DES is a property of AIM Media House.
DES is a property of AIM Media House.
By adopting a lakehouse architecture, BigBasket transformed its data ecosystem into a unified, cost-efficient, and near real-time platform built for scale and reliability. The lakehouse enables trusted and well-governed, end-to-end data lifecycle management—from streaming ingestion to governed analytics—shifting operations from D-1 reporting to near real-time decision-making. Built on principles of interoperability and incremental evolution, this foundation powers critical use cases such as on-time delivery, inventory optimization, personalization, and smart store intelligence—creating a future-ready, data-to-action platform that supports 4x business growth.
Every enterprise is racing to deploy AI agents. Very few have asked the harder question: can your data infrastructure actually support them?In this session, we’ll explore what the agentic era actually demands from a data platform — and why most existing architectures hit a wall before they get there. We’ll walk through the architecture of the Agentic Data Stack — how ClickHouse unifies OLTP, real-time analytics, observability, and agentic AI workloads on a single platform — and why eliminating the old trade-offs between speed and scale, simplicity and volume is no longer optional. It’s the baseline.
AI prototypes might take minutes to develop yet to make them enterprise-grade requires a lot more thought, rigour, process, and tooling investments. In this talk, we will explore the key building blocks to design, develop, deploy, and operate production-worthy AI use cases for the enterprise. We’ll look at how to think about the quality attributes, guard-rails, observability, and many critical elements that will be essential for your AI projects to go from prototypes to production-worthy investments.
As GenAI & Agenitc systems are increasingly built on complex data pipelines, real‑time platforms, and shared data products, traditional governance models struggle to keep pace. For data engineering leaders, the challenge is no longer limited to data quality or access controls—it is about ownership across pipelines, platforms, and AI outputs. This session examines how modern data engineering organizations are redefining accountability, control, and trust for GenAI‑driven systems. Focusing on platform design, data contracts, lineage, observability, and privacy‑by‑design, the talk presents leadership‑level patterns that embed governance directly into data and AI pipelines for Enterprise scale—enabling scale, reliability, and responsible AI adoption without slowing down engineering velocity.
Enterprises are moving beyond traditional data foundations toward real-time, AI-ready ecosystems. This session explores how to modernize data architectures to enable faster insights and intelligent decision-making to solve business use cases at scale.
Many data platforms fail to deliver impact due to fragmented data and lack of clear business alignment. This session focuses on what truly moves the needle, use-case-driven strategies, better data quality, and integrated ecosystems that deliver real outcomes.
As audio data grows exponentially, building scalable storage systems becomes critical for efficient search and retrieval. This session will explore architectures and strategies to handle high-volume audio data while ensuring performance, reliability, and cost efficiency.
As AI rapidly reshapes how organizations build intelligence, this session explores what it truly means to go beyond the algorithm. Drawing from real enterprise experience at one of India’s most iconic consumer brands, the talk traces the evolution from classical analytics and ML to multimodal GenAI and agent‑driven decision systems. Using the end‑to‑end product lifecycle as a lens from design and manufacturing to go‑to‑market and continuous intelligence – it demonstrates how AI delivers impact only when grounded in strong data foundations, business context, and governance. The session also reflects on the changing role of data scientists into AI orchestrators, highlighting why system thinking, judgement, and business storytelling matter more than ever in the age of AI.
What if your data pipeline could generate user intelligence before a single real user touches your product? This session introduces Synthetic Users – AI agent swarms that simulate real human behaviour at scale and the data pipeline architecture that transforms their interactions into actionable product intelligence in hours, not months.
