Coinbase leverages Databricks to scale ML on blockchain data, turning vast transaction networks into actionable insights. This session explores how Databricks’ scalable infrastructure, powered by Delta Lake, enables real-time processing for ML applications like NFT floor price predictions. We’ll show how GraphFrames helps us analyze billion-node transaction graphs (e.g., Bitcoin) for clustering and fraud detection, uncovering structural patterns in blockchain data. But traditional graph analytics has limits. We’ll go further with Graph Neural Networks (GNNs) using Kumo AI, which learn from the transaction network itself rather than relying on hand-engineered features. By encoding relationships directly into the model, GNNs adapt to new fraud tactics, capturing subtle relationships that evolve over time. Join us to see how Coinbase is advancing blockchain ML with Databricks and deep learning on graphs.

In today’s fast-evolving crypto landscape, organizations require fast, reliable intelligence to manage risk, investigate financial crime, and stay ahead of evolving threats. In this session we will discover how Elliptic built a scalable, high-performance Data Intelligence Platform that delivers real-time, actionable Blockchain insights to their customers. We’ll walk you through some of the key components of the Elliptic Platform, including the Elliptic Entity Graph and our User-Facing Analytics. Our focus will be put on the evolution of our User-Facing Analytics capabilities, and specifically how components from the Databricks ecosystem such as Structured Streaming, Delta Lake, and SQL Warehouse have played a vital role. We’ll also share some of the optimizations we’ve made to our streaming jobs to maximize performance and ensure Data Completeness. Whether you’re looking to enhance your streaming capabilities, expand your knowledge of how crypto analytics works or simply discover novel approaches to data processing at scale, this session will provide concrete strategies and valuable lessons learned.

We’ll explore how CipherOwl Inc. constructed a near real-time, multi-chain data lakehouse to power anti-money laundering (AML) monitoring at a petabyte scale. We will walk through the end-to-end architecture, which integrates cutting-edge open-source technologies and AI-driven analytics to handle massive on-chain data volumes seamlessly. Off-chain intelligence complements this to meet rigorous AML requirements. At the core of our solution is ChainStorage, an OSS started by Coinbase that provides robust blockchain data ingestion and block-level serving. We enhanced it with Apache Spark™ and Arrow™, coupled for high-throughput processing and efficient data serialization, backed by Delta Lake and Kafka. For the serving layer, we employ StarRocks to deliver lightning-fast SQL analytics over vast datasets. Finally, our system incorporates machine learning and AI agents for continuous data curation and near real-time insights, which are crucial for tackling on-chain AML challenges.