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Popular frameworks for big data processing in Java

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The big data challenge The concept of big data is understood differently in the variety of domains where companies face the need to deal with increasing volumes of data. In most of these scenarios the system under consideration needs to be designed in such a way so that it is capable of processing that data without sacrificing throughput as data ...

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Big data isn’t – well, almost

Back in ancient history (2004) Google’s Jeff Dean & Sanjay Ghemawat presented their innovative idea for dealing with huge data sets – a novel idea called MapReduce Jeff and Sanjay presented that a typical cluster was made of 100s to few 1000s of machines with 2 CPUs and 2-4 GB RAM each. They presented that in the whole of Aug ...

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Spark Microservices

As continuation of big data query system blog, i want to share more techniques for building Analytics engine. Take a problem where you have to build system that will be used for analyzing customer data at scale.           What options are available to solve this problem ? – Load the data in your favorite database and have ...

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The Apache Ignite Native persistence, a brief overview

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In-memory approaches can achieve blazing speed by putting the working set of the data into the system memory. When all data is kept in memory, the need to deal with issues arising from the use of traditional spinning disks disappears. This means, for instance, there is no need to maintain additional cache copies of data and manage synchronization between them. ...

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Handling the Extremes: Scaling and Streaming in Finance

Editor’s Note: At Strata+Hadoop World 2016 in New York, MapR Director of Enterprise Strategy & Architecture Jim Scott gave a presentation on “Handling the Extremes: Scaling and Streaming in Finance.” As Jim explains, agility is king in the world of finance, and a message-driven architecture is a mechanism for building and managing discrete business functionality to enable agility. In order to ...

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Top 10 Big Data Trends in 2017 for Financial Services

In the past year, the big data pendulum for financial services has officially swung from passing fad or experiment to large deployments. That puts a somewhat different slant on big data trends when compared to 2016 trends. The question of big data hype versus reality has finally been put to rest for banks. This is further supported by the upward ...

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