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Top Hadoop Tools to Make Your Big Data Analytics

In the Past before digitalization, amounts of data were generated at a relatively sluggish pace. all the data was mostly documents and in the form of rows and columns. storing or processing this data wasn’t much trouble as a single storage unit and processor combination would do the job but as years passed the internet took the world by storm giving rise to tons of data generated in a multitude of forms and formats. Every microsecond, semi-structured and unstructured data is available now in the form of emails, images, audio, and video to name a few all this data became collectively known as big data in Hadoop assignment help services. although fascinating it became nearly impossible to handle this big data and a storage unit processor combination is obviously not enough so what was the solution. Multiple storage units and processors were undoubtedly the need of the hour. This concept was incorporated in the framework of Hadoop that could store and process vast amounts of any data efficiently using a cluster of commodity hardware. 

Hadoop consisted of three components that were specifically designed to work on big data in order to capitalize on data the first step is storing it. The first component of Hadoop is its storage unit, the Hadoop distributed file system or HADOOP assignment help from top experts. storing massive data on one computer is unfeasible hence data is distributed amongst many computers and stored in blocks. So, if you have 600 megabytes of data to be stored, hdfs splits the data into multiple blocks of data that are then stored on several data nodes in the cluster. 128 megabytes is the default size of each block hence 600 megabytes will be split into four blocks a, b, c, and d of 128 megabytes each and the remaining 88 megabytes in the last block. 

so now you might be wondering what if one data node crashes. Do we lose that specific piece of data? hdfs makes copies of the data and stores it across multiple systems for example when block. ‘a’ is created. it is replicated with a replication factor of 3 and stored on different data nodes. This is termed the replication method by doing so data is not lost at any cost even if one data node crashes making hdfs fault-tolerant after storing the data successfully. 

Features of HDFS

1) Data replication

2) Fault tolerance and reliability

3) High availability

4) Scalability

5) High throughput

6) Data locality.

 

Benefits of using HDFS

1) Cost effectiveness

2) Large data set storage

3) Fast recovery from hardware failure

4) Portability

5) Streaming data access

  

2) MapReduce

In the traditional data processing method, entire data would be processed on a single machine having a single processor. This consumed time and was inefficient especially when processing large volumes of a variety of data. To overcome this MapReduce splits database assignment help into parts and processes each of them separately on different data nodes. The individual results are then aggregated to give the final output. Let’s try to count the number of occurrences of words. In this, First, the input is split into separate parts based on a delimiter. The next step is the mapper phase where the occurrence of each word is counted and allocated a number after that depending on the words. similar words are shuffled, sorted, and grouped following which in the reducer phase all the grouped words are given a count, finally, the output is displayed by aggregating the results. all this is done by writing a simple program. similarly, MapReduce processes each part of big data individually and then sums the result at the end. This improves load balancing and saves a considerable amount of time now that we have our MapReduce job-ready. 

  

3) NoSQL databases

 It is not a relational database that means while creating a database we don’t have to pre-defined a schema. It is also called a non-relational database. It is used to store unstructured and semi-structured data.

NoSQL database can be divided into the following four types:

Key-value storage: In this database data is stored in key-value pair where each key is associated with one and only one value in a collection. It is easy to use, scalable and fast. Some key-values databases are Redis, Riak, etc. Document-oriented Database assignment help: It stores data in JSON format. We don’t need to predefine schema for database and all information for an object or document is stored in a single instance. Some of the popular databases are MongoDB, PostgreSQL, Elasticsearch, etc.

Graph Databases: It is the type of NoSQL database where data is stored as nodes, relationships, and properties. It uses nodes to store data entities, and relationships between entities are stored in edges in Jupyter Notebook Data Analytics. An edge has direction,  start node, and end node which also describe parent-child relationships. Some of the popular graph databases are SPARQL, Neo4J, etc. Wide-column stores: It is the schema-free database that stores data in records and a record has large numbers of columns. Some of the databases are Cassandra, HBase, etc.

 

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