data in a RAM (memory) because data-access via memory is faster.

If it’s sounds so easy, why didn’t people do it earlier? And why are we talking about “In Memory” NOW?

1) BIGGER Data Size/sets and so today with more data, it takes more time to query data from databases. And so researchers have looked at other approaches. One of the effective approach they found is: In-memory

(And I am not ignoring the advances in Database Technologies like Parallel databases, But for the purpose of understanding “Why In-memory”, it’s important to realize the growing size of data sets and a viable alternative we have to tackle the problem: In memory. And also I am not saying that it’s the ONLY way to go. I am just trying to understand the significance of in-memory technologies. We, as data professionals, have lot’s of choices! And only after evaluating project requirements, we can talk about tools and techniques)

2)  PRICE of Memory: Was the price of RAM/memory higher than what it is today? So even though it was a great idea to put data in memory, it was cost-prohibitive.

So Let’s connect the dots: Data Analysis + In Memory Technologies:

What’s common between Microsoft’s PowerPivot, SAP HANA, Tableau and Qlikview?

1) Tools for Data-Analysis/Business-Intelligence 2) Their Back End data architecture is “In Memory”

So since Data Analysis needs SPEED and In-Memory Technologies solves this need – Data Analysis and Business Intelligence Tools adopted “In-memory” as their back-end data architecture. And next time, when you hear a vendor saying “in-memory”, you don’t have to get confused about what they’re trying to say. They’re just saying that we got you covered by giving you ability to query your data at “speed of thought” via our In-memory technologies so that you can go back to your (data) analysis.

That’s about it for this post. Here’s a related post: What’s the benefit of columnar databases?

your comments are very welcome!