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Get a QuoteBig Data often helps the organizational or business decisions. The concept of Big Data and Analytics is the complex process of scrutinizing the huge data sets with varied permutations and combinations to discover the data structure, and data relations. Big Data Analytics helps the Organisations to interpret the customer pulse, customer feedback and contributes towards the business decisions. Organizations using Big Data Analytics, are dealing with terabytes of data that is received from different means like and processes to derive a logical equation that contributes to the strategic planning. Big Data overrules the capacity of traditional RDBMS available in the market. As the term says, it is the capacity of Big Data is very huge to manage or process.
Volume, Velocity and the variety are main features of Big Data. Fast moving trends of internet operations, usage, Artificial Intelligence, Social Media, and Mobile World are contributing to the intricacy of Data. Various sources that contribute towards Big Data are Chats, Facebook, What’s App, Files, feedback posted on sites, customer reviews, feedback, and images posted on Social Media.
Generally the data in the form of chats, SM conversations, Facebook chats/images and customer feedbacks, views and reviews content is the available data that is in unstructured format. Leverage this unstructured data by applying AI and converting it into meaningful structured data that helps the Organizations to derive best business equations is the capacity of Big Data Analytics.
Sentiment Analysis
Customer Churn Analysis
Advertisement Analysis
Predictive Analysis
Weather Forecasting
Health Care Analysis
The Best Way to a solution is to “Split the Problem”. This Architecture helps in designing the Data Pipeline with the various requirements of either the Batch Processing System or Stream Processing System. Secured data flow can be viewed under Big Data Architecture in 6 layers that ensures data security.
This layer is the first step for the data coming from variable sources to start its journey. Data here is prioritized and categorized which makes data flow smoothly in further layers.
In this Layer, more focus is on the transportation of data from ingestion layer to rest of data pipeline. It is the Layer, where components are decoupled so that analytic capabilities may begin.
In this primary layer, the focus is to specialize in the data pipeline processing system, or we can say the data we have collected in the previous layer is to be processed in this layer. Here we do some magic with the data to route them to a different destination, classify the data flow and it’s the first point where the analytic may take place.
Storage becomes a challenge when the size of the data you are dealing with, becomes large. Several possible solutions can rescue from such problems. Finding a storage solution is very much important when the size of your data becomes large.
Main data analytics processing is done here. The primary focus is to gather the data value so that they are made to be more helpful for the next layer.
Visualization Layer is the most important layer and this is phase where the users can have the look and feel and important of Data. This provides the view for the end users of the actual VALUE of the Data synthesized.