3.1.5. Task 3.5: Determine High-Performing Data Ingestion and Transformation Solutions

💡 First Principle: High-performing data ingestion and transformation is about the efficient data flow: rapidly and reliably moving raw data into a system and preparing it for analysis.

This task explores key AWS services and patterns for optimizing data pipelines. You'll delve into various data ingestion patterns, understanding how to choose between batch and real-time approaches based on data velocity and volume. Concepts like streaming data with services such as Kinesis and MSK are critical for high-velocity scenarios. Effective data transformation strategies, whether ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform), are examined for preparing data for consumption. The role of data lakes (e.g., S3) as scalable, cost-effective repositories for raw and processed data is also central.

This section emphasizes applying optimization techniques to design resilient and performant data pipelines, aligning with the SAA-C03 exam's focus on comprehension and practical application.

Scenario: You are designing a system to process customer clickstream data from a high-traffic website. The data arrives continuously and needs to be analyzed in real-time, and later transformed for batch analytics.

Visual: High-Performing Data Ingestion & Transformation

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⚠️ Common Pitfall: Using a batch ingestion method for real-time streaming data, leading to unacceptable latency in analytics.

Key Trade-Offs:

• Real-time vs. Batch: Real-time ingestion offers low latency for immediate insights but is generally more complex and expensive. Batch ingestion is simpler and more cost-effective for large volumes of data that don't require immediate processing.

Reflection Question: How does the volume, velocity, and variety of your data influence your choice of ingestion (real-time vs. batch) and transformation services (e.g., Kinesis, Glue) to build a high-performing data pipeline?