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Today there are new emerging sources which come hand in hand with new technological challenges which we are unable to meet with traditional systems. With the imminent need to process large amounts of data, there are solutions that help us to solve these gaps in some aspects.
What is Hadoop?
It is an Open Source framework from Apache that allows us to implement highly distributed, functional and scalable Big Data platforms without depending on the investment of licenses and hardware. Thanks to the fact that it is written in the Java programming language, we have various additional applications or projects that empower it in addition to its own adaptation of the Map-Reduce programming model for the processing of large databases including structured and unstructured. For comprehensive hands-on experience, participants in both Hadoop Admin Training in Bangalore and hadoop training institutes in pune have complete access to the virtual laboratory as well as various Big Data certification projects and assignments.
Hadoop is designed to create applications that process large volumes of data in a distributed manner through Map-Reduce. Furthermore, thanks to the fact that it works with local (but distributed) storage and processing, it allows us to work with single-node clusters as well as thousands of nodes, offering a high level of error tolerance.
Hadoop components
It mainly consists of two components which are divided into data storage / distribution and data processing:
- Hadoop Distributed File System HDFS
It is a distributed file system which allows data to be spread across hundreds or thousands of nodes for processing. This is where redundancy is provided (data is replicated or replicated across multiple nodes) and fault tolerance is provided (if any node fails it is automatically replaced).
In its operation, the HDFS file system divides the data into blocks where, in turn, each block is replicated in different nodes so that the fall of a node does not imply the loss of the data it contains. In this way, the use of programming models such as Map-Reduce is facilitated, since several blocks of the same file can be accessed in parallel.
- Map-Reduce
It is the heart of Hadoop which allows the easy development of applications and algorithms under the Java language for distributed processing of large amounts of data.
Within the ecosystem the applications developed for the Map-Reduce framework are known as Jobs, they are made up of the following functions:
- Map (Mapping): In charge of the division of the processing units to be executed in each node and their distribution for their execution in parallel. Here each call will be assigned a list of key / value pairs.
- Shuffle and sort: Here the results of the previous stage are mixed with the entire key / value pairs to combine them in a list and in turn is sorted by key.
- Reduce: Here all the keys and lists of values are received, making their aggregation if necessary.
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