Real-time Intelligence on Google Cloud | Real-time Data Summit 2024

Learn about the latest developments in BigQuery, Google Cloud's data analytics platform and the advantages of using multiple engines in BigQuery, BigQuery Studio, and Gemini AI assistance. Try Aerospike Cloud for free: https://auth.control.aerospike.cloud/?utm_medium=organic-social&utm_source=youtube In this session, Maruti C., Partner Engineering Lead at Google Cloud, discusses the latest BigQuery advancements that are transforming how organizations approach data analytics, real-time processing, and AI integration. Whether you're a data engineer, analyst, or ML practitioner, this talk will equip you with powerful tools and strategies to elevate your cloud data workflows. 🔍 What You'll Learn in This Video: - BigQuery Multi-Engine Support: Work seamlessly with SQL, Python, and Spark, all on a single copy of your data. - BigQuery Studio + Gemini AI: Use a unified IDE for SQL and Python with built-in AI-assisted workflows. - Python DataFrames in BigQuery: Experience Pandas-like syntax with the power and scalability of BigQuery. - Integrated ML & AI with Vertex AI: Build, train, and deploy ML models directly on BigQuery using BQML and foundation models via Gemini. - AI Governance with DataPlex: Centralized metadata, lineage, and quality monitoring across your data assets. - Open Table Format Support: Natively use Iceberg, Delta Lake, and Hudi in BigQuery for lakehouse architecture. - BigLake Integration: Unify data lakes and data warehouses through a single storage engine. - Real-Time Streaming with Pub/Sub & Dataflow: Build serverless pipelines using Apache Beam for streaming analytics. - Apache Kafka for BigQuery: Secure, managed event streaming now integrated into BigQuery. - Continuous Queries: Process streaming data in real time using always-on SQL queries with zero scheduling delays. ⚙️ Technologies Covered: - BigQuery Studio - Gemini AI (Google Cloud) - Vertex AI + Agent Builder - BigQuery ML (BQML) - DataPlex (AI governance) - Apache Kafka for GCP - Dataflow & Pub/Sub - BigLake Storage Engine - Continuous Queries - Python (DataFrames) - Apache Spark (Serverless Spark on BigQuery) 💡 Use Cases Explored: - Generative AI on top of BigQuery - Unified analytics pipelines for batch and streaming data - Event-driven applications with real-time enrichment - Low-code/No-code agent building for search and conversational AI - Reverse ETL and real-time anomaly detection 🌐 Why It Matters: BigQuery is no longer just a data warehouse, it's a complete data-to-AI platform. From multi-language development to lakehouse support, and from streaming ingestion to generative AI, this session shows how Google Cloud’s data stack is purpose-built for modern enterprise needs. Explore Aerospike: https://www.aerospike.com Aerospike Database https://aerospike.com/products/database/ Aerospike Documentation Guide https://aerospike.com/docs/ Build with Aerospike https://aerospike.com/docs/develop/ #BigQuery #GoogleCloud #DataAnalytics