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Giving 1.2 billion citizens a unique identity.

Aadhaar, an Indian government initiative aims to provide a unique identity for every citizen. This involves managing, authenticating and reporting staggering amounts of data that requires a robust and scalable information management platform.

Here’s how we helped the Indian government manage this complex and critical initiative successfully.

The challenge

The Unique Identification Authority of India (UIDAI), envisioned a dedicated12-digit number as a unique identity for every citizen of the country. The number would be stored in a centralized database and linked to the basic demographics and biometric information (photograph, ten fingerprints and iris), of every individual. UIDAI needed a partner to design a credible mechanism for identity establishment, authentication, storage and retrieval. The system needed to meet the following objectives:

  • Scalability to withstand enormous data volumes and throughput needs
  • Availability of information through Business Intelligence (BI) systems and portals to be used by diverse users such as the public, UIDAI and academicians
  • Building a data warehouse that has zero knowledge of the resident, thereby ensuring security of data
  • Flexibility to store data at the most atomic level, ensuring data aggregation at any level for reporting
  • Usage of open source technologies and tools to avoid vendor lock-in

Our solution

Mindtree was chosen by UIDAI as the primary application development partner. We collaborated with UIDAI and devised a solution strategy that comprised the following:

  • A data warehouse to capture all enrolment related information except PII (Personal Identifiable Information) data of over 1.2 billion residents of India
  • Solution design to effectively manage 8 peta bytes of raw data
  • An efficient data management system to handle over 100 million database transactions per day
  • Asynchronous data acquisition from transactional systems
  • Dataset generation and distribution platform enabling consumption by portals and dashboards, data marts and analysis tools
  • Use of technologies that leverage data locality, which support distributed processing for dataset generation

Our approach

  • Designed a layered architecture for flexibility and scalability of data collection and information dissemination
  • Used big data open-source technologies such as Hadoop, Hive, Pig to handle the scale
  • Developed MySQL-based Operational Data Store (ODS) to cater to operational reporting
  • Asynchronous data acquisition from transactional systems using an event publish / subscribe framework
    - The framework uses message queues to publish data to the data warehouse without impacting the enrolment process
    - The framework is also used to publish data on authentications

Business impact

  • Scalable architecture to handle ten billion enrolments
  • Easy monitoring of the enrolment process through dashboards and customized reports
  • Informed decision making through a ‘self-service’ portal with relevant metrics and custom reports
  • Prevented potential fraud cases through formidable checks at the enrolment stage