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2.3.2. Streaming Data Processing (Kinesis Analytics, Spark Streaming)

First Principle: Streaming data processing fundamentally enables real-time transformations and aggregations of continuous data streams, providing immediate insights and updated features for real-time ML inference.

For ML applications that require immediate insights or continuously updated features, processing data streams in real-time is essential. This involves transforming data as it flows, rather than waiting for batches.

Key Concepts of Streaming Data Processing for ML:
  • Real-time Insights: Enables immediate analysis of incoming data for dashboards, anomaly detection, or real-time feature updates.
  • Continuous Transformation: Data is transformed on the fly as it arrives, rather than in scheduled batches.
  • Event-Driven: Processes individual events or micro-batches as they occur.
  • Latency: Critical consideration for how quickly data is processed and made available.
AWS Services for Streaming Data Processing in ML:
  • Amazon Kinesis Data Analytics: (Processes and analyzes streaming data.)
    • What it is: A serverless service for analyzing streaming data, supporting SQL or Apache Flink applications.
    • SQL Applications: Write standard SQL queries to analyze and transform data streams from Kinesis Data Streams or Kinesis Firehose and send results to various destinations.
    • Apache Flink Applications: Develop sophisticated stream processing applications using Java, Scala, or Python leveraging Flink's powerful capabilities for stateful computations and complex event processing.
    • Use Cases: Real-time dashboards, real-time anomaly detection, simple feature engineering for real-time inference.
  • Apache Spark Streaming on Amazon EMR:
    • What it is: Running Spark Streaming applications on a managed Amazon EMR cluster.
    • Use Cases: More complex, custom streaming ETL, advanced real-time feature engineering, integrating with other Spark libraries for ML algorithms. Offers greater flexibility and control than Kinesis Data Analytics SQL.
  • AWS Lambda: Can process records from Kinesis Data Streams or DynamoDB Streams for lightweight, event-driven transformations before storing or passing to other services.

Scenario: You need to calculate rolling averages of sensor readings from a Kinesis Data Stream in real-time. These averaged values will be used as features for an anomaly detection model that performs real-time inference.

Reflection Question: How do streaming data processing services (e.g., Kinesis Data Analytics with Flink for complex real-time transformations, Spark Streaming on EMR for more custom processing) fundamentally enable real-time transformations and aggregations of continuous data streams, providing immediate insights and updated features for real-time ML inference?