Digital footprints are inevitable in today’sworld. Data is generated almost everywhere including bio-medical data in lifesciences, geospatial data in logistics, sensory data from IoT devices, and publicdata in social networking sites.

Big Data is a blanket term for datasets thatare so large in volume, move in and out with a velocity that makes it difficultto analyze, and are collected from varying sources as structured, unstructuredand semi structured data. Thus, it is not adequate to acquire, store and processthem using traditional data processing software. Nevertheless, organizations want to explorethese massive datasets and extract value from them as this acumen will helpfacilitate their productivity, identify customer behaviour and opinions, and promotebest practices. This is where a Big Data Platform comes into picture and helpsto collect, store, manage and analyze big data. Good characteristics is crucialfor a platform as it enables efficient storage and processing of huge datasetsand outputs analyzed data for consumption via business intelligence and datavisualization tools, and thus promotes exploration of datasets.

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Ideally, a productive big dataplatform addresses the 3V’s of big data– Volume, Velocity and Variety andenables integration with information architecture including databases, datawarehouses and BI applications to unveil purposeful information; the platform offersscalability such that the solution is scalable even up to petabytes. Thesolution is extensible to hold newer technologies and fault-tolerant to avoidfailures, especially in real-time analytics. Apache Hadoop, an open-sourceframework almost synonymous with big data, allows large-scale distributed storageand computing of enormous datasets across clusters of commodity computers in acost-effective manner. Hadoop’s clustered computing delivers higher storage& computational speed compared to classical data processing systems and isscalable by adding machines to the network. It provides Batch Processing inwhich the entire datasets being processed are usually bounded, persistent andlarge. MapReduce, the parallel processing engine for Hadoop, divides thedatasets into chunks and distributes them across the available nodes in whichthe computation power of the nodes is utilized for the processing of these datasubsets. Reshuffling is done based on intermediate results and the results of individualnodes are assembled to get the final result.

The Hadoop Distributed File System(HDFS) empowers handling of any type of data by providing reliable data storageacross all the nodes in a cluster. It allows the storage of unstructured/semistructured data across the cluster, facilitates processing of data at thepetabyte scale, stores intermediary processing results, and also persists thefinally computed results. While batch processing is taken careof by Hadoop, Stream Processing is event-based and real time i.e., individualdata items are computed continuously even as new data comes in. Apache Spark isa lightning-fast framework that supports advanced analytics such as SQLqueries, machine learning, streaming data and graph algorithms, which makes it competentfor real time stream processing. Spark’s in-memory cluster computing offers acomputation speed that is much faster than that of MapReduce and helps inbuilding analytical datasets.

It is also a better fit for machine learning thanHadoop due to its dedicated libraries – MLib, speed and stream processingabilities. Since Spark doesn’t have its own distributed file system likeHadoop’s HDFS, it is often installed on top of Hadoop making use of the datastored in HDFS along with its stream processing capabilities.However, it has become increasinglydifficult for organizations to meet the requirements on infrastructure for bigdata. In order to meet this demand, vendors provide Hadoop-as-a-Service (HDaaS)on a pay-per-use basis. Organizations have the choice of choosing a commercialdistribution among in-house and cloud-based distributions. A cloud-onlyplatform acts as a substitute for in-house Hadoop and empowers organizations tostore, manage, analyze and visualize big data on the cloud without having to procure,maintain and scale any infrastructure.

It is up to an organization to decidewhich services it requires based upon its individual needs. For instance, an organizationthat prefers to gain business insights from its structured and unstructureddata would benefit by installing Spark on top of Hadoop’s storage. Some of theleading HDaaS market players include Amazon Elastic Map Reduce, Cloudera, Hortonworks,MapR, Google and Microsoft amongst others.The overallpurpose of storing and managing a myriad of huge datasets is to explore them andapply them to Business Intelligence. Various techniques like optimization,pattern recognition, statistical analysis, machine learning and data visualizationneed to be performed in order to present the final output to decision makers.

Firstly,data needs to be cleaned to eliminate incomplete, incorrect and duplicaterecords. Data Cleansing tools such as OpenRefine, Trifacta Wrangler help inachieving this. After cleaning, the data will be ready for data mining throughwhich useful trends and patterns could be identified. Popular choices for data mininginclude Sisense and RapidMiner, which will help in transforming patterns intoeffective information. Using these obtained patterns, data analysis can beimplemented to find out answers for desired questions that concern with thedevelopment of business. The Hadoop ecosystem contains data analysis tools likeHive, Pig and HBase.

The final step is to expose these insights to businessexecutives via Data Visualization. Visualization techniques include linegraphs, network diagrams, word clouds and correlation matrices. It is a vitalphase since any misinterpretation of data could easily lead to faulty decisionmaking, which could adversely affect the business.

Tableau and Qlik Sense are amongthe well-known visualization tools in the market to showcase comprehensibledata by illustrating key relationships and unknown correlations for smarterdecisions.Modern bigdata platforms are well-equipped to provide the necessary solutions using whichbusinesses can leverage their operations. An online bus ticketing agency inIndia – Redbus, uses Google BigQuery for analyzing its booking and inventorydata. Google’s cloud platform helps RedBus gain valuable customer insights suchas bus booking patterns without having to maintain an on-premiseinfrastructure.

Even government forces such as the Indian Army, border securityforce and police departments across India have benefited from data analytics. Bigdata has the potential to bring tremendous opportunities and big data platformshelp in achieving that goal.