How L&T Finance is strengthening its data foundation to support scalable, reliable AI use cases—focusing on data quality, governance, and real-time accessibility.

This talk will introduce hybrid search with Apache Doris as a next-generation retrieval solution for generative AI and context engineering. Addressing the semantic confusion problem by combining vector search, full-text search, and SQL to capture the exact-match intent with semantic similarity, resulting in a more accurate and cost-effective solution. Matt will then touch on how the native real-time capability that Apache Doris brings to OLAP can be extended to real-time RAG, helping organizations think about future challenges in this space.

RisingWave is redefining real-time data processing with its next-gen streaming database architecture. This session explores how organizations can move beyond batch systems to build low-latency, scalable pipelines that power instant insights thereby reducing operational complexity of traditional stream processing stacks

Traditional data lakes often struggle to deliver reliable and actionable insights at scale. This session shares how redBus evolved to a modern lakehouse architecture, built a unified knowledge layer for context and governance, and enabled AI-powered, natural language data access—transforming its data platform into a real-time, decision-centric system.

Traditional data pipelines were designed to move and transform data efficiently—but in today’s AI-driven world, that is no longer enough. This session explores the evolution from DataOps to AI-native pipelines, where data systems are not just enablers but active participants in intelligence generation. We will dive into how modern architectures integrate real-time data processing, feature engineering, model deployment, and feedback loops into a unified AI pipeline. Attendees will gain insights into designing scalable, reliable, and cost-efficient systems using technologies like Spark, Kafka, and cloud-native platforms.

In high-scale platform ecosystems- where a single backend powers diverse business verticals , the traditional boundaries between "Application Engineering" and "Data Engineering" are dissolving. When processing millions of transactions daily, data integrity cannot be a post-facto batch process; it must be an architectural first principle. This session explores the "Truth Platform" pattern, a blueprint for building near real-time (NRT) reconciliation and observability into the core of a multi-tenant business platform. We will move away from the "data as exhaust" mindset and dive into three transformative engineering patterns: Cohorted Observability, Recon as a contract and Handshake Pattern

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.

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!