In a world where AI can build pipelines and infer schemas, the role of the data engineer is undergoing a fundamental shift. The skills that once defined the profession are being automated and that raises an important question: where does a data engineer's value truly lie? Join this session to explore how a data engineer’s role is not being replaced, but rather elevated. Understand how you can move beyond manual execution and step into higher-impact roles, shaping business outcomes, driving adoption, and applying the human judgment that no model can match.

Creating a single source of truth for customer data is no longer optional. This session will dive into the strategies, architectures, and governance models needed to integrate fragmented data systems into a unified ecosystem that powers seamless experiences and data-driven growth.

Every enterprise is rushing to deploy AI. Few have fixed the foundation underneath it. Despite years of investment in data platforms, most enterprise AI systems still hallucinate, produce inconsistent outputs, and fail to reason reliably. The problem is not the model — it is a structural gap between how data platforms were built and what AI agents actually need. This session defines the AI Readiness Gap, diagnoses where enterprises are falling short across six critical dimensions and introduces the Knowledge Layer — the most overlooked component in modern data architecture that bridges raw data assets with AI agent cognition.

Every enterprise wants AI, and most have more raw material than they realize. Industry research by IDC shows that while over 80% of enterprise content is unstructured, less than 1% of it is currently ready for AI consumption. This gap is what we call the "Context Crisis", which is the difference between data that's stored and data that's truly seen. Closing it is what turns models that guess into models that know, and agents that assist into agents that can be trusted with real decisions. This keynote argues that AI reliability is a data engineering problem before it is ever a model problem. Drawing from real-world failure patterns, like dark data, broken lineage, missing metadata, unversioned documents, and more, we'll explore how modern DataOps disciplines turn this crisis into a competitive edge. From intelligent ingestion and semantic layers to data products and governance as code, we'll walk through the engineering blueprint that transforms fragmented documents and unversioned information into the contextual fuel agentic AI demands.

Today’s data stacks are complex and fragile. AI is changing that. This talk explores how AI can automate pipelines, improve data quality, and create a more reliable, self-healing data ecosystem, offering a glimpse into the future of AI-powered data platforms.

This session explores how generative AI is reshaping product discovery into a more intuitive, predictive, and user-centric experience.

India’s DPDP Act is redefining how organizations handle personal data, making privacy and governance non-negotiable. Enterprises must move beyond compliance checkboxes to embed privacy into the core of their data architecture. And have to do it by May 2027. Skyflow enables this shift by isolating sensitive data, enforcing strong governance, and minimizing exposure. This foundation allows AI and analytics to run on protected data without compromising user trust. The result: a secure, compliant, and scalable AI data stack built for India’s privacy-first future.

As organizations rush to layer AI on top of their data warehouses, most are discovering an uncomfortable truth. AI-driven analytics is only as trustworthy as the metadata underneath it. Without lineage, ownership, and semantic context, even the best models confidently produce wrong answers, and no one can tell until a dashboard lies to an executive. In this talk, I'll share how we built a data catalog that serves as the backbone of our AI-driven analysis infrastructure. We'll walk through the pillars that make it work: end-to-end lineage from source systems to dashboards, blast-radius analysis that prevents silent breakage, documentation and accountability that AI agents can actually reason over, and a metrics layer that turns all of this into reliable, explainable answers. You'll leave with a concrete blueprint for why catalogs aren't documentation tools, they're infrastructure, and why getting this foundation right is the difference between AI that hallucinates and AI that works.

In this session, we’ll explore how large analytics enterprises can evolve from traditional data systems to AI-driven, modular architectures. The talk introduces five engineering pillars—Data Foundations, AI Agent Building (including persona-based “ask” capabilities), Agent Catalog & Monitoring, Internal Agent Marketplace, and Business Integration. Attendees will learn how data engineers can design semantic layers, build reusable AI agents for merchandising and customer analytics, and integrate them into live business workflows. The session highlights practical strategies for measuring impact, optimizing AI performance, and creating scalable solutions that drive measurable business outcomes.

