Big Data has become a new buzz word today. Businesses are actively using real time data of millions ofconsumers to get actionable insightsinto customer’s behavior to provide them the personalized services. Big data help businesses to leverage data to enhance the overall efficiency of their business. This allows businesses to improve the engagement and retentionof their customers with their business.
Big Data can process huge volumes of data that cannot be managed with traditional database technologies. Due to the specific nature of big data, it is stored in a distributed file system architecture, which is easy to scale and effectively process and store massive amount of data.
Hadoop and Big Data
Apache Hadoop holds a primary position in Big Data universe and is considered one of the best frameworks to implement Big Data because it is a fundamental general purpose platform for Big Data. It is open source and allows you to quicklyprocess and store huge amount of data.
It possesses the capabilities of an operating system, data platform and an application platform. It has a file system of its own and has its own fundamental capability to handle applications and control the resources that are used by those applications. In addition, uses YARN (Yet Another Resource Manager), which is a cluster management technology.
Some of the other characteristics, which makes Hadoop the best framework for Big Data implementation are:
- Scalability: Hadoop is highly scalable and can manage any amount of processing requirements. Especially the data coming from social media and next generation devices connected to IoT (Internet of Things), which generates a vast amount of data. Hadoop does not need high end servers with large memory and high processing power. It works on commodity hardware, which is affordable and easy to obtain.
- Parallel Processing: Parallel processing through MapReduce makes Hadoop a very powerful and fast computing platform. This processing technique distributes the processing across multiple nodes and processes data from where it is stored instead of transporting data across network.
- Fault Tolerance: Hadoop is highly fault tolerant. It uses HDFS(Hadoop Distributed File System), which is a distributed file system and always makes 3 copies of the entire file system across 3 separate compute nodeswithin a cluster. Whenever a node goes offline HDFS uses another node to serve the request and to avoid any kind of disruption.
- Flexible: Hadoop is very flexible and allows you to capture and store many different data types including documents, images, and videos and makes them readily available for processing and use. This is a big advantage to businesses because it allows business to draw valuable insights from various different data sources such as social media, email conversions or click stream data.
- Lower Administration: Hadoop does not have high administration and monitoring needs. It works resiliently and does not pose any scaling issues when situation demands.