Hadoop is a popular open-source project running under the Apache Software Foundation. To download it the user has to visit the Apache website. It is a free Java-based programming framework which supports big data processing in any distributed computing environment.
- Best programming framework for running applications on a distributed cluster
- Processes data at an unprecedented speed data and prevents the system from failures
- Supports both Windows and Linux operating systems
What is Big Data?
Major characteristics of Big data are volumes, velocities, and veracities. The volume is calculated in terms of terabytes these days. Analysis carried out on big data can identify the problems and focus points in the enterprise preventing big losses and opening up avenues for profit. When it comes to managing the big data, Hadoop is one of the best solutions.
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Big Data Hadoop characteristics
This big data framework is written in Java programming language and it has the capability of solving all the issues that crop up with big data analysis. The Hadoop programming model is modulated on Google MapReduce and its infrastructure is based on Google’s big data as well as its distributed file systems. Since Hadoop is scalable, additional nodes can be added to it.
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Hadoop is much different from the traditional RDBMS as the later is useful for single file as well as short data and Hadoop is useful for big data handling. It uses MapReduce to analyze big data. The filing system is HDFS that is used to store large data files. It can handle streaming data as well as running clusters on the commodity hardware. HDFS has great fault tolerance capabilities, high throughput and suitability for handling large data sets.
Hadoop installation on commodity hardware refers to average and non-expensive systems, and it may not require high-end hardware for functioning. However the master node in HDFS which is the Name node has job tracker running on it and it cannot be commodity because the entire HDFS works on the same. A couple of important elements in this framework are the job tracker and task tracker daemons. Job tracker runs on name node and it tracks the MapReduce tasks to be accomplished by Hoop. Usually there would be only one job tracker. Task trackers run on what data node and take care of individual tasks on slave nodes. The tasks to the task trackers are entrusted by Job tracker.
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Data nodes as well as the task trackers transmit heartbeat signals to the name node and also the job tracker. Receipt of such signals indicates that these apps are alive. If no signal is received it would indicate that there are some problems with either the node or the task tracker.
Hadoop users need to bear in mind that Hadoop only can process the digital data. Name mode determines the data node to write on. In whichever mode Hadoop runs; standalone, pseudo distributed and fully distributed modes; it is the best for handling big data.
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About The Author:-
Vaishnavi Agrawal has 10 years experience in various technology platforms such as Big Data, Hadoop, Java, etc. She has worked for companies such as American Express, Symphony Teleca, Mercedes R&D. She loves pursuing excellence through writing and have a passion for technology. She looks for new challenges in media as well as helping technology companies.She had successfully managed and run personal technology magazines and websites.