Submitted to : Supratik GhatakSubmitted by: Manmeet Kaur ChathaDivision: CRoll No: 14PRN: 15020441139

Process followed for the Project

The above project was assigned by the professor and was well researched on the Internet

After careful analysis of the topic assigned, the company (Opera Solutions) was approached and permission to conduct interview was obtained through email

The questionnaire was prepared for the above topic and emailed to the Interviewer. On the receipt of confirmation of interview, telephonic interview was conducted for the purpose of completion of the assignment.

Opera Solutions

World leader in Big Data & Analytics

Incorporates science & technology to provide

solutions to their clients

Analyse their client’s future behaviour and suggest strategies keeping in consideration their

customer base

Solutions offered by Opera Solutions

TravelCapital Markets

Telecom & Helathcare

Consumer Banking

Spend Analytics

How is Opera Solutions Managed?

Opera solutions is managed by a specialised Software Team The Software Team looks into the development as well as constant improvements in the software The software is usually hosted on the servers which are located in the US office

Who are your Clients?

(The identity of the clients is confidential)

largest pharmacy chain from US

largest cinema chains of UK

biggest credit card companies in US

Is there any standard industrial process/model in use by Opera

Solutions?Opera Solutions is managed by Data Rush Platform (DRP)

DRP is used to build Proprietary Signal Hub Platform (SHP)

SHP is able to process big data using Hadoop efficiently

Why Signal Hub Platform?

It aids the businesses to enhance their ability to create customised and personalized contacts with their clients.

It further aids the businesses to serve their customers efficiently and save on costs. It is faster and accessible.

It also aids in the development of Bid Data Analytics in an organization.

Components of Signal Hub Platform

APIs (Application Programming

Interface)

Analytics engine

Signal Generator & Management

System

ETL (Extract, transform &

Load) Processing

Data Filter & Mapper

How the above model has been implemented?

The model has been implemented in YAML Language in order to deliver quicker, faster and accurate solutions to the client's complex problems

How the implementation of the above model has benefitted Opera

Solutions?

They are able to market themselves uniquely

It has improved visibility and alignment with their clients ensuring cordial client relationships,

responsive to their needs and solutions requirement.

Through the software, they are able to generate quick & meaningful insights for the clients

What modifications have been effected by Opera Solutions in the

model it has implemented?

Most of the clients have enormous amount of data so they have integrated various techniques like parallel processing

The parallel processing incorporates Hadoop clusters to make it more faster

They have also tries to automate the process so that the data that comes on a periodic basis does not require manual intervention

Does the model used by Opera Solutions responsive to changes in market dynamics?

the software used by the company is continuously

upgraded to meet the ever evolving client requirements

How the process of up-gradation is done and how often it is

done?

Usually the development team looks into the constant feedback of the product and make the changes to improve the software

The changes might be adding more functionality, bug fixes, improving performance etc. We usually release a new version of the software quarterly.

How is quality and operational efficiency ensured in your current

model in practice?•The software is tested by a special testing team which tests the system in stress scenarios

•Further the development team looks into the quality management and improvement

•The efficiency of the system is aimed to maintain and managed continuously.

What tools are used for testing the model in practice?

The server performance is constantly monitored by the IT team checking the spikes in CPU and RAM usage.

The output of the software is verified by comparing the results with other standard big data tools like SAS, Python etc.