Hadoop an awesome tool for large scale batch processing.
Overall Satisfaction with Hadoop
I have been working with Hadoop since last year. It is very user friendly. Hadoop was used by the data center management team. It allows distributed processing of huge amount of data sets across clusters of computers using simple programming models.
Pros
- It is robust in the sense that any big data applications will continue to run even when individual servers fail.
- Enormous data can be easily sorted.
Cons
- It can be improved in terms of security.
- Since it is open source, stability issues must be improved.
- Apache Spark and Apache Flink
Apache Spark has an in memory processing model, making it powerful for lightning fast data processing. Apache Spark also exposes Scala and Python in APIs which is one of the most commonly used programming languages in data analytic and data processing domains.
Evaluating Hadoop and Competitors
- Product Features
- Product Usability
I used hadoop and found it really useful while working with bigger data sets. I used Hadoop for my project to get insight of different patterns from given data set. It was easy and user friendly.
I'll be looking at scalability, reliability. At the same time it will be good to have small learning curve.
Using Hadoop
Pros | Cons |
---|---|
Like to use Easy to use Well integrated Quick to learn Feel confident using | Unnecessarily complex Requires technical support Inconsistent Cumbersome Lots to learn |
- Processing huge data sets with good performance
- Distributed data handling with multiple nodes
- Small Learning curve
- Using Hdoop is a heavy weight process
- Installation is a little tricky for newbees
- Not suitable for dynamic data sets
Yes, but I don't use it
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