Digital Nest Blog

Important Tools used in Big Data

Important Tools used in Big Data, Benefits of Big Data Analytic tools, Applications of Big data, What are the tools used in Big Data

Important Tools used in Big Data, Benefits of Big Data Analytic tools, Applications of Big data, What are the tools used in Big Data

In the previous article, we have read what Big Data is. As traditional analytics and Business Intelligence solutions, Data mining and analytics also help find the hidden patterns, correlations, raw data and the other beneficial business data. Big data is all about 3 V’s of data i.e Variety, Volume and Velocity of Data. Here Volume applies to the total amount of data, Variety applies to a number of types of Data and Velocity applies to the speed at which data streams.

Benefits of Big Data Analytic tools

 

There are many resources and using Big Data changes the way a business performs

  • Cost –Savings
  • Competing Advantage
  • Improved customer service
  • Better operational efficiency
  • New business opportunities
  • Recalculating entire risk portfolios in minutes.
  • Prior identification of risk to the product/services
  • Detecting illegal performance before it affects your business.
  • Business can employ the external intelligence while making decisions
  • Creating coupons easily at the point of sale based on the customer’s buying practices

Applications of Big data

Big data is applied widely in many fields. Some of them where Big Data is used

Applications of Big data - Digital nest - www.digitalnest.in

MARKETING SECTOR

  • Campaign management and optimization
  • Micro-segmentation of consumers and markets
  • Location-based marketing
  • Cross-selling and up-selling

FINANCE

  • Risk management
  • Fraud detection and prevention
  • Wealth management
  • Anti-money laundering

GOVERNMENT

  • Fraud and threat prediction and detection
  • Cybersecurity
  • Compliance and regulatory analysis

HEALTHCARE

  • Patient care quality and outcomes analysis
  • Reimbursement modelling
  • Public health reporting
  • Clinical data transparency
  • Public health surveillance and response
  • Clinical trial design and analysis

INSURANCE

  • Risk assessment and avoidance
  • Claims fraud detection
  • Call center workload analysis
  • Telematics-optimized underwriting
  • Customer value management
  • Catastrophic planning

RETAIL

  • Merchandising and market basket analysis
  • Supply chain management and analytics
  • Loyalty program management
  • Event/ behaviour-based targeting
  • Cross-channel customer service optimization

TELECOMMUNICATION

  • Customer churn prevention
  • Call detail record analysis
  • Network planning and optimization
  • Mobile user location analysis
  • New product research and development

GAMING

  • Improve playing to paying conversion rates
  • Optimize virtual offers based on player behaviour
  • Optimize games for player retention

What are the tools used in Big Data?

Apache Hadoop

Hadoop is an open source software framework originally incepted in 2006. The main purpose of Hadoop is to handle a large number of datasets. It is built with two main segments.

  1. Hadoop Distributed File System (HDFS) and
  2. MapReduce. HDFS is the storage constituent of Hadoop.

Hadoop stores data by dividing files into large blocks and scattering it across connections. MapReduce is the processing engine of Hadoop. Hadoop operates data by delivering code to nodes to process in correspondence. If you want to learn Hadoop for Big Data, check out the link Here

Apache Spark

Apache Spark is quickly emerging as a data analytics tool. It is an open source framework used for cluster computing. Spark is commonly used as an option to Hadoop’s MapReduce as it is capable of analyzing data up to 100 times faster for certain applications. Some of the common use cases of Apache Spark include streaming data, machine learning and interactive analysis. If you want to learn Spark for Big data, click here

Apache Hive

Apache Hive is a SQL-on-Hadoop data processing engine. Apache Hive excels at batch processing of ETL jobs and SQL queries. Hive employs a query language called HiveQL. If you want to learn Hive for Big data, click here

NoSQL Databases

NoSQL databases have great demand these days. These are not bound by traditional schema models allowing them to collect unstructured datasets. There are some of the databases like MongoDB, Cassandra, and HBase which have a lot of flexibility. That is the main reason why it has been a popular option for big data analysis.

However, for the Big Data Analysts, knowledge of these concepts is a must. If you want to learn the complete package of Big Data, head over to Digital Nest. It renders complete spectrum of courses for the students, IT professionals, and corporates to excel in the Big Data field.

digital

 

Add comment