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Hadoop Review
May 16, 2018

Hadoop Review

Kartik Chavan | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User

Modules Used

  • Hadoop Distributed File System
  • Hadoop MapReduce

Overall Satisfaction with Hadoop

It is massively being used in our organization for data storage, data backup, and machine learning analytics. Managing vast amounts of data has become quite easy since the arrival of the Hadoop environment. Our department is on verge of moving towards Spark instead of MapReduce, but for now, Hadoop is being used extensively for MapReduce purposes.
  • Hadoop Distributed Systems is reliable.
  • High scalability
  • Open Sources, Low Cost, Large Communities
  • Compatibility with Windows Systems
  • Security needs more focus
  • Hadoop lack in real time processing
  • Positive impact as this is the future. Abundance of tools
  • Return on Investment is high, as Big Data helps make better decisions
  • Hadoop has made it possible to implement projects that require large amounts of data from a diverse set of source systems.
  • For real-time streaming, use Spark; can provide a stark contrast to the way MR works
  • Hadoop offers a scalable, cost-effective and highly available solution for big data storage and processing.
  • Amazon Redshift is somewhat closer to Hadoop. But to analyze Petabytes of data Hadoop as better performance.
  • Hadoop is being open source, is cheaper to use and do POCs for client

Hadoop helps us tackle our problem of maintaining and processing a huge amount of data efficiently. High availability, scalability and cost efficiency are the main considerations for implementing Hadoop as one of the core solutions in our big-data infrastructure. 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 data points.