Real-Time Data Pipeline Design for Clickstream

Oct 10, 2024

Data Pipeline for Near Real-Time Ingestion of Clickstream or Playback Data

Introduction

  • Speaker: Karthik, Senior Manager, Lead Data Engineer with 15 years of experience.
  • Expertise: Designing, building, and optimizing large-scale data pipelines in big data and cloud engineering.

Question Overview

  • Task: Create a data pipeline for near real-time ingestion of Netflix clickstream or playback data, focusing on ad hoc monitoring of metrics.

Clarification Questions

  • Metrics of Interest:
    • User engagement metrics, playback metrics, customer churn, navigation paths, and behavior profiling.
    • Importance of understanding user behaviors for product insights and machine learning applications.

Metrics Discussion

  • Key Metrics:
    • Customer Churn: Monitoring user activity to prevent loss of users (e.g., those who stop visiting).
    • Path Analysis: Understanding navigation flow and identifying potential obstacles in user journey (e.g., number of clicks to reach a target).
    • Playback Data: Analyzing streaming activity including number of sessions, pauses, and engagement levels with content.

Pipeline Design Overview

  • Pipeline Segments:
    • Data Capture
    • Streaming Processing
    • Storage and Analytics

Data Capture Methods

  • Push Model: Servers push data to the data infrastructure.
  • Pull Model: Services pull data from applications.
  • Trade-offs:
    • Push can overwhelm infrastructure; Pull may introduce latency.

High-Level Design

  1. Data Collection:
    • Users generate clickstream events while interacting with the application (e.g., Netflix).
    • API Gateway: Exposes endpoints for data ingestion.
  2. Event Processing:
    • Use of Kafka as a streaming platform to buffer events.
    • Optional: Use Flink for millisecond-level latency processing.
  3. Data Storage:
    • Data Lake: Organized in layers (raw, processed, access) for historical analysis and ease of querying.
    • NoSQL Database: For real-time access and performance, particularly for high event throughput.
  4. Analytics:
    • Utilize tools like AWS Athena for direct queries on the Data Lake.
    • Consider Redshift for data warehousing.

Additional Technologies Discussed

  • AWS Kinesis: An alternative to Kafka for stream processing, suited for clickstream data.
  • Firehose: To buffer and load data into data lakes periodically, ensuring data availability for historical analysis.

Challenges and Considerations

  • Cost Factors: Balance between performance and costs.
  • Schema Evolution: Importance of flexibility in data modeling, particularly with NoSQL databases.
  • Potential Bottlenecks: Need for fault tolerance and scalability in high-throughput scenarios.

Conclusion

  • Overall, the design focused on flexibility, scalability, and real-time processing to effectively handle and analyze clickstream and playback data for Netflix.
  • Suggested areas for future exploration include deeper discussions on trade-offs, bottlenecks, and fault tolerance in the pipeline.