Intelligent CDC Pipelines & AI-Driven Automation for Enterprise Data Platforms

Modern AI and analytics systems depend on high-quality, real-time data—yet most enterprises struggle with scaling Change Data Capture and maintaining accuracy across complex source systems such as SAP. This session presents practical engineering patterns for building intelligent, automated CDC pipelines at enterprise scale. Drawing from experience implementing SAP SLT → cloud CDC frameworks at Walmart, I will cover schema drift detection, automated SQL generation, lineage tracking, data quality checks, auditability, workflow orchestration, and AI-assisted pipeline optimization. Attendees will gain actionable insights into designing resilient, scalable, and AI-enhanced data architectures that support real-time analytics and machine learning workloads.
Who is this presentation for?
Data engineers, cloud architects, AI practitioners, analytics leads, platform engineers, and anyone building large-scale data pipelines for analytics or machine learning.
Prerequisite knowledge:
Basic understanding of data engineering, ETL/ELT concepts, SQL, and cloud data platforms. Familiarity with CDC or analytics pipelines is helpful but not required.
What you'll learn?
How to design scalable CDC pipelines for enterprise data systems How to automate ingestion, schema management, and SQL generation Techniques for data quality checks, lineage, and auditability How AI/ML can optimize pipeline performance and anomaly detection Architectural patterns for real-time analytics and AI workloads

Avinash Tripathi is a Senior Data and Software Engineer specializing in large-scale data platforms, intelligent CDC pipelines, and real-time analytics systems. With more than seven years of experience across Walmart, Amazon, and Deloitte, he has designed enterprise-grade SAP SLT CDC ingestion frameworks, automated SQL generation systems, BigQuery modernization architectures, and CI/CD-driven data quality automation. His work focuses on building resilient cloud-native pipelines, improving auditability and governance, and leveraging ML to optimize data engineering. Avinash brings deep hands-on experience in designing data systems that support high-volume analytics and AI workloads across Fortune 50 organizations.