Every data scientist must familiar with how to extract data for analysis, and Hadoop is the technology that enables data scientists to work with massive volumes of data. Nobody agrees on a single definition of what abilities a data scientist should possess because there are so many different meanings. According to some, a data scientist should be able to handle data using Hadoop and do effective statistical analyses on the data collection. When data volume surpasses system memory or when business demands that data be dispersed over numerous servers, Hadoop is a crucial tool for data science. In these situations, Hadoop helps data scientists by enabling them to more quickly move data between system nodes.
A platform for storing and processing data is called Apache Hadoop. The amount of data coming from social media platforms like Facebook, Twitter, and ecommerce sites has grown significantly over the past few years. These data cannot be processed or stored by our conventional system, the RDBMS.
Hadoop has the following advantages:
1) It stores data in its current form, both structured and unstructured.
2) It is fault tolerant since any node failure is automatically recovered.
3) It handles complex data quickly and easily.
4) It operates via distributed processing, which implies that many tasks will be carried out concurrently in parallel.
5) Hadoop provides an economical data storage option.
6) In spite of computer failure, data is securely kept on a cluster of machines.
Below are some benefits of utilising Apache Hadoop:
- The capacity to handle and store vast volumes of data: Huge amounts of data can be stored in the HDFS layer.
- Data processing speed thanks to Hadoop’s distributed computing model.
- Error Tolerant Jobs are immediately sent to other nodes whenever a node goes down to ensure that distributed computing does not fail.
- Additionally, data is copied to achieve fault tolerance.
- Low cost: To store massive amounts of data, the open-source framework uses inexpensive hardware, which is also free.
- Scalability: A cluster can quickly expand to accommodate more data by adding nodes.
- not constrained by any one Schema.
- It is quick because reading and computing data are done in parallel.
- Hadoop is more effective and quick than traditional computing since computation is moved to data.