February 13, 2007

QlikTech – flexible, memory-centric, columnar BI

QlikTech has a pretty interesting story, and a number of customers seem to agree. Their flagship product QlikView is a BI suite that runs off an in-memory copy of the data. Specifically, that copy is logically relational and physically columnar. In an important feature, QlikView is happy to import data from multiple sources at once, such as a warehouse plus an operational data store.

So the QlikTech pitch is essentially “Buy our stuff, and you can start doing BI immediately, running any queries and reports you want to. No reason to limit your queries to any kind of dimensional model. No need to prepare the data.” More precisely, QlikTech claims to do away with some kinds of data preparation; obviously, cleaning and so on might still be necessary. Indeed, they describe their classic use case as being the combination of data partly from an operational store and partly from a pre-existing warehouse.

QlikTech’s story is consistent with a variety of trends I see generally underway. The memory-centric aspect is shared with SAP and Applix. In particular, their technical story is very similar to SAP’s BI Accelerator, although I agree with QlikTech’s estimate that QlikView has close to 100X the customers BI Accelerator does. The columnar story is also on the rise in VLDB-land, as exemplified by Vertica and Kognitio. And the “leave the data in place” story is similar to what fellow Scandinavian FAST has been talking about lately.

What I haven’t done is looked into the actual QlikView BI suite, or how it’s used. Stay tuned.


One Response to “QlikTech – flexible, memory-centric, columnar BI”

  1. Scott on March 3rd, 2007 8:12 am

    QlikView is in-memory, but there’s no comparison between QlikView and SAP’s BI Accelerator, except for the fact that both technologies have the word “memory” in them.

    The differentiator for QlikView is in the associative, highly compressed data model that it forms when the built in ETL process happens. There’s no cubes or universes,or any other data plumbing. No fixed drill down paths. If the data you pull in,(from ODC or OLEDB structured DBs, or XML, or Excel spreadsheets, or charachter delimited files, or tables in a web page, or well, you get the idea) has 63 dimensions in it, they’re all available for drill down starting anywhere, ending anywhere, at the users’ whim.

    If you avoid all the data plumbing infrastructure required by legacy OLAP and yet provide a far more easier, faster, more flexible and value BI application to end users, folks will beat a path to your door. They are today, at the rate of 10+ new companies a business day.

    Think “in-memory” is limiting from a data volume standpoint? Think again. Not when coupled with an associative data model that highly compresses that data without losing any detail. In fact, most often with QlikView you’ll keep the transactional level of detail that you would have to give up in the summarized, aggregated world of traditional OLAP. Gartner says, in their just released magic quadrant, “QlikTech is able to show the most references analyzing hundreds of millions of rows of data with good query performance.”

    As for anyone trying out QlikView themselves, you can here:



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