APPROACH

We have built our reputation on the understanding that the more data you can gather the better quality solution you can produce. The volume of structured, unstructured and semi-structured data is accelerating at a blistering pace including – all types of new and traditional data, complex data that now comes from sources such as blogs, Facebook, E-mails, internet search, and sensor data/logs.

Our Approach in implementing successful projects on BI & Big Data Analytics answers these questions and is used in developing the solution approach:
- Where and When do I make data a strategic advantage?
- Why do I need Big Data analytics?
- What tools do I really need to achieve this?
- How do I get there?


Our delivery methodology uses the Waterfall or SDLC approach; to build a Data Warehouse and Big Data Analytics Solution for your organization keeping in mind the organizational goals along with people, processes and technology considerations.

The first step will be to identify and sign-on on all the requirements and priorities for the project across the organization. Subsequently, we will identify the modules with the highest value add or return on investments to the organization. The project plan for further phases namely - analyze, design, build, test and deploy - will be planned across modules to achieve highest realization of value for the effort in the quickest time possible subject to Budget, Quality and Timeline restrictions.

In the case of incremental BI and Big Data Analytics requirements, an Agile methodology may be preferred depending on the solution approach signed off. A solution approach that is not too invasive across the BI solution layers is candidate for implementing under an Agile approach to ensure rapid development and reduced project costs.

TOOLS

We have the experience and expertise to help you with your Enterprise Systems Integration, Business Intelligence & Big Data Analytics needs. With the right tool of choice our Data Architects can help you design your Data Warehouse, Marts, Cubes, and Analytics systems

  • Installation and Configuration of Standard BI Integration tools including Big Data tools such as Cloudera, Hortonworks, and other Hadoop distributions
  • Data processing using Python-Pandas, Hive, Sqoop, Pig, and Spark
  • Development using Java, Python, and Scala
  • Integration with Amazon Web Services (AWS) and Microsoft Azure environments
  • NoSQL processing with MongoDB, HBase, and others
  • Relational analysis with Redshift, Vertica, Teradata and other standard SQL tools
  • Analytics with Spark, R, Python, and others
  • Relational analysis with Redshift, Vertica, Teradata and other standard SQL tools
  • Visualization and analysis with Tableau, Datameer, Alpine Data Labs, and others