vikrant Sikarwar

Modernizing Enterprise Data & Analytics in the Cloud

Abstract:

Enterprises modernizing legacy analytics environments often face fragmented data estates, brittle ETL pipelines, and slow insight cycles that limit business agility. This session presents a practical blueprint for cloud modernization and analytics migration at enterprise scale, based on an enterprise implementation on Azure.

The session outlines a metadata-driven medallion architecture (Bronze/Silver/Gold) using Azure Data Factory, ADLS Gen2, Databricks, and Synapse to standardize ingestion, transformation, governance, and consumption across domains. Attendees will learn how a framework-first approach can reduce custom pipeline development, improve delivery velocity, and strengthen trust in reporting through embedded data quality controls, lineage, and auditability.

The talk also covers domain-driven modeling for enterprise reporting, migration patterns for legacy BI platforms, and operational practices for performance, scalability, and cost optimization. Implementation outcomes—including migration of dashboards/reports/extracts, retirement of major legacy data assets, and measurable gains in delivery speed and reliability—will be shared, along with actionable lessons, anti-patterns to avoid, and a phased roadmap that teams can adapt to their own modernization journeys.

Profile:

Vikrant Sikarwar is a Cloud Data Architect and Principal Data Engineer with nearly 20 years of experience leading enterprise data, cloud, and analytics transformation programs. His work focuses on modernizing legacy ecosystems into scalable, cloud-native platforms that improve agility, governance, and decision intelligence.

He has led end-to-end initiatives including legacy BI/reporting decommissioning, cloud migration, enterprise data modeling, real-time and batch ingestion frameworks, and multi-region analytics standardization. His core expertise includes Azure, Databricks, Spark, big data engineering, SQL, Python, Power BI, and metadata-driven data engineering practices that support reliable and AI/ML-ready analytics at scale.

Vikrant combines deep technical leadership with practical execution, helping organizations reduce technical debt while accelerating insight delivery and business value. He is also an active contributor to the academic and professional community, with published technical papers available on Google Scholar. In parallel, he has served as a technical reviewer and judge for IEEE journals and global conferences, where he assesses research and engineering submissions for originality, technical rigor, scalability, and practical relevance.

His sessions are known for being implementation-focused, offering audiences clear frameworks, migration patterns, and lessons learned from real enterprise transformation journeys.