The Fifth Elephant 2026 Annual Conference
Built for humans. Now rebuilding for agents.
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Nilesh Mahajan Presenter Breaking the Trilemma: Serverless Data Platform in Your Own Cloud AccountDescription If you run data workloads, you’ve probably hit the same wall we did. You want compute that stays in your own cloud account, runs fast, and stays cheap — and sooner or later, someone tells you to pick two. This is a hard problem we solved for our own platform, and this talk shares those learnings around the engineering it takes to get all three. more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: 30 mins talk
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Alex Campos Workshop facilitator Streaming Meets the Lakehouse: Hands-on with Fluss and IcebergAbstract: In this hands-on workshop, attendees will build a Streaming Lakehouse using Apache Fluss and Apache Iceberg. Learn to ingest real-time streams, store them in Fluss tables, and transparently tier data into Iceberg for long-term analytics. Through guided exercises, you’ll define Flink SQL pipelines, enable Fluss tiering to Iceberg, and run real-time and historical queries. By the end, you… more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: Hands-on workshop - 2-4 hours
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Dhruv Nigam  Your voice agent is (probably) doomed, OR how not to fall victim to outdated voice agent playbooksGoogly Bhai was busy this IPL season. He live-streamed to an audience of 300,000 every day, with 1.5 million minutes of watch time and 1.1 million concurrent viewers at peak. Hundreds of other streamers called him onto their streams to discuss live scores, gossip, and make predictions (see him live jamming with another streamer). He switched to Aussie and British accents mid-conversation at the a… more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 30 mins talk
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Vivek Kalyanarangan Senior Technical Architect - AI Tech at IDfy Beyond GPUs: Cutting ML Inference Costs by 10× Without Sacrificing LatencyInference cost-to-serve is usually treated as a fixed tax: the model needs a GPU, the GPU costs what it costs, and the bill scales with traffic. It isn’t fixed. For a large class of production models — embeddings, CNNs, classic CV and NLP — quantization plus graph fusion turns that GPU tax into a variable you control, cutting cost-to-serve by ~10× at the same latency, throughput, and accuracy env… more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 30 mins talk
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Shivam Gupta Discover Globally, Materialize Locally: Building a Governed Cross-Domain Data Sharing PlatformCross-business-unit data sharing usually starts with good intentions and quickly turns into ticket-driven exports, undocumented copies, governance bottlenecks, and growing compliance risk. At InMobi, multiple business units operate independent lakehouses, catalogs, and data engineering organizations. Combining data across these domains creates significant business value, but traditional approache… more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: 30 mins talk
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Sujeet Gholap Architecting Observability Platform on S3 + LambdaObservability is a Data Engineering problem. Write-heavy, realtime latency, faster queries are the typical requirements of a Observability system. Traditional observability systems fail to meet the ever-growing demand of increased volumes due to cloud deployments, AI agents speeding up the feature and product development. AI agents also changes the query patterns which were common in Observabilit… more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: 30 mins talk
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Fenil Jain Composable Query Engines: breaking down query engines to rebuild themDescription Query engines are amongst the most interesting pieces of software, they span from frontend to the lowest layers of software inside hardware! They have their own compiler, graph theory applications, truly distributed systems, low level kernel and even hardware, you name it and there’s a variant present. But this has also meant, teams working on these behemoths have to be really good at… more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: 30 mins talk
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Harshad Nawathe The Practical Guide to Reverse-Engineering XXL Codebases with Agentic AIAbstract Point an AI coding agent at an unfamiliar codebase and ask it to explain the architecture, and for a few hundred files, it works beautifully. Point the same agent - with the same well-engineered prompt - at a ten-thousand-file enterprise Java monolith, and it quietly falls apart: the context window fills, compaction kicks in, the model starts forgetting details, and then it starts invent… more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 30 mins talk
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Sathish Presenter AIOps: Leveraging AI for Software IncidentsDescription During production incidents or on-call schedules with a barrage of alerts, engineers must sift through hundreds or thousands of services, code changes, metrics data points, logs, and traces to reason about the issue and find the root cause. more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 30 mins talk
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Kalpesh Jajoo Founding Solutions Architect, APAC at Skyflow Don’t Block AI: Handling Sensitive Data and DPDP While Preserving ContextDescription Enterprise AI is forcing organizations to rethink one of the most fundamental assumptions in data security. For decades, security strategies have focused on protecting data where it is stored. Encryption, tokenization, access controls, and governance were designed for applications that queried databases and presented information to users. Generative AI changes that model completely. T… more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: 30 mins talk
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Abhijith Neerkaje Workshop instructor AI evals workshopOverview Why do Agents make mistakes - 3 Gulfs [Comprehension, Specification and Generalization]. (10 min) more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: Hands-on workshop - 2-4 hours
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Rajath 
Lakshmi Narayana G From Files to CatalogsWorkshop: From Files to Catalogs — Modern Data Foundations with Parquet, Iceberg, and Polaris more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: Hands-on workshop - 2-4 hours
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Sourav Bhuwalka Building Realtime CDC and Fabric Mirroring(Streaming data) at scale : Solving Replication Lag and Schema Evolution in Real-Time Data Platforms{Describe your session in 2 paragraphs Real-time analytics platforms promise fresh data without complex ETL pipelines, but operating them at cloud scale introduces a very different set of challenges. In this talk, we share lessons learned while building Microsoft Fabric Mirroring, a system that continuously replicates Azure SQL Database workloads into OneLake with near real-time latency eliminati… more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: 30 mins talk
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Amit Prabhu Full Refresh to Incremental: Rebuilding Denormalization for Reporting at Razorpay ScaleAbstract / Session Description: At Razorpay, our lakehouse platform ingests over 6 billion events daily and powers a reporting platform that generates close to a million reports every month. As scale grew, full-refresh denormalization became unsustainable: joins across 10-30 entities consumed heavy compute, report freshness lagged by up to 48 hours, and highly mutating datasets made derived table… more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: 30 mins talk
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