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TDWI Checklist Report | Eight Considerations for Utilizing Big Data Analytics with Hadoop

March 10, 2014

As companies seek to gain competitive advantage using advanced analytics, a sea change is occurring in terms of the data and the infrastructure that supports it. Several technology factors are coming together to form the fabric of an evolving analytics ecosystem. These include:

  • Big data. Companies have been dealing with increasing amounts of diverse and often high-velocity data for some time. Some of this big data is new, such as data generated from smartphones or sensors. Much of it is unstructured, including machinegenerated data (e.g., satellite images) or human-generated data (e.g., text data, social media data, or website content). Big data is putting a strain on current analytical processes.
  • Hadoop. As big data continues to get bigger, companies are seeking out new technologies to help them cope. One of these technologies is the Hadoop file system (HDFS) and the ecosystem of tools surrounding it. Hadoop is an inexpensive solution for storing and processing big data, especially semi-structured and unstructured data. It is rapidly becoming an important part of the big data ecosystem.
  • Advanced analytics. At the same time, there have been advances in analytics algorithms and analytics processing. Visualization has helped companies explore data to discover insights—even with big data. Analytics algorithms such as machine learning and predictive analytics have matured to support the distributed processing needed for big data analytics. Text analytics is helping people derive new meaning from unstructured data.

Data preparation and staging technologies are evolving to support big data. In addition, advances such as in-memory analytics and in-database analytics have accelerated analytics performance, which has helped organizations analyze data more effectively in order to compete.

As enterprises look to embrace big data and Hadoop, they have numerous questions: “How can I deal with data preparation on Hadoop?” “How does utilizing Hadoop impact visualization and other kinds of analysis?” “What kind of analytical techniques are available to analyze Hadoop data?” “How do I use Hadoop with in-memory processing?”

This Checklist Report focuses on these questions and provides information to help you explore big data analytics.

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