Session: Ensure cloud data platform migration success using data observability

Pushpa Ramachandran, General Manager & Head - Strategy, Alliances, Platforms & Solutions  @ Wipro

Modernizing data platforms to address issues with scalability and performance, security, growing cost, reliability of data, and support for advanced analytics and AI workloads has become a multi-phase, multi-year journey.

Data observability has emerged as a more effective way to help modernize data platforms away from Hadoop/proprietary Cloudera ecosystem to an open source platform or to cloud native platforms such as Snowflake, Databricks, and Redshift.

In Pushpa's talk, you will see how data observability addresses key modernization challenges:

  • De-risking data platform migrations with data reconciliation and pipeline performance
  • Automating, data validation, and troubleshooting data and performance issues
  • Avoiding unexpected overruns and determining optimal workload placement
What is the cost to attend the virtual sessions?

EDS is always free and open for all to attend.

What is Enterprise Data Summit?

Enterprise data teams are moving from dashboards and reports to powering AI agents, real time decisions, and mission critical products. At large scale, that work is shaped by platform choices, governance, cost pressure, and the realities of operating complex data systems inside large organizations.

Enterprise Data Summit is a focused day for leaders who are modernizing AI ready data platforms. It is where enterprise data engineers, platform and analytics leaders, AI and ML teams, and partners in finance, risk, and security compare notes on how to ship reliable data and AI systems at scale.

Who comes to EDS?

  • Data and analytics engineering

  • Data platform and architecture

  • AI, ML, and LLM platforms

  • FinOps and cloud cost management

  • Data governance, security, and risk or compliance

Join us for talks that include:

  • Designing AI and agent ready data platforms, including lakehouse, streaming, and vector or retrieval layers

  • Using generative AI to accelerate data work while keeping quality, lineage, and controls in place

  • Data and AI governance as the control plane for trust, access, and regulatory compliance

  • FinOps for data and AI, including unit economics, chargeback models, and practical cost controls

  • Operating models for enterprise data teams, from platform teams to data products and self service

  • Data and AI reliability and observability in production, including real incidents and lessons learned

  • Hybrid and multi cloud data strategies, sovereignty, and regionalization in regulated environments

  • Moving from AI pilots to an enterprise AI platform backed by durable data foundations

Interested in speaking at the next Enterprise Data Summit or supporting the event as a sponsor?

Please submit your talk topic here or reach out to astronaut@solutionmonday.com.

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