Big Data News Weekly - Issue 1

Big Data News Weekly - Issue 1

Big Data News Weekly

Issue 1


Data science is related to big data in the sense that it is an interdisciplinary field that helps extract sensible information from data in various forms. This is done by means of different algorithms, scientific methods, and algorithms.

When organizations use SQL on Hadoop for their business intelligence, they often find it difficult to cope up with the growing needs of business users who expect instant answers to complex queries. Forrester estimates that more than 20% of Big Data projects fail every year. So, why do these projects fail?

Big data will give you the most significant confidence in the integrity of the data you see. Data integrity is a huge component; block chain will introduce data layer that creates a completely new set of possibilities for Artificial intelligence capabilities and insights.

Starting Apache Hadoop 3.1 and with HDP 3.0, we have a first-class support for operators and admins to be able to configure YARN clusters to schedule and use GPU resources.

Mathematics is the bedrock of any contemporary discipline of science. It is no surprise then that, almost all the techniques of modern data science (including all of the machine learning) have some deep mathematical underpinning or the other.

The 1990s and 2000s saw the rise of the relational databases for transaction processing (OLTP) as well as analytical processing (OLAP). As the volume and variety of data explodes in the 2010s, database experts are looking to parallel graph analytic database– what some are calling GOLAP data warehouses — to enable the timely extraction of insights.

Although machine learning (ML) can produce fantastic results, using it in practice is complex. Beyond the usual challenges in software development, machine learning developers face new challenges, including experiment management (tracking which parameters, code, and data went into a result);


NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence 1st Edition

The need to handle increasingly larger data volumes is one factor driving the adoption of a new class of nonrelational “NoSQL” databases. Advocates of NoSQL databases claim they can be used to build systems that are more performant, scale better, and are easier to program.

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