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Big Data Investment and Learning: Need of the Hour

Apache Spark uses cases have been gaining a lot of fame in its area of work. It has probably become one of the largest open source communities in big data. This open source has grown to be one of the largest producers of big data significantly at a faster rate. It’s the need of the hour to understand that every engine requires different use cases an all sorts of business and commercials require a combination of these derived use cases.

Frequently used Use Cases

Apache Spark use cases which are frequently in use and demand are:

1. Streaming data: Surplus volume of data are being processed daily and to manage and analyze them in a given time is equally important. This favour is given by Spark Streaming. It has the potential to load this burden and get it sorted.

2. Machine Learning: The machine learning library of Apache Spark use cases work on various areas such as classification, dimensionality reduction, clustering and many more. Network security is another feature of Spark use cases. It helps in finding any sort of fraudulent activity.

3. Interactive analysis: One of the most fulfilled features of theApache Spark use case. This is basically the potential of performing interactive queries related to living data.

4. Fog Computing: This is basically designed to work on data that is compulsory to get viewed. Basically, these perform those works that are on the edge of the network.

5. Other businesses which are benefitting from Apache spark use case are:

a) Uber: The most required necessity in today’s era. This company collects terabytes of data daily from the users.

b) Pinterest: Commonly used site for browsing different quotes, images, recipes, products and many more. This is also investing with Spark Streaming to get immediate responses from viewers.

Call centre analytics, another area of great achievements basically focuses on providing distinct opportunity to improve the efficiency, overall performance of the employee, various services from all times and most importantly customer satisfaction. Call centre analytics is basically the various tools that various companies keep for their optimal performance.

Call centre analytics have six approaches for improvement

Successful centres these days employ call centre analytics to monitor and review the performances not from the customer’s point of view but also from the employee’s and different business owner’s perspective.

  1. Call centre speech analytics
  2. Call centre text analysis
  3. Predictive analysis
  4. Call centre desktop analytics
  5. Self-service analytics
  6. Cross channel analytics

With all these big data investments done significantly in a faster way Apache Spark use cases have now acquired a prized position and possession in big data fields. It is been adopted by a wide range of industries. Apache spark use cases singularly focus on all sorts of data sets whether structured, unstructured or semi-structured in order to derive intelligence-based applications. It has by far achieved lots of success when it comes to big data and cloud computing spaces. In short security, compliance and privacy have been the top priorities for success.

Written By

I'm Emma Thompson from US. Writing is my passion and my interest in researching and writing on technical topics such as Business, Technology, IT Services and more.

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