Apache Hadoop is a software framework for distributed processing of enormous data sets. It is maintained by the Apache Software Foundation. It is famous for its non-profit open-source software development community. The Hadoop project was first released to the masses in 2006. It was based on work by Doug Cutting and Mike Cafarella at Yahoo.
Today, hundreds of major tech companies use Hadoop as their tech stack, from IBM and Amazon to Uber and Airbnb. Reportedly, Yahoo and Facebook have the most significant Hadoop clusters in the world. Yahoo, for one, has more than 100,000 CPUs in 40,000 servers running Hadoop. The total data storage size is of 455 petabytes (455,000,000 gigabytes).
Hadoop consists of five essential features:
- Hadoop Common: The collection of standard utilities and libraries that support the other Hadoop modules. It is an essential feature as it enables companies to take note of the modules and sets they have acquired so far in their data management journey.
- Hadoop Distributed File System (HDFS): A distributed file system for processing substantial unstructured data sets, designed to improve the scalability of Hadoop clusters by running on commodity hardware.
- Hadoop YARN: Short for “Yet Another Resource Navigator”; used for job scheduling and cluster data management. It is an essential feature as with this, companies can navigate their scheduling and data management work while multitasking and prioritizing other essential things.
- Hadoop MapReduce: A programming model for processing extensive data in parallel consists of two steps. A mapping step that applies some map or function to each data element and a reduced step that combines data and reduces the size of the data set.
- Hadoop Ozone: A distributed object store designed for big data. As a company, it is equally important to store vast stacks of data so that organizations have their relevant tabs all in one place. With Hadoop Ozone, companies can be tension free about stacking and managing their data all in one place, that too with a secure network.
Pros and Cons of Hadoop Software Framework
The pros of using Hadoop include:
- Cost-effective: Hadoop is a free and open-source project platform—you do not have to pay a cent to use it. Moreover, you can modify its source code as necessary. Furthermore, Hadoop was designed to run on low-cost commodity hardware, not . Moreover, assive supercomputers, so even businesses with limited IT budgets can use it.
- Highly scalable: Undoubtedly, Hadoop is able to divide up computation among several or many machines. It’s easy to improve the speed and performance of a Hadoop system through horizontal scaling. Scalability is one of the project’s primary goals.
- Flexibility: Hadoop is designed to process many different data types, including structured, semi-structured, and unstructured data. This means that Hadoop has a wide variety of applications, from tracking real-time social media to performing fraud detection.
The cons of using Hadoop include:
- Lack of support: The downside to Hadoop being free and open-source is that you won’t have access to dedicated support and maintenance. While the Hadoop community is large and helpful, businesses who depend on Hadoop for their daily operations will likely need to use a paid “Hadoop as a service” offering.
- Limited use cases: Hadoop performs best on a small number of huge files. On the other hand, using Hadoop for smaller data sets or for a large number of small files will limit the performance gains that you see.
What do other people think about Hadoop Software Framework?
Hadoop user reviews are generally positive. For example, on the software review website TrustRadius, Hadoop has an average of 8.5 out of 10 stars, based on 244 ratings. With all that said, is Hadoop the right choice for you?
Meanwhile, data engineer Johannes Siregar gives Hadoop a score of 8 out of 10, writing:
“Scalability is one of the main reasons we decided to use Hadoop. Storage and processing power enlarges by simply adding more nodes… Using commodity hardware as a node in a Hadoop cluster can reduce cost and eliminate dependency on a particular proprietary technology.”
However, he also notes a few issues with the platform: “User and access management are still challenging to implement in Hadoop… Processing a large number of small files also becomes a problem on a very large cluster with hundreds of nodes.”
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