Real-Time Analytics for Web3: Fraud Detection, Trading, and Growth at Scale скачать в хорошем качестве

Real-Time Analytics for Web3: Fraud Detection, Trading, and Growth at Scale

Web3 platforms face analytics challenges that traditional data stacks can’t solve. Anti-fraud and AML teams need second-level visibility, exchanges demand sub-second insights across millions of orders, and growth teams rely on real-time behavioral analytics to keep users engaged. Yet legacy solutions often lead to high costs, stale data, and performance bottlenecks at scale. 👉 Watch the webinar to learn how modern analytics engines are helping leading Web3 companies like Coinbase overcome these roadblocks through: 📌 Second-Level Data Freshness — Stream blockchain and market data with less than 10 s latency, no batch jobs, and no delays. 📌 Sub-Second Latency at Scale — Handle hundreds to thousands of concurrent queries on wallets, tokens, and orders with consistent performance. 📌 Zero ETL Overhead — Query Apache Iceberg and other open formats directly, with sub-second performance and no data copies. Learn from real-world Web3 use cases and get practical insights to tackle the industry’s biggest data challenges. Save your seat today. ---------------------------------------------------------------------------------------------------------------------- Timestamps 00:00 Intro & Agenda 01:29 Web3 and Its Use Cases 02:25 Web3 Anti-Fraud and Regulatory Tracing 04:35 Web3 Analytics for Growth and Monitoring 06:26 Technical Challenges for the Underlying Analytical Database 09:26 Architectural Approach to Real-Time Web3 Analytics 10:50 Meet StarRocks – Fastest Analytical Database for Real-Time, Lakehouse, and Agent Workloads 11:49 StarRocks Is Trusted by 500+ Enterprises Worldwide – Open Source Analytical Database for Real-Time, Lakehouse, and Agent Workloads 12:03 StarRocks – Real-Time Analytics 13:26 StarRocks – Data Lakehouse 14:26 How to Run Joins Fast Enough So You Don’t Need to Do Denormalization 14:45 Query Planning – Cost-Based Optimizer: The Key to Efficient Query Planning 16:35 Data Pruning – Global Runtime Filter 18:23 Compute Architecture – Architectures Tailored for Different Purposes 20:35 Efficient Query Execution – C++ SIMD Optimized 21:46 Eliminates Denormalization – SSB Benchmark Test: StarRocks vs. ClickHouse vs. Apache Druid (Out-of-the-Box) 23:06 Example: Demandbase – Challenges & Solution 25:22 Real-Time Mutable Data – Second-Level Data Freshness for All of Your Data 25:32 Delete-and-Insert Strategy – Primary Key Index Accelerated Upserts and Deletes 26:52 StarRocks in Production – Coinbase Blockchain Analytics 28:53 TRM Labs – Before and After 30:52 Running a Database Yourself Can Be Challenging... Enter CelerData Cloud (BYOC) Powered by StarRocks 32:26 Physical Isolation – Multi-Warehouse Architecture 33:47 Q&A Question 1 (34:19): What is the size of deployment in Coinbase, FECN, and what is the amount of data? Question 2 (34:35): Can StarRocks do joins between a native table with real-time ingestion and a lakehouse table? Question 3 (34:46): For StarRocks OSS, does it have a web UI to run queries? Question 4 (34:58): Does StarRocks support S3, Iceberg, or even AWS Glue? How is the performance? Do you have performance benchmarks? Question 5 (36:21): Assuming we only have Parquet files on S3, when are you going to release updates and deletes for Iceberg? Question 6 (37:37): What is the scale of nodes required to support multi-terabyte table sizes and join queries on Iceberg? ---------------------------------------------------------------------------------------------------------------------- Learn more at https://celerdata.com/ Connect with us: LinkedIn:   / celerdata   Twitter:   / celerdata   CelerData Website: https://celerdata.com/ StarRocks GitHub: https://github.com/StarRocks/StarRocks StarRocks Website: https://www.starrocks.io/ Slack: https://starrocks.io/redirecting-to-s... #DataAnalytics #DataEngineering #RealTimeAnalytics #RealTimeData #OLAP #DataAnalyst #DataEngineer #DataInfrastructure #userfacinganalytics #Database #cryptoanalytics #web3 #web3analytics