Presented by:

Amit Parikh

Quest Software

With nearly four decades in IT, I’ve evolved from hands-on systems administration and software development to leading strategic enterprise transformations as Field CTO at Quest Software.

I blend deep technical expertise with business strategy to help organizations modernize data platforms, operationalize observability, and prepare for AI-driven operations. My background spans presales and post-sales consulting, product and project management, and enterprise IT delivery—equipping me to advise both technical and business executives with clarity, credibility, and measurable outcomes.

Today, I partner with global enterprises, industry leaders, and internal stakeholders to:

  • Build AI-ready data platforms rooted in governance and trust

  • Enable observability for analytics and ML workloads

  • Improve data performance, quality, and resilience across hybrid/cloud architectures

  • Leverage metadata intelligence and lineage to drive compliance and business insight

I share these perspectives as a frequent speaker at global conferences, podcasts, and user communities with focus areas including:

  • Data Observability & Governance

  • AI/ML Workload Readiness

  • Cloud-Native Architectures & RDBMS Strategy

  • Metadata Intelligence & Data Lineage

Outside of tech, I’m an ICC-certified cricket coach and umpire—mentoring players with the same passion and discipline I bring to guiding organizations toward resilient, data-driven innovation.

“Cricket is life. Life is cricket.”

Let’s connect—always happy to talk data, AI, observability, or cricket.

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As AI projects move from proof-of-concept to production, organizations face a critical challenge: modernizing data architectures in ways that enable innovation without compromising governance, trust, or performance. This session presents a practical framework for evaluating today’s most common architectural patterns—data lakehouse, data mesh, and data fabric—through the lens of AI readiness. We’ll explore the key trade-offs each approach brings and examine how metadata, lineage, and data modeling play a pivotal role in reducing risk and accelerating AI adoption. You’ll also gain insight into hybrid data movement strategies—including replication, streaming, and virtualization—and how observability can help you detect performance bottlenecks before they derail your initiatives. Drawing on real-world modernization scenarios that include integrating legacy platforms with cloud-native systems like Snowflake and Databricks, this session equips you with actionable strategies to build a future-ready architecture that scales with your AI ambitions.

Date:
2026 April 23 11:00 PDT
Duration:
50 min
Room:
San Pedro (Level C)
Conference:
Postgres Conference: 2026
Language:
Track:
Essentials
Difficulty:
Easy