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I'm quite new to BigData architecture so please don't be to harsh on me.

I am trying to figure out the best alternative to build a BI Architecture able to deal with huge amounts of data. As I see it, the solution has to be clustered/horizontally scalable to cope with system growing. I would like to be able to interact with the system using SQL, so HBase + Hive (or even Pig, not for sql but not to need to manually write MR tasks) could be a solution. What would be the benefits/disadvantages of such an architecture opposed to, for instance, Exasolution and their In-Memory - MPP - Columnar solution.

Are there other alternatives which might have some extra-benefits? What about maintenance and configuration? Any Microsoft solution (I may find customer specific needs regarding this)

Sorry for posting such an open question, but I would like to see some discussion so that I can learn from you as much as possible.

Brian Tompsett - 汤莱恩
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aluncob
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Though being an EXASOL guy, I will not start to try to convince you that EXASOL is the one and only good solution out there. It heavily depends on the use case you are trying to implement, and the requirements you have to fulfill.

Hadoop is a very flexible, scalable system and used very often for storing and processing huge volumes of data.

EXASOL in contrast is a specialized RDBMS for complex analytic query processing.

I think that these two options don't really directly compete but complement each other. In many cases companies need a scalable data lake to store and preprocess there data, or to query it in rather simply ways. Once you want to enter the real-time business with complex analytics, where dozens, hundreds or even thousands of analysts are running lots of queries, then an in-memory RDBMS is a great choice.

King, the producer of Candy Crush, combines these two worlds to a powerful data management eco system. They store petabytes of data within Hadoop and use EXASOL on top as an in-memory layer for hundreds of terabytes of data. You can read more about that exciting use case here: http://bit.ly/1TR8APY

Another important difference of these two worlds is the complexity. While EXASOL is tuning-free because it is a specialized system (similar to an appliance) for a certain use case running SQL queries or R/Python/Java in-database-analytics, the Hadoop stack is much more complex. You'll need a certain level of know how to setup, maintain and tune this system. This doesn't need to be a reason for any of the two option. As mentioned, it heavily depends on what you want.

From a price perspective, Hadoop is free and so it should be much cheaper than an in-memory db such as EXASOL, right? Wait a minute, it's not that easy. Again, you have to consider the whole picture. How much data you really want to store, how much of that needs to be queried for analysis, how much hardware would you need to buy, how many people do you have to be hired and trained for the operation or the analytics deployed on the system.

Summary

To summarize my thoughts, the world is too complicated to directly compare these two technologies. Depending on the use case and your personal requirements, either one or the other could be the better option. And in my opinion, the trend in the market is combining such systems to a data mgmt eco systems where you get the best out of the two worlds... Actually three worlds, because the world of operational data processing of NoSQL solutions should also be mentioned here.

I hope that helped a bit. If you need any further details especially about EXASOL, don't hesitate to contact me or connect with me on LinkedIn: de.linkedin.com/in/exagolo

Mathias Golombek
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