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
Where & when?

Enterprise Data Summit 2023 took place virtually on June 7th, 2023.

What is the cost to attend the virtual sessions?

EDS 2023 is always free and open for all to attend.

What is Enterprise Data Summit?

Data initiatives and priorities are often affected by resources, organizational structure, and data maturity, all of which can be different inside an enterprise-sized company or for teams processing data at scale.

Enterprise Data Summit is an annual day where leaders from large companies looking to succeed with large-scale data systems can explore ways to modernize their data platforms, enable engineering best practices, allocate resources efficiently, and wrangle topics like data observability.

Who comes to EDS?

Data, analytics, AI, data science, and other team and department leadership, engineers and administrators, FinOps, and risk/compliance officers.

Join us for talks that include:
  • Guiding enterprise-level data teams
  • Modernizing data platforms
  • Cloud financial management
  • Succeeding with large-scale data systems
  • Data reliability
  • Resource allocation
  • Use cases for data observability
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