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With technology changing rapidly, more and more data is being obtained regularly.
A recent study suggests that around 2.7 Zettabytes of data exist today in the digital universe!
Therefore companies now require different software to manage huge amounts of data. They are constantly looking for ways to process and store massive amounts of data and distribute it across different servers so that the departments can easily operate and derive helpful results from it.
In today's article, we will discuss Hadoop, HDFS, HBase, and Hive, and how they help us process and store large amounts of data to extract useful information.
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Apache Hadoop is an open-source software that offers various utilities that facilitate the usage of a network on multiple computers to solve the problems on big data.
Hadoop also provides a software framework for distributed computing as well as distributed storage. For doing so it divides a document onto several stores and blocks across a cluster of machines. To achieve fault tolerance, Hadoop replicates these stores on the cluster. Nextly, it performs distributed processing by dividing a job into several smaller independent tasks.
This task and then run in parallel over the cluster of computers as discussed earlier Hadoop works with distributed processing on large data sets across a cluster service to work on multiple machines simultaneously. to process any data on Hadoop, the client submits the program and data to this utility. The HDFS stores data while the MapReduce function processes the data and then YARN divides these tasks.
Let's discuss the working of Hadoop in detail:
As mentioned earlier, HDFS is a master-slave topology running on two daemons; DataNode and NameNode. Datanode runs on the slave nodes it stores the data in Hadoop. On the very starting Datanode connects to NameNode and keeps checking for requests to access data. NameNode, on the other hand, stores directory of files into the file system. It is also responsible for cracking down where the cluster of file data resides however it does not store the data contained in those.
With the Hadoop distributed file system you can write data once on the server and then subsequently read over there and also use it many times. HDFS is a great choice to deal with high volumes of data required in the current day. The reason is that HDFS works with the main node and multiple nodes on a commodity of hardware cluster. All the nodes are usually organized within the same physical rack on the data center so when the data is broken down into different blocks, they are distributed among different nodes for storage. The blocks are also replicated across nodes to reduce the chances of failure.
HDFS uses validations and transaction logs to ensure data integrity across the cluster. Usually, there is only one NameNode and a possible data node running on the physical server while all other servers only run data nodes.
Although Hadoop is great for working with big data sets it only performs batch processing and therefore data can only be accessed sequentially. The good thing is that Hadoop has some other applications such as Hbase which can randomly access huge amounts of data files according to the user’s requirement. This is extremely useful for gaining concrete insights which are exactly why 97.2% of organizations are investing in big data-related tools and software.
Hbase is an open-source column-oriented database that is built on top of the Hadoop file system. It is horizontally scalable. The data model of HBase is very similar to that of Google's big table design. it not only provides quick random access to great amounts of unstructured data but also leverage is equal fault tolerance as provided by HDFS.
HBase is part of the Hadoop ecosystem that provides read and write access in real-time for data in the Hadoop file system. Many big companies use HBase for their day-to-day functions for the very same reason. Pinterest, for instance, works with 38 clusters of HBase to perform around 5 million operations every second!
what's even greater is the fact that HBase provides lower latency access to single rows from A million number of records. to work, HBase internally uses hash tables and then provides random access to indexed HDFS files.
HIVENow while Hadoop is very scalable reliable and the great for extracting data its learning curve is very steep to be cost-efficient and time-effective. Another great alternative to it is the Apache hive. Hive is a data warehouse software that allows users to quickly and easily write SQL-like queries to extract data from Hadoop.
The main purpose of this open-source framework is to process and store huge amounts of data. In the case of Hadoop, you can implement SQL queries using MapReduce Java API while in the case of Apache Hive you can easily bypass the Java and simply access data using the SQL like queries.
The working of Apache Hive is very simple it translates the input program written in HiveQL into one or more Java a MapReduce, Spark, or Tez jobs. It then organizes the data into HDFS tables and runs the jobs on a cluster to produce results. Hive is a simple way of applying structure to large amounts of unstructured data and then performing SQL based queries on them. since it uses interface very familiar with JDBC (Java Database Connectivity), it can easily integrate with traditional data center technologies.
Some of the most important components of the Hive are:
CONCLUSIONIn the above article, we discussed Hadoop, Hive, HBase, and HDFS. All these open-source tools and software are designed to process and store big data and derive useful insights.
Hadoop, on one hand, works with file storage and grid compute processing with sequential operations. Hive, on the other hand, provides an SQL-like interface based on Hadoop to bypass JAVA coding. HBase is a column-based distributed database system built like Google's Big Table - which is great for randomly accessing Hadoop files. Lastly, HDFS is a master-slave topology built on top of Hadoop to store files in a Hadoop environment.
All these frameworks are based on big data technology since their main purpose is to store and process massive amounts of data.
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