Coforge's Data Cosmos DataFlux - Intelligent Scriptless Ingestion

DataFlux is Coforge's enterprise-grade, AI-powered data ingestion solution artifact engineered to simplify, automate, and govern large-scale data movement on Azure. As a dynamic zero-code solution, DataFlux empowers data engineering teams to manage complex ingestion workflows without programming expertise, dramatically reducing time-to-value for data platform initiatives.

The platform features six autonomous AI agents that drive end-to-end automation: • Orion – Zero-code pipeline generation that auto-creates production-ready Python ingestion pipelines • Vega – Live schema drift detection and DDL reconciliation to maintain data integrity • Andromeda – DAG orchestration and scheduling with dependency and concurrency control • Phoenix – Intelligent failure analysis and auto-recovery to minimize downtime • Sirius – AI-generated data quality rules scoring datasets across six quality dimensions • Atlas – Audit, lineage, and compliance reporting for full governance traceability

DataFlux supports batch, CDC (Change Data Capture), and real-time streaming ingestion modes. It connects to diverse source systems including RDBMS (Oracle, SQL Server, MySQL, PostgreSQL), NoSQL databases, cloud warehouses (Snowflake, BigQuery, Redshift), and streaming platforms (Kafka). Data can be onboarded into into Azure Data Lake Storage, Azure Synapse Analytics, Databricks Delta Lake, Fabric Lakehouse, or other target systems.

Key Use Cases: • Real-Time Analytics Pipelines – Stream Kafka data into Databricks Delta Lake with watermark-based ingestion • Legacy System Consolidation – Unify multi-RDBMS/NoSQL data into centralized cloud platforms • CDC-Based Operational Replication – Near real-time replication from Oracle/SQL Server to cloud warehouses • AI-Driven Data Quality Audits – Auto-generate DQ rules and score datasets continuously

The 8-week implementation engagement covers: discovery and source profiling, platform deployment on Azure, configuration of ingestion pipelines for priority data sources, AI agent activation, data quality rule setup, monitoring dashboard configuration, and knowledge transfer.

Target Audience: Enterprise Data Engineering & Platform Teams, Data Architects, Cloud Migration Leads, Data Quality & Governance Teams, and DevOps & Platform Engineers.