Explore AI-powered streaming analytics for real-time customer experience in this 39-minute conference talk. Learn how to sense, reason, and act on customer data using Spark Structured Streaming. Discover techniques for capturing event data, combining it with existing information for context, and responding appropriately in real-time. Gain insights into an end-to-end solution that leverages AI and metadata to automate data management processes and guide user behavior. Understand how to unify fast-lane data streaming and batch processing to deliver in-the-moment actions that enhance customer interactions. Examine case studies from media, entertainment, and financial services industries, and learn about event-centric data processing methodologies. Delve into the technical aspects of Spark Structured Streaming, including its advantages over Dstream processing and state store maintenance.
Overview
Syllabus
Intro
Streaming Data Management
Intelligent Streaming Analytics
Case Study: Multi-latency data management at a large global Media & Entertainment company Objective Enhance the customer experience with chatbot on mobile app in cloud
Case Study. Streaming Analytics at Ovo, a leading Indonesian financial services platform Objective: Create better customer engagement with targeted real-time campaigns
Event-centric Data Processing - Methodology The value of most events is multiplied by the context
Customer Expectations
Informatica Enterprise Streaming and Ingestion
Cloud Mass Ingestion Streaming Service
Decipher Data With CLAIRE
Intelligent Structure Discovery in Action
Spark Structured Streaming-Overview
Motivation for Structured Streaming
Spark Structured Streaming Support
Spark Structured Streaming vs Dstream
Processing with Dstream
Processing with Structured Streaming State Store maintained by Spark
Enterprise Streaming & Ingestion - Reference Architecture
Demo Scenario
Summary
Taught by
Databricks
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