Data Integration Services

Large and scattered data needs combined and relevant homogeneity to give a unidirectional view. Welkyn simplifies data by assimilating it from myriad sources and provides meaningful information. The setbacks due to data explosion, expensive maintenance of multiple sources, multiple records are unravelled. The data assets of the enterprise is understood, modified, long-term data integration is implemented such that the data is consistent with clarity and beneficial to the enterprise. Data Integration with high-performance systems enhances productivity and reduces cost delivering reliable data from a scattered sources.

The data Extraction, Transformation and Loading (ETL) is above par and different data formats are brought under one application making accessibility very easy and complete where you can access all at once.


Welkyn Data Integration Services include

  1. Maintenance and Sustenance of ETL Applications – An engagement model providing best SLA based responses at competitive cost.
  2. Data Integration Architecture Definitions And Consulting – Aligning Data Integration technology to easily manage high data volumes and complexities.
  3. Data Integration Enablement for Emerging Business Cases includes high volume batch services, real-time services, unstructured data, appliance solutions, optimizing data integration infrastructure.
  4. Data Integration Capability in the Cloud – Moving Data Integration capability to the cloud and support scenarios such as data exchanges between on-premise and cloud based solutions.
  1. Data Integration Competency Centre - Standard Data Integration across business verticals and reducing lead time to implement Data Integration Solutions. The Overall Total Cost of Ownership (TCO) is reduced through higher consistency and efficiencies of scale.
  2. Performance Optimization, Technology Migration, Data Integration Administration– High efficiency in Data Integration Management, in alignment with business, optimal hardware, software, optimized costs and people.
  3. Data Governance – Creating and Organizing a Data Governance model for protecting and managing you data assets, increasing revenue, improving quality, accuracy and consistency of information and to control costs.
  4. Meta Data Management – Increasing Standardization, reuse of data assets and simplifying it to maintain specific metadata.




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