Organizations today are seeking to drive deep analysis, detect patterns, and find anomalies across terabytes or petabytes of raw big data. Whether you’re trying to discover the root cause of the latest customer churn or the hidden costs that are eroding the bottom line, you need analytic tools and techniques that work well with unstructured and multi-structured data in its original raw form.
Apache Hadoop is maturing as a loosely coupled stack for inexpensive batch storage, where you don't need to know data formats or schemas to store and process the data. Hadoop can store an abundance of data and has the potential to serve a variety of analytic and data science applications, from e-commerce customer segmentation and A/B testing to fraud detection, machine learning, and medical research. But trying to explore, analyze, and visualize data in Hadoop has often meant significant work manually writing jobs or setting up predefined schemas, which takes time and keeps vital data out of the reach of business and IT. New tools and analytic platforms allow even nontechnical users to explore and understand massive data sets. They are enabling organizations to get up and going to explore, analyze, and visualize unstructured data in Hadoop in hours instead of weeks and months.
You Will Learn
Individual, Student, & Team memberships available.