使用 Apache Pinot 和 Apache Flink 进行实时分析
Realtime Analytics with Apache Pinot and Apache Flink
- 1. Introduction
- 1. Course Introduction and Outcome
- 2. What is Realtime Analytics and Why is it Needed
- 2. Practical Business Use Case
- 1. Realtime Analytics for an Online Cab Service Company
- 2. Architecture and Data Model for Realtime Analytics
- 3. Data Ingestion with Apache Kafka
- 1. A Quick Introduction to Apache Kafka
- 4. Apache Kafka Setup Hands On
- 5. Data Production and Consumption from Apache Kafka Hands On
- 4. Realtime Data Processing with Apache Flink
- 1. Flink Basic Concepts
- 2. Using the Table API to Read from Kafka Hands On
- 3. Using Flink SQL to perform Transformations Hands On
- 4. Checkpoints and Watermarks
- 6. Regular Joins
- 7. Temporal Joins
- 9. Setup MySQL for Location data Hands On
- 10. Using Event Time Temporal Joins and Lookup Joins to Enrich Ride Data Hands On
- 11. Running Flink on a Cluster Hands On
- 5. Apache Pinot for Realtime Analytics
- 1. A Quick Introduction to Apache Pinot
- 2. Apache Pinot Setup Hands On
- 4. Realtime Ingestion in Apache Pinot Hands On
- 5. Upserts in Pinot
- 6. Realtime Upserts for Enriched Ride Data in Apache Pinot Hands On
- 7. Running Realtime Analytics Queries in Pinot Data Explorer Hands On
- 8. Querying Data through Pinot REST API
- 6. Creating Dashboards with Apache Superset
- 1. A Quick Introduction to Apache Superset
- 2. Setting up Apache Superset Hands On
- 4. Connecting Superset with Apache Pinot Hands On
- 5. Creating Realtime Analytics Dashboards in Superset
- 7. Summary
- 1. Congratulations
- 8. Bonus Section
- 1. Bonus Lecture