Important Facts About Hadoop

Important Facts About Hadoop

Important Facts About Hadoop

Operating huge data is considered to be one of the difficult challenges and Hadoop parallelizes processing data across numerous cluster nodes. Hadoop is prominently suited well for processing task of large data and thus it can hold its system of the distributed file to reliably and cheaply replicate data chunks to nodes in the cluster. It makes the data accessible on the machine which is processing it. Learn Hadoop Training Chennai to enhance your career growth

Let’s discuss some of the important facts about Hadoop to solve the problems that are related to huge structured and unstructured data volumes.

1. Import/Export Data (Hadoop Distributed File System)
In the world of Hadoop, data can be imported to the HDFS from different kinds of mixed sources. A required processing level can happen using MapReduce on the data or other languages such as Pig, Hive, etc. The Hadoop system offers the flexibility to you, it does not only process the large volume of data but the processed data like aggregated, and filtered and transformed data which can be exported to external or some other databases using a scoop. Thus exporting data to other databases such as SQL Server, MySQL, MongoDB, etc. is said to be a powerful feature, that can be leveraged for containing better control over data.

2. Data Compression
Hadoop reserves data in HDFS and it supports data compression or data decompression. The Data compression can be achieved with compression algorithms like LZO, gzip, bzip2, etc. Various algorithms can be used in various scenarios that depend upon their capabilities. For e.g., file split ability or compression/decompression speed. Take up Hadoop Training in Chennai to update yourself and become an expert.

3. Transformation
Hadoop is said to be a perfect environment for transforming and extracting large volumes of data. Hadoop also provides a distributed, reliable, and scalable processing environment. There are numerous methods to transform and extract data with MapReduce, Pig, and Hive etc.

Once the input data is placed or imported to HDFS, then the cluster of Hadoop is used to transform huge datasets in parallel. Transformation can be done through available tools. For e.g., if you need to transform data to a tab separated file then MapReduce is said to be one of the best tools for it. Python and Hive can be leveraged to transform and clean geographical event data.

4. Acquire Common Task
There are numerous common tasks which need to be done during processing of data daily and frequency will be very high. The accessible languages like MapReduce, Pig, and Hive are much useful to attain these tasks.
Sometimes a task can be obtained in various ways. In such kind of situation, an architect or a developer has to take the correct decision to execute the best solution. For e.g., Pig and Hive an abstraction between queries and data flow, and the MapReduce workflows they assemble. The Pig is used by writing the operation in Pig Latin. Hive is used to manage data and build analytics with HiveQL. MapReduce is leveraged for scalable queries.

Big Data Training in Chennai helps you enhance your skills and offers you greater knowledge. Hope this article provides you brief information about some of the important facts about Hadoop.


comments user

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comments user

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