An Overview of Big Data and Hadoop Databases

What do you mean by Big Data? Over recent years, Big Data has been in the news. This refers to data that is very large and big. Generally, users work with data with MB or GB sizes, but if the data is in Petabytes, it becomes Big Data. Today, over 90% of the data has been generated in the last few years.

What are the sources of Big Data?

There are several sources like-

1. Social networking platforms- Social networking platforms like Google, Facebook, and LinkedIn, generate large amounts of data daily. Billions of people use them across the globe.
2. Ecommerce sites- Sites like Amazon, Flipkart, and Alibaba generate a lot of logs about their users. From here, their buying patterns and trends are traced.
3. Telecom companies-Telecom companies like Vodafone, Orange, and more store and study the trends of their users. Based on these surveys, the data is stored, and they can publish their plans for millions of people.
4. Weather Stations- Satellite and weather stations offer massive data that is stored and mainly manipulated for forecasting the conditions of the weather.
5. Share market- The Stock Exchange across the globe generates large amounts of information or data via daily transactions.

Big Data comprises of 3V’s

Velocity- This data is increasing fast, and there are estimates that this volume of data will double every two years.

Variety- Today, data is no longer collected and stored in columns and rows. This data is structured and unstructured. The CCTV footage and log files are examples of unstructured data. Data that can be stored in tables like the bank's accounting transactions is an example of structured data.

Volume- the amount of data that you deal in is large and the size of petabytes. A vast quantity of unstructured data has to be stored, then processed, and finally analyzed.

How does Big Data solve problems?

The following are the key points via which Big Data resolves problems-

1. Storage - To store vast amounts of data, Hadoop deploys HDFS or Hadoop Distributed File System that uses the commodity hardware to create clusters and store it in an even fashion. The principle it works on is to write one and read several times.
2. Processing- Here, the paradigm of map-reduce is applied to the data distributed over the network for finding the needed result.
3. Analyze- Hive and Pig are used for analyzing the data.
4. Cost- Hadoop is an open-source database where prices are no longer an issue.

An overview of Hadoop

Hadoop is an open-source database sourced by Apache and used for the analysis and process of data large in volume. Hadoop does not use the online analytical processing and OLAP and is written in the JAVA language. This database is used for offline and batch processing. Companies use Hadoop like Google, Facebook, Twitter, LinkedIn, and more credible companies. The database can be easily scaled up with the addition of nodes in the database cluster. 

What are the modules of the Hadoop database?

Esteemed company in the field of database managed services, RemoteDBA says Hadoop is best suited for large data sets. Given below are the main modules of the database-

1. HDFS- the Hadoop Distributed File System found its place in the GFS paper published by Google. It was based on this paper that HDFS was created. This paper says the files can be broken into several blocks and stored into nodes across its distributed architecture.
2. Yarn- This is another kind of Resource Negotor used for the scheduling of tasks and the management of the cluster
3. Map Reduce- This is the framework that aids programs under JAVA to complete parallel computation on the data that uses the pair with the critical value. This task takes the data that has been placed and transfers it into a computed data set as the key-value pair. This result of the map task can be consumed by the reduced task and later out of the reducer for getting the desired results.
4. Hadoop common- These libraries under Java are used for Hadoop. They start the database, and other modules of the database use them too.

Hadoop Architecture

The Hadoop architecture is a complete package of the system of files, HDFS, and MapReduce engine. This can be Yarn/MR2 or MapReduce/MR1. One cluster under Hadoop has a single master and several slave nodes. This master node has a task tracker, a job tracker, a name node, and a data node. The slave node includes the task tracker and data node.

Benefits of the Hadoop database

Given below are the advantages of Hadoop database -

1. It is fast- Fast retrieval of data is possible because the data across the cluster can be mapped. The tools for processing this data are located on the same server, so here the processing time is saved. Petabytes of data can be processed in hours and terabytes in minutes.
2. Scalable- The cluster under Hadoop can be extended with the addition of nodes in the cluster.
3. Affordable- Since the database is open source and uses commodity hardware for the storage of data. This makes it affordable over conventional relational database management systems.
4. Handles failures better-The Hadoop database has all the properties with which it can duplicate the data across the network. This means that if one of the nodes is down and a network failure occurs, Hadoop takes a copy of the data and uses it. Generally, this data is replicated around 3 times; however, the replication factor can be configured.

The Hadoop database is fast and flexible. This is why it is the ideal solution for large data solutions. It can be easily scalable and helps to collect vast amounts of data on affordable servers.

Hadoop is a database that helps with Big Data; however, it does have some security concerns. If businesses can take care of this, it is ideal for streamlining large volumes of data in a fast and flexible manner.