Hadoop for Justifying Business Decisions with Hard Data
October 24, 2017

Hadoop for Justifying Business Decisions with Hard Data

Anonymous | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Modules Used

  • Hadoop Distributed File System
  • Hadoop MapReduce

Overall Satisfaction with Hadoop

Hadoop has been an amazing development in the world of Big Data. Where relational databases fall short with regard to tuning and performance, Hadoop rises to the occasion and allows for massive customization leveraging the different tools and modules. We use Hadoop to input raw data and add layers of consolidation or analysis to make business decisions about disparate datapoints.
  • Hadoop can take loads of data quickly and performs well under load.
  • Hadoop is customizable so that nearly any business objective can be justified with the right combination of data and reports.
  • Hadoop has a lot of great resources, both informal like the community and formal like the supported modules and training.
  • Hadoop is not a relational database, but it has the ability to add modules to run sql-like queries like Impala and Hive.
  • Hadoop is open source and has many modules. It can be difficult without context to know which modules to leverage.
  • Hadoop has an amazing potential for ROI if implemented properly to justify business decisions.
  • Hadoop can allow groups to understand how their work is impacting performance at a high level, such as page clicks, where people are spending their time, how users are engaging with the application, etc.
I haven't worked with other Big Data aggregation services like Hadoop. As far as I know, Hadoop is the leading choice in this field with good cause. There is a lot of community support, custom modules, paid consultants, free and paid training. All this makes it an ideal choice for facilitating Big Data aggregation.
Hadoop is well suited for organizations with a lot of data, trying to justify business decisions with data-driven KPIs and milestones. This tool is best utilized by engineers with data modeling experience and a high-level understanding of how the different data points can be used and correlated. It will be challenging for people with limited knowledge of the business and how data points are created.