Hadoop and Spark
HADOOP AND SPARK
- Farsana P
- 17 September 2019
Hadoop is an open source distributed processing framework that manages data processing and storage for big data applications running in clustered systems. It is at the center of a growing ecosystem of big data technologies that are primarily used to support advanced analytics initiatives, including predictive analytics, data mining and machine learning applications. Hadoop can handle various forms of structured and unstructured data, giving users more flexibility for collecting, processing and analyzing data than relational databases and data warehouses provide.
Hadoop runs on clusters of commodity servers and can scale up to support thousands of hardware nodes and massive amounts of data. It uses a namesake distributed file system that's designed to provide rapid data access across the nodes in a cluster, plus fault-tolerant capabilities so applications can continue to run if individual nodes fail. Consequently, Hadoop became a foundational data management platform for big data analytics uses after it emerged in the mid-2000s.
Spark has become one of the key cluster-computing frameworks in the world. Spark can be deployed in numerous ways like in Machine Learning, streaming data, and graph processing. Spark supports programming languages like Python, Scala, Java, and R. Spark is an open-source distributed cluster-computing framework. Spark is a data processing engine developed to provide faster and easy-to-use analytics than Hadoop MapReduce. Before Apache Software Foundation took possession of Spark, it was under the control of University of California, Berkeley's AMP Lab.
Hadoop is among the major big data technologies and has a vast scope in the future. Being cost-effective, scalable and reliable, most of the world's biggest organizations are employing Hadoop technology to deal with their massive data for research and production. Future is all about big data and spark provides a rich set of tools to handle real-time the large size of data. Its lighting fast speed, fault tolerance, and efficient in-memory processing make Spark a future technology.