Day 2 | HALL 2 - Practical Insights and Best Practices

AI Agents for Data Platforms: Discover, Model, Analyze
Building AI-ready data platforms involves transforming traditional data systems into intelligent, self-service ecosystems powered by automation and AI. The foundation lies in creating an automated data catalog that continuously ingests, organizes, and enriches metadata, enabling better data discovery, governance, and contextual understanding through platforms

Complementing this, AI-driven data modeling agents automate the identification of fact and dimension tables, generate optimized analytical schemas, and improve performance through intelligent recommendations. Together, these components create a unified, feedback-driven architecture that accelerates analytics, reduces manual effort, and enables scalable, AI-powered decision-making across the organization.

On top of this, self-service analytics using Text-to-SQL empowers business users to interact with data using natural language, leveraging advanced LLMs such as Qwen, Gemini, and OpenAI GPT models to generate accurate, context-aware queries.

A story of taming fragmented, unstructured healthcare data to unlock scalable AI and measurable patient impact.

Modern data stacks often promise a simple idea: a single query layer that can unify access across databases, data lakes, and SaaS sources. Engines like Trino make this vision technically feasible—allowing teams to query MySQL, MongoDB, S3-based lakehouses, and even spreadsheets through one interface.

This talk shares a practitioner’s perspective on what it actually takes to make that promise work inside a regulated fintech environment, where data residency, auditability, and access control are not optional. Drawing from real-world experience building a federated query layer over heterogeneous systems, it explores the gap between architectural elegance and operational reality.

The session will highlight challenges that emerge at scale: inconsistent semantics across sources, unpredictable query performance, governance complexities with role-based access, and the risk of turning federation into “distributed chaos.” It will also cover the practical guardrails required to make such a system usable—data contracts, curated layers, controlled abstractions, and user-focused enhancements like custom functions.

Rather than presenting federation as a silver bullet, this talk reframes it as a powerful but disciplined capability. Attendees will leave with a clearer understanding of when a unified query layer accelerates data access—and when it quietly amplifies complexity.

In a world of always-on connectivity, telecom systems generate massive streams of real-time data every second. This session explores how modern data platforms process, analyze, and act on this data instantly, powering everything from network optimization to personalized customer experiences at scale

DataOps gave us insights. DecisionOps makes them execute. The goal has changed from data to decisions & AI-native platforms are accelerating decision-making across the value chain. Let us hear about how is that advancing clinical research, transporting life saving medicines on time & saving lives!

Most startups bolt on privacy later. Designing systems where PII is isolated, tokenised, or abstracted early. This can include schema design, access control layers, and service boundaries.

As organizations transition to AI-first models, building scalable and reliable data pipelines becomes critical. This session explores how real-time data platforms enable faster decision-making, seamless data flow, and robust AI deployment at scale.

As data ecosystems evolve, building and operating reliable, maintainable, and scalable data pipelines becomes increasingly complex. This session introduces a modern shift in data engineering: a zero-code ETL platform where users define what the pipeline should do, and data engineers define how the platform should handle its execution at scale. It essentially abstracts pipeline complexities behind an intuitive UI and then standardised configurations.

We extend this architecture with an LLM-powered segmentation layer on top of the data warehouse, turning raw data into actionable insights. It converts high-level user intent into SQL queries and downstream pipelines, allowing business users to run experiments on their own—without depending on engineering teams or facing bottlenecks.

Zero-code ETL: users see magic, engineers maintain the illusion.