Analytics and Big Data

With increase in magnitude of data, it is very difficult to process data in hitherto prevalent traditional ways. To process the huge amounts of data that enterprises are faced with today, business management need to implement a data management approach that helps them manage and process data, in various forms, through various Big Data technologies.

How Cloud Future Technology Helps

Cloud Future Technology built its expertise on Big Data and Analyitcs out of sheer necessity as data from the web exploded, and grew far beyond the ability of traditional systems to handle. Our big data and analytics services empower you to unlock value from the large volumes of customer and transaction data that you possess, helping you create and market improved and more relevant service offerings

At Cloud Future Technology we make your data valuable by processing them in such a way that it can be deployed or various analytical purposes. For instance

  • from user generated content on social media platforms
  • to purchase behaviour on e-commerce websites, or location data via smartphones
  • new opportunities arise to create more complete images of markets, consumer preferences, actions and lifestyles
  • Complementing existing (traditional) data, such new sources of data create unprecedented opportunities for enterprises, if they are able to connect the dots and systematically implement data-driven strategic and tactical decisions
  • In the Banking environment it would be easier to predict AML activities, credit risk on the basis of previous transactions
Node. JS

“Node.js is a packaged compilation of Google’s V8 JavaScript engine, the libuv platform abstraction layer, and a core library, which is itself primarily written in JavaScript.” The developer across the world love Node.js as it is easy to start, quick and constantly evolving, Building light-weight REST / JSON api’s is something where node.js is really useful. Its non-blocking I/O model combined with JavaScript make it a great choice for wrapping other data sources such as databases or web services and exposing them via a JSON interface.

One of other great benefit of Node.js is Traditional web stacks often treat http requests and responses as atomic events. However, the truth is that they are streams, and many cool node.js applications can be built to take advantage of this fact. One great example is parsing file uploads in real time, as well as building proxies between different data layers.


The lure of using big data for your business is a strong one, and there is no brighter lure these days than Apache Hadoop. Hadoop is the scalable data storage platform that lies at the heart of many big data solutions. But as attractive as Hadoop is, there is still a steep learning curve involved in understanding what role Hadoop can play for an organization, and how best to deploy it.

Hadoop is a distributed file system for storage and the MapReduce framework that lets organizations perform batch analysis on whatever data they have stored within Hadoop. That data, notably, does not have to be structured — which makes Hadoop ideal for analyzing and working with data from sources like social media, documents, and graphs: anything that can’t easily fit within rows and columns.

Because of its batch processing capabilities, Hadoop should be deployed in situations such as index building, pattern recognitions, creating recommendation engines, and sentiment analysis — all situations where data is generated at a high volume, stored in Hadoop, and queried at length later using MapReduce functions. But this does not mean that Hadoop should replace existing elements within your data center. On the contrary, Hadoop should be integrated within your existing IT infrastructure in order to capitalize on the myriad pieces of data that flows into your organization.

Consider, for instance: One of our clients having the logs from one of their customer’s popular sites would undergo an extract, transform, and load (ETL) procedure on a nightly run that could take up to three hours before depositing the data in a data warehouse. At which time, a stored procedure would be kicked off and (after another two hours) the cleansed data would reside in the data warehouse. The final data set, though, would only be a fifth of its original size — meaning that if there was any value to be gleaned from the entire original data set, it would be lost.

After Hadoop was integrated into this organization, things improved dramatically in terms of time and effort. Instead of undergoing an ETL operation, the log data from the web servers was sent straight to the HDFS within Hadoop in its entirety. From there, the same cleansing procedure was performed on the log data, only now using MapReduce jobs. Once cleaned, the data was then sent to the data warehouse. But the operation was much faster, thanks to the removal of the ETL step and the speed of the MapReduce operation. And, all of the data was still being held within Hadoop — ready for any additional questions the site’s operators might come up with later

Mango Data Base

MongoDB is an open source database that uses a document-oriented data model. MongoDB follows NoSQL banner. Instead of using tables and rows as in relational databases, MongoDB is built on an architecture of collections and documents. Documents comprise sets of key-value pairs and are the basic unit of data in MongoDB. Collections contain sets of documents and function as the equivalent of relational database tables.

Like other NoSQL databases, MongoDB supports dynamic schema design, allowing the documents in a collection to have different fields and structures. The database uses a document storage and data interchange format called BSON, which provides a binary representation of JSON-like documents. Automatic shredding enables data in a collection to be distributed across multiple systems for horizontal scalability as data volumes increase.

Oracle Business Intelligence Cloud Service

Cloud Future Technology has pre-built templates, reports, and dashboards that will help to accelerate implementation of Oracle BICS. We have developed the most popular reports including all relevant KPIs to support users in:

  • Human Resources
  • Finance
  • Procurement
  • Sales
  • With these pre-built templates, and our experience, we can implement a complete BICS solution in less than half the time it would take if you had to start from scratch, normally within 4 to 6 weeks
  • Business processes with ready built BICS solutions from Apps Associates:
    • Hire to Retire
    • Procure to Pay
    • Forecast to Deliver
    • Order to Cash
    • Financial Close

Apart from these pre-built applications, Cloud Future Technology will deploy Oracle BICS for any custom application of an organization adhering to our proven process and methodology.

Oracle BI Applicatiions

Cloud Future Technology is one of the best service providers certified for Oracle Business Intelligence. Over the years our team has developed extensive expertise in Oracle BI Applications and an in-depth knowledge of relevant enterprise applications such as ERP, Salesforce, and Hyperion. Our team will help you to:

  • Enable Mobile and Oracle Business Intelligence Cloud Service Solutions
  • Integrate your Oracle BI Applications with your enterprise applications and data sources including Oracle E-Business Suite, PeopleSoft, SAP, and many others
  • Conduct Oracle BI Applications workshops for your business users to review the current solution, develop a gap analysis and encourage user adoption
  • Build custom Oracle BI solutions that extend Oracle’s pre-built analytics and deploy a library of related data models developed by Apps Associates
  • Deliver industry specific solutions such as Life Sciences Analytics (Quality, Safety, Learning & Training, Product Lifecycle, Healthcare Aggregate Spend)
  • Migrate, build and optimize your Oracle BI Applications for Oracle Exalytics, In-Memory Analytics machine

Our Competencies
Over the years, Cloud Future Technology has created niche expertise on various new Big Data technologies – some of the most popular ones including Hadoop, MongoDB, Node.JS, Hive, among others.

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