The data integration status quo is predicated on a model of data-at-rest. The designated final destination for data-at-rest is (and, at least for the foreseeable future, will remain) the data warehouse (DW). Traditionally, data of a certain type was vectored to the DW from more or less predictable directions—viz., OLTP systems, or flat files— and at the more or less predictable velocities circumscribed by the limitations of the batch model. Thanks to big data, this is no longer the case.
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It is no secret that we are in the data age. Data comes at us from all directions, in all shapes and sizes.Incumbent vendors and startups constantly add new features, build on top of emerging open source projects, and claim to solve the next wave of challenges. Within the Hadoop ecosystem alone, there are (at least) 11 Hadoop-related open source projects. Making sense of it can be a time-consuming headache. To bring clarity and peace of mind, here are the top 5 big data predictions for 2015 and beyond.
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In 2014, we continued to watch big data enable all things "big" about data and its business analytics capabilities. We also saw the emergence (and early acceptance) of Hadoop Version 2 as a data operating platform, with cornerstones of YARN (Yet Another Resource Negotiator) and HDFS (Hadoop Distributed File System). In 2015, the mainstream adoption with enterprise data strategies and acceptance of the data lake will continue as data management and governance practices provide further clarity. The cautionary tale of 2014 to ensure business outcomes drive big data adoption, rather than the hype of previous years, will likewise continue.
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The old models of data architecture aren't enough for today's data-driven business demands. An architecture designed a decade ago, that rapidly and seamlessly moves data from production systems into data warehouses, for example, may not be capable of meeting the needs of today's real-time enterprises. Here are the 8 essential components to building a modern data architecture.
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