udit mittal

• Wrote maintainable and extensible code in a team environment.
• Installation, configuration and administration of Big Data components (including Hadoop/Spark) for batch and real-time analytics and data hubs
• Capable of processing large sets of structured, semi-structured and unstructured data.
• Familiar with data architecture for designing data ingestion pipeline design
• Able to assess business rules, collaborate with stakeholders and perform source-to-target data mapping, design and review.

• Built, tested and deployed scalable, highly available and modular software products
• Streamlined deployment process by developing continuous integration tools.
• Designed and developed transactional and analytical data structures.
• Visual communicator – ability to convert and present data in an easy comprehensible visualization

Securing 9.09 CGPA.

Recipient of Best Rank Award Scholarship.

Securing 75% Percentile.

Information Technology coursework

The platform ingests and processes almost real time data published from global sites and provides valuable insights including real time alerts and exceptions, profiling and benchmarking against similar sites, potential savings to building managers. The insights are provided as a service by the marketing department of a fortune 500 company.

The infinitely scalable, infinitely flexible, distributed platform is built upon proven open source technologies including Apache Storm, Kafka, Tomcat, OrientDB (the document and graph DB) to name few and leverages R for Machine Learning. Javascript (primarily D3 and Angular) and Shiny Server (R) are used for visualization.

The product provides a unique flexible solution to one of the most critical and diverse planning problem faced by department store businesses - every business performs their planning and tracking in a way which best suits their differentiation and positioning to their customers.

With the Excel UI as front end; Node.js and MongoDB as backend the planning process is truly unique, quick and easy to change. The data warehouse and analytics framework sits on top of Apache Hadoop and utilizes Apache Drill and Hive for low latency querying. Azkaban provides the much needed scheduler capabilities.

The product actually consists of two separate systems, or sets of activities, which are operating at the same time. These are:

The service sequence - this is the system that is concerned with the delivery of the food and beverages to the customer.

The customer process - this is the system that is concerned with the process, or experience, that the customer undertakes.