As enterprises seek to take advantage of ever-larger stores and sources of information, data analytics continues to take on greater importance. Modern CIOs are tasked with determining how best to capitalize on an organization's data to improve decision-making, fine-tune marketing programs, respond to changes in customer demand and seize new business opportunities.
But before technology managers can put big data to strategic use, they must first make some tactical decisions around IT infrastructure. One decision involves how big data is stored: Hadoop clusters versus traditional network-attached storage and storage-area network installations, for instance. The process of collecting, processing and analyzing large data sets also calls for a lot of processing power. Enter framework offerings like Hadoop's Apache Spark. NoSQL databases, in-memory analytics tools, distributed computing systems and converged infrastructure appliances rank among the other considerations when building a technical architecture to support a data analytics strategy.
1Get acquainted with the processing powerhouse
You can't talk about data analytics without also talking about Hadoop; it's a crucial processing technology that has changed the analytics game and become an integral part of IT ecosystems. In this section, get acquainted with the evolution of Hadoop and ponder next steps.
2How the right infrastructure can make or break your big data projects
Of course, any good data analytics strategy needs a solid foundation to build upon. IT professionals are consistently challenged with new choices on hardware, storage and other aspects of data center infrastructure. In this section, explore these choices and get tips on how to overcome infrastructure hurdles.
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3Learn how organizations are using analytics to their advantage
How are other IT leaders harnessing the power of data analytics? In this section, see enterprise data analytics strategies in action and find the approach that best fits your organization's IT and business goals.