Designing a Real-Time Data Ingestion Pipeline

Nov 22, 2024

Lecture Notes: Creating a Data Pipeline for Near Real-Time Ingestion

Speaker Introduction

  • Name: Karthik
  • Role: Senior Manager, Financial Lead Data Engineer
  • Experience: Over 15 years in data engineering, specializing in big data and cloud engineering.
  • Tech Stack: Big data computational platforms (e.g., Spark), real-time stream processing (e.g., Kafka), microservices design.

Goal of the Lecture

  • Design a data pipeline for near real-time ingestion of Netflix, clickstream, or playback data.
  • Focus on ad hoc monitoring of certain metrics.

Clarifications and Objectives

  • Metrics: User engagement, playback data, customer churn, path analysis, and behavior profiling.
  • Generic vs. Specific Tools: Start with generic solutions, but specific technologies can be integrated.

Key Metrics and Use Cases

  • Customer Churn: Identify users at risk of churning.
  • Path Analysis: Identify and optimize navigation paths on the website.
  • Behavior Profiling: Assist in machine learning for recommendations.
  • Playback Data: Analyze session numbers, trending content, and user interaction (e.g., pauses).

Pipeline Design Overview

  • Segments: Data capture, streaming processing, storage, and analytics.
  • Data Capture Methods: Push vs. pull methods.
    • Push: Servers push data to data infrastructure.
    • Pull: Services poll for data at intervals.

High-Level Pipeline Design

  1. Data Capture
    • API Gateway for data collection from user interactions.
    • Pre-processing with tools like Kafka and Spark or alternative like Flink for sub-second latency.
  2. Data Streaming and Processing
    • Kafka for buffering and Spark Streaming for processing.
    • Consider Flink for millisecond latency needs.
  3. Data Storage
    • Data Lake: Store raw events for historical analysis.
    • NoSQL Database: For real-time insights (e.g., Amazon DynamoDB).
    • Relational Database: For analytical OLAP queries.
  4. Analytics
    • Tools like AWS Athena for querying data lakes directly.
    • Insights can be stored in Elasticsearch for user searches or displayed directly to users.

AWS-Specific Alternatives

  • Kinesis Streams: Alternative to Kafka for streaming.
  • Kinesis Analytics: Run streaming queries directly.
  • Kinesis Firehose: Buffer and store data in data lakes or warehouses.

Discussion Points

  • Scalability: System must handle traffic spikes and large-scale data.
  • Trade-offs: Different methods have various trade-offs regarding latency, cost, and complexity.
  • Challenges: Schema evolution, handling large-scale data, and ensuring fault tolerance.

Feedback and Reflection

  • Use of high-level design principles to guide technology choices.
  • Importance of explaining the rationale behind technology selection (e.g., NoSQL vs. SQL).
  • Consideration of potential bottlenecks and system fault tolerance.
  • Need to balance between discussing specific technologies and general principles.

Conclusion

  • The proposed pipeline design is flexible and scalable, capable of ingesting and processing large clickstream data.
  • There are multiple technological paths that can be tailored to specific business needs.

Full transcript