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Exploring The Pros And Cons Of Big Data Hadoop


Hadoop is specially designed to store and manage huge amounts of data otherwise known as Big Data. It possesses diverse features like easy to use, open source framework, scalability, its enhanced performance, less scope for errors & much more.

So, let’s dig into the subject and explore the pros and cons of Hadoop.

Pros Of Using Big Data Hadoop-

Hadoop is easy to use, measurable and cost-effective. Along with this, Hadoop has many uses.

  • Economical

Hadoop is a reliable and financial solution as it uses a cluster of commodity hardware to store data. Commodity hardware is an inexpensive machine framework & hence the price of adding nodes to the framework is not much higher. In Hadoop 3.0 we have only half of storage overhead as opposed to 200% in Hadoop2.x. This requires fewer machines to store data as the redundant data decreases significantly.

  • Highly Available

In Hadoop 2.x, HDFS architecture has a single active NameNode and a single Standby NameNode, so if a NameNode goes down then we have standby NameNode to count on. But Hadoop 3.0 supports multiple standby NameNode making the system even more highly available as it can continue functioning in case if two or more NameNodes crashes.

  • Open Source

Hadoop is an open source technology i.e. its source code is freely available. We can modify the source code to suit a specific requirement.

 Disadvantages of Hadoop

  • Vulnerable By Nature

Hadoop is written in Java which is a widely used programming language & there is scope of being exploited by cyber criminals which makes Hadoop vulnerable to security breaches.

  • Supports Only Batch Processing

At the core, Hadoop has a batch processing engine which is not efficient in stream processing. It cannot produce output in real-time with low latency. It only works on data which we collect and store in a file in advance before processing.

  • Iterative Processing

Hadoop cannot do iterative processing by itself. Machine learning or iterative processing has a cyclic data flow whereas Hadoop has data flowing in a chain of stages where output on one stage becomes the input of another stage.

  • Security

For security, Hadoop uses Kerberos authentication which is hard to manage. It is missing encryption at storage and network levels which are a major point of concern.

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