Integrating Data on a Shared Hadoop Platform for the Financial Sector

on Jan 12, 2018 4:42:39 PM | By Michael Shaw | Data Lake Financial Services Hadoop
Financial services firms are notorious record-keepers. They have to be—the financial/banking industry is among the most regulated in the nation. Despite the fastidious data-gathering, banks are like most organizations in that their information architecture evolved organically over time and consists of a patchwork of systems and solutions. This approach may have worked for a while, but the piecemeal setup now prevents timely insights, thwarts accurate reporting and hinders financial crime prevention efforts.
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SAP HANA & Hadoop - Is Your Data Hot or Not?

on Aug 10, 2017 8:20:00 AM | By Clarity Insights | Hadoop Big Data SAP HANA
Buzzwords such as Big Data, In-Memory, Hot Data and Cold Data inundate our world, but what do these words really mean and how does it impact your landscape?
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4 Key Takeaways from SAPPHIRE 2017

How to Solve the Challenges of HANA and Hadoop If you attended SAPPHIRE 2017, you’re probably still digesting, distilling, and sharing the vast amount of information presented. If you missed it, we’ve compiled a few of our own key takeaways and some helpful tips to share about what we’ve done to address some of the major challenges highlighted in the conference.
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Decoupling Big Data Compute and Storage

on Dec 1, 2016 3:42:07 PM | By Tripp Smith | Hadoop Big Data data architecture Analytics SQL
Once upon a time, if you wanted a multipurpose platform for analytics, SQL databases were the way to go. This was doubly true for big data, where volumes swelled beyond the capacity of a single machine to handle analytics on such a scale. Big data was simply too big to pass across the network, and data proximity was a fundamental assumption for MPP architecture. The concept of randomized distribution without consideration for the most common access paths was unthinkable.
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Machine Learning in Spark with MLLib: Beyond Word Count

on Feb 26, 2016 9:47:47 AM | By Clarity Insights | Hadoop Machine Learning Big Data
Apache Spark™ brings great improvements in speed compared to traditional Hadoop MapReduce (checkout the Terasort challenge) and one area in which it really shines is Machine Learning, especially with Hadoop. While many introductions to Apache Spark give a demonstration using the canonical word count example, let’s dive into something a little more in-depth.
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