Big Data Analytics

Data with a huge size is known as big data.It is generally used to describe a data collection that is tremendous in size and is still growing with time. Put simply, this data is so large and complex that the traditional data management tools are able to store it or process it efficiently.

Some common examples of big data are:

Big Data Analytics goes through large chunks of data to discover hidden patterns, correlations and other information.By using today’s technology, it is possible to examine your data and get answers from it almost immediately. It offers an almost endless source of business and informational insight that can lead to improvement in the operations and new opportunities that provide companies with unrealized revenues across almost every industry.


The importance of big data analytics

Driven by specific data analytics systems and programming, just as powerful computing systems, big data analytics offers different business benefits, including:

  • New income openings
  • Increasingly compelling advertising
  • Better client assistance
  • Improved operational proficiency
  • Upper hand over rivals

Big data analytics applications let big data analysts, data scientists, predictive modelers, statisticians and different analytics experts to break down developing volumes of structured transaction data, in addition to different types of information that are regularly left undiscovered by conventional BI and analytics programs. This envelops a blend of semi-organized and unstructured data - for instance, web clickstream data, web server logs, web-based social media content, text from customer emails and survey reactions, cell phone records, and machine data caught by sensors associated with the internet of things (IoT).


Big Data Analytics Technologies and Tools

Unstructured and semi-structured data types regularly don't fit well in data warehouses that depend on relational databases related to structured data sets. Furthermore, data warehouses will be unable to deal with the preparing requests presented by sets of big data that should be refreshed every now and again or even constantly, as on account of real-time data on stock exchanging, the online activities of site visitors or the performance of smartphone applications. Therefore, a large number of the associations that gather, process and break down big data go to NoSQL databases, just as Hadoop and its buddy data analytics tools, including:


  • YARN: One of the key highlights in second-age Hadoopand cluster management technology.
  • MapReduce: A product structure that enables software developers to write programs that process huge amounts of unstructured data in parallel over a distributed cluster of processors or stand-alone PCs.
  • Spark: An open source, parallel processing framework that empowers clients to run enormous scaledata analytics applications across clustered systems.
  • HBase: A segment arranged key/valuedata store constructed to run over the Hadoop Distributed File System (HDFS).
  • Hive: It is an open source data warehouse system that is used for querying and analyzing large data sets stored in Hadoop files.
  • Kafka: Designed to replace traditional message brokers, the Kafka is a distributed publish/subscribe messaging system.
  • Pig: This is an open sourced technology which offers a high-level mechanism for the parallel programming of MapReduce jobs that are executed on Hadoop clusters.


The working of big data analytics

Sometimes, Hadoop clusters and NoSQL systems are majorly used as staging areas and landing pads for data before it gets stored into a data warehouse or analytical database for analysis – which is usually in a summarized form that is more conducive to relational structures. However, big data analytics these days are using the concept of a Hadoopdata lake which will act as the primary repository for the incoming streams of raw data.When you are using such architecture, the data can be analyzed directly using a Hadoop cluster or can be run through a processing engine like Spark.Just like in data warehousing, the crucial first step in the big data analytics process is sound data management. Therefore, the data stored in the HDFS must always be organized, configured and parted properly for better performance of extract, transform and load (ETL) integrated jobs and analytical queries.

The data that is ready can be analyzed with the software that is commonly used for advanced analytics processes. It generally includes tools that are used for

  • Data mining, which will thoroughly go through data sets looking for patterns and relationships;
  • Predictive analytics, which is done to build models to predict customer behavior and other developments for the future;
  • Machine learning, which is used to write algorithms to analyze large data sets and
  • Deep learning, which is a more advanced form of machine learning.

Similarly, text mining and statistical analysis software can also play a huge role in the big data analytics process, so can major business intelligence software and data visualization tools as well.


The uses and challenges of big data analytics

More often than not, the big data analytics applications include data both from the internal systems and external sources namely weather data or demographic data of consumers that are compiled by third-party information service providers. In the same way, streaming analytics applications are being commonly used in big data environments as users are looking to perform real-time analytics on data that is fed into Hadoop systems through the stream processing engines like Spark, Flink and Storm.

Earlier, in large organizations, the big data systems were mainly deployed on premises where massive amounts of data was collected, organized and analyzed. However, cloud platform vendors like Amazon Web Services (AWS) and Microsoft have facilitated the set up and managing of Hadoop clusters in the cloud, which support the distribution of big data frameworks on the AWS and Microsoft Azure clouds.

As far as supply chain analytics is concerned, big data is increasingly beneficial.Big data and quantitative methods are utilized by big supply chain to improve the decision making processes across the supply chain. Highly effective statistical methods are implemented on new and existing data sources by big supply chain analytics. The insights that are gained are used to make better informed and more effective decisions that will benefit and improve the supply chain.However, the potential risks of big data analytics include a lack of internal analytics skills and a high cost of hiring qualified and seasoned data scientists and data engineers to fill the gaps.



Big data enables the clients to analyze, comprehensively get information from and deal with data sets that are too large or complex to be handled by normal data-processing application software. The modern big data analytics has benefits like speed and efficiency. This enables businesses to make immediate decisions.  Therefore, the ability of working faster and staying agile gives organizations an edge they did not have before. It also provides improved customer service and better operational efficiency.