What does data governance look like when your biggest data consumer doesn't sleep, doesn't follow SOPs, and makes thousands of decisions every second? When a majority of your users bypass official AI tools entirely? When a single ungoverned prompt can leak training data, customer PII, or regulated information across your entire enterprise? These aren't hypothetical questions — they're the operational reality of GenAI in 2026. And the governance frameworks most enterprises rely on were built for an era when data consumers were human, deterministic, and slow. This talk presents DataGovOps: governance as code, dynamic access policies, AI-assisted classification, and end-to-end lineage from source through agent output. We'll explore why legacy governance fails under agentic workloads, how to architect consent and access as programmable policy, the recursion problem of governing AI with AI, and practical patterns for staying compliant under regimes like GDPR & DPDPA — without strangling the velocity that makes AI worth deploying in the first place.

In the era of Generative AI and autonomous decisioning, enterprises are rapidly investing in AI models. However, the reality remains stark most AI initiatives fail not due to model limitations, but due to fragmented, unreliable, and non-AI-ready data ecosystems. This session challenges the conventional narrative of AI being model-centric and repositions data as the true foundation of AI success.

In this high-impact session, Anurag Chaudhuri explores how modern enterprises are evolving from traditional data pipelines to intelligent, agent-driven systems that can autonomously make and execute business decisions. Using relatable e-commerce scenarios, the talk breaks down complex concepts like Agentic AI, decision automation, and system orchestration into simple, practical insights. Attendees will learn how organizations can move beyond dashboards and alerts to build systems that not only understand data but act on it in real time, driving faster decisions, improved customer experiences, and measurable business outcomes. This session offers a clear, future-focused perspective on how Agentic Operating Systems are redefining the way businesses operate at scale.

Your warehouse got you this far. Your lake promised flexibility. But the future lives in between — the lakehouse. This workshop tears apart the modern data stack and rebuilds it. You’ll go deep on Delta Lake, Apache Iceberg, ACID transactions, and schema evolution at scale. You’ll discover why semantic layers are quietly becoming the most important piece of the puzzle — turning data chaos into self-service analytics teams actually trust. And because no data platform in 2026 is complete without AI, we’ll design architectures where feature stores, ML pipelines, and analytics don’t just coexist — they reinforce each other. Bring your SQL skills and curiosity. Leave with a blueprint you can use on Monday.

Your pipelines work — until they don’t. And when they break at 2 AM, nobody knows why. This workshop fixes that. You’ll learn how to bring real engineering discipline to data — CI/CD, automated testing, version control, and observability applied to pipelines the way software teams have done it for years. We’ll get hands-on with orchestration frameworks like Airflow and Dagster, build data quality checks that catch issues before your stakeholders do, and implement lineage tracking so you always know what went wrong and where. We’ll also connect the dots to LLMOps and ML pipelines, because reliable AI starts with reliable data. Bring your Python or SQL chops. Leave with pipelines that don’t page you at midnight.

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

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.

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.

Every enterprise is building something expensive. Lakehouses, real-time pipelines, RAG systems, agentic workflows — the data stack has never been more sophisticated, or more costly or more easier to build. Yet ask most data engineering teams what their platform costs per insight, per model inference, or per AI decision and you will struggle to find the right answer. With AI advancing almost quarter by quarter, the next competitive advantage in data engineering is not a better pipeline framework or a smarter vector store — it is engineering economics and AI economics. The ability to measure, attribute, and optimise the cost of intelligence at scale. Whether you are a data engineer designing pipelines, a platform lead managing infrastructure, or head of AI/ Data / Analytics justifying AI investment to the board — this session will give you a concrete language for the conversation that every data team is avoiding: what does our intelligence layer actually cost, and is it worth it? Target audience: Data Engineers, Platform Leads, CDOs, Head of Data Category tags: FinOps, Cost Engineering, AI Infrastructure, Platform Economics Key takeaway: A reusable framework for unit economics on data + AI platforms

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.

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!

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.

This session covers how organizations can use Generative AI in CI/CD pipelines while maintaining the right balance between fast delivery, strong security, and cost efficiency. It will highlight simple ways to scale GenAI in production without compromising performance or control.