Comparing and Contrasting Kx and the Hadoop Ecosystem

1 Nov 2016 | , , , , ,
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“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.


Kx Product Insights: Inter-Trading Alert

5 Dec 2018 | , , ,

by Aidan O’Neill Kx has a broad list of products and solutions built on the time-series database platform kdb+ that capitalize on its high-performance capabilities when analyzing very large datasets. Kx for Surveillance is a robust platform widely used by financial institutions for monitoring trades for regulatory compliance. The Surveillance platform instantly detects known trading […]

Kx extends relationship with NASA Frontier Development Lab and the SETI Institute

The Exploration of Space Weather at NASA FDL with kdb+

4 Dec 2018 | , , , ,

Our society is dependent on GNSS services for navigation in everyday life, so it is critically important to know when signal disruptions might occur. Physical models have struggled to predict astronomic scintillation events. One method for making predictions is to use machine learning (ML) techniques. This article describes how kdb+ and embedPy were used in the ML application.