Comparing and Contrasting Kx and the Hadoop Ecosystem

1 Nov 2016 | , , , , ,
Share on:

“Through 2018, 70% of Hadoop deployments will fail to meet cost savings and revenue generation objectives due to skills and integration challenges.”

Gartner, 100 Data and Analytics Predictions Through 2020



By Glenn Wright

The exponential increase in available digitized data, or Big Data, is transforming business and research. The appreciation of the potential of Big Data to change how companies operate has tracked the rise of the Apache Hadoop ecosystem, which includes open-source computing frameworks for working with large datasets.

Over the past two decades companies in the financial services industry working with extremely large datasets have turned to the Kx platform, a high-performance time-series database called kdb+ with a built-in programming language called q for high performance analytics. Kx predates the Apache Hadoop ecosystem by decades, and Kx is proven to be more performant, especially as data volumes increase.

In my latest whitepaper I discuss of some of the differences between the two approaches for tackling large-scale, complex business analytics. I highlight the advantages of both systems and inspect some of the key architectural differences between the two. While both approaches have merits, it is only when you are required to deploy the tools for complex analytics that the true merits of the Kx approach can be fully realized.


Kdb+ Transitive Comparisons

6 Jun 2018 | , ,

By Hugh Hyndman, Director, Industrial IoT Solutions. A direct comparison of the performance of kdb+ against InfluxData and, by transitivity, against Cassandra, ElasticSearch, MongoDB, and OpenTSDB

Kx provides rapid access to unstructured data

Insights into kdb+ 3.6

5 Jun 2018 | , ,

At Kx25, the international kdb+ user group conference held on May 18th, we released kdb+ version 3.6. With this release, we are opening up new possibilities for kdb+ application developers.
It provides a lot of new capabilities and streamlines previously complex processes so that programmers will be able to take a simpler approach to writing systems compared to what they did in the past.