What is Hadoop technology?
Hadoop technology is basically an open source framework based on the Java programming language, that allows for the distributed processing and storage of large data sets across clusters of computers. This technology was developed by the Apache Software Foundation. The technology uses different programming and computation models to solve the common occurrences of hardware failure caused by computation and processing of big data hence mitigating the risk of system failures and unexpected data losses.
Hadoop’s four core elements are:
- Hadoop Distributed File System(HDFS)
- Hadoop MapReduce
- Hadoop YARN
- Hadoop Common
What is a Hadoop cluster?
A cluster is simply a combination of many computers designed to work together as one system. A Hadoop cluster is, therefore, a cluster of computers used at Hadoop. Hadoop clusters are designed specifically for analyzing and storing large amounts of unstructured data in distributed file systems. These computer clusters run Hadoop’s open source distributed processing software to achieve this task. Typically, Hadoop clusters are organized in racks having three nodes that are; master node, worker node, and client node. Each node has a specific role in achieving the task above.
What are Hadoop and big data?
Big data is a huge amount of large data sets usually made up of thousands of terabytes. Due to its sheer size, it’s often difficult and time-consuming to create, manipulate, process and manage big data. Hadoop clusters come up with a solution to this problem by sharing the processing power between each machine in the cluster, hence boosting the processing speed of data analysis applications.
Hadoop clusters are highly scalable. Therefore, they can be expanded further by adding new cluster nodes in order to boost the cluster’s processing power. This comes in handy when there is an ever-growing volume of data to be processed as is the case in many technology companies such as Facebook and Google. Hadoop clusters can be monitored and managed for optimal performance using another web-based tool by Apache called Ambari.
What is Hadoop ecosystem?
The Hadoop ecosystem refers to the add-ons that make the Hadoop framework more suited to specific big data needs and tastes. The Hadoop ecosystem consists of both open source projects and commercial tools that are optimized to serve different purposes with regards to big data.
Examples of open source projects include:
- Spark – A programming and computing model, used as an alternative to Google’s MapReduce model utilized for in-memory computing.
- Hive – Is a SQL-based data warehouse software for Hadoop that defines how data is structured and queried in Hadoop’s clusters. Hive contains HCatalog, HiveQL, and WebHCat among other components.
- Pig – Is a platform for developing parallel processing applications for managing and analysis of large datasets.
- HBase – Is a flexible, distributed NoSQL database that is built into the HFDS and used for storing large sets of structured data.
- Sqoop – This is a front-end tool for importing and exporting big data from Hadoop to relational databases.
- Oozie – this is a back-end web application used as a workflow scheduler for big data.
Spark, Hive, and Pig are the main and most used Apache open source Hadoop ecosystem elements. Hadoop also offers commercial tools that are even more diverse and provide more comprehensive functionality. These tools can be accessed from vendors such as Cloudera and Hortonworks.
What is Hadoop good for?
Hadoop technology is good for handling flexible big-data analytics in various data formats ranging from unstructured data formats such as raw text to semi-structured formats such as logs, and finally to structured data formats. Hadoop can be used in any environment where big data is collected and due to its scalability, overly, it can be used in both small and big firms.