How Apache Hadoop Helps in Solving Big Data Problems

As more organizations began to apply Hadoop and contribute to its development, word spread about the efficiency of this tool that can manage raw data efficiently and cost-effectively. The fact that Hadoop was able to carry out what seemed to be an imaginary task; its popularity grew widely. The open source nature of Hadoop allows it to run on multiple servers. As the quality of the tool improved over time, it became able to perform robust analytical data management and analysis tasks. The ability of Hadoop to analyze data from a variety of sources is particularly notable. Certain core components are behind the ability of Hadoop to capture as well as manage and process data. These core components are surrounded by frameworks that ensure the efficiency of the core components. The core components of Hadoop include the Hadoop Distributed File System (HDFS), YARN, MapReduce, Hadoop Common, and Hadoop Ozone and Hadoop Submarine. These components influence the activities of Hadoop tools as necessary.

Applications run concurrently on the Hadoop framework; the YARN component is in charge of ensuring that resources are appropriately distributed to running applications. This component of the Hadoop framework is also responsible for creating the schedule of jobs that run concurrently. The MapReduce component of Hadoop tools directs the order of batch applications. This component is in charge of the parallel execution of batch applications. The Hadoop Common component of Hadoop tools serves as a resource that is utilized by the other components of the framework. Hadoop Ozone is a component that provides the technology that drives object store, while Hadoop Submarine is the component that drives machine learning. Hadoop Submarine and Hadoop Ozone are some of the newest technologies that are components of Hadoop.

Hadoop processes a large volume of data with its different components performing their roles within an environment that provides the supporting structure. Apart from the components mentioned above, one also has access to certain other tools as part of their Hadoop stack. These tools include the database management system, Apache HBase, and tools for data management and application execution. Development, management, and execution tools could also be part of a Hadoop stack. The tools typically applied by an organization on the Hadoop framework are dependent on the needs of the organization.

For big data and analytics, Hadoop is a life saver. Data gathered about people, processes, objects, tools, etc. is useful only when meaningful patterns emerge that, in-turn, result in better decisions. Hadoop helps overcome the challenge of the vastness of big data:

1.) Hadoop clusters scale horizontally

More storage and compute power can be achieved by adding more nodes to a Hadoop cluster. This eliminates the need to buy more and more powerful and expensive hardware.

2.) Hadoop can handle unstructured / semi-structured data

Hadoop doesn't enforce a 'schema' on the data it stores. It can handle arbitrary text and binary data. So Hadoop can 'digest' any unstructured data easily.

3.) Hadoop clusters provides storage and computing

We saw how having separate storage and processing clusters is not the best fit for Big Data. Hadoop clusters provide storage and distributed computing all in one.

4.) Storage

With the help of HDFS, Big Data can now be stored in a proper distributed manner. For that, data node block is used, it’s an efficient storage solution and allows you to specify the size of every block in use. Additionally, it doesn’t only divides the data across different blocks but also replicated all the blocks on the data nodes, thus making way for better storage solution.

5.) The speed of accessing and processing data

Instead of relying on traditional methodologies, Hadoop prefers moving processing to the data, which means the processing dynamo is moved across different slave nodes and parallel processing of data is carried on throughout the slave nodes. And the processed results are then shifted into a master node, where a mixing of data takes place and the response arising out of it is sent to the client. 

6.) Resilience 

Data stored in any node is also replicated in other nodes of the cluster. This ensures fault tolerance. If one node goes down, there is always a backup of the data available in the cluster.

7.) Scalability

Unlike traditional systems that have a limitation on data storage, Hadoop is scalable because it operates in a distributed environment. As the need arises, the setup can be easily expanded to include more servers that can store up to multiple petabytes of data.

8.) Low cost 

As Hadoop is an open-source framework, with no license to be procured, the costs are significantly lower compared to relational database systems. The use of inexpensive commodity hardware also works in its favor to keep the solution economical.

9.) Speed 

Hadoop’s distributed file system, concurrent processing, and the MapReduce model enable running complex queries in a matter of seconds.

10.) Data diversity 

HDFS has the capability to store different data formats such as unstructured (e.g. videos), semi-structured (e.g. XML files), and structured. While storing data, it is not required to validate against a predefined schema. Rather, the data can be dumped in any format. Later, when retrieved, data is parsed and fitted into any schema as needed. This gives the flexibility to derive different insights using the same data.