Data Pipeline Architecture for AI Intermediate

Data is the foundation of every AI system. Without reliable, scalable data pipelines, even the most sophisticated models will fail. This lesson covers how to architect data ingestion, transformation, storage, and feature engineering systems that feed production ML workloads.

Data Architecture Components

• Ingestion layer - Batch and streaming data collection from diverse sources
• Data lake - Centralized storage with schema-on-read for raw and processed data
• Data warehouse - Structured analytics data for feature engineering and reporting
• Feature store - Centralized repository for computed ML features with online and offline serving
• Data versioning - Track dataset versions for reproducibility and lineage

Feature Store Architecture

Component	Purpose	Technology Options
Offline Store	Historical features for training	S3/GCS + Parquet, BigQuery, Redshift
Online Store	Low-latency features for inference	Redis, DynamoDB, Bigtable
Feature Registry	Feature discovery and metadata	Feast, Tecton, SageMaker Feature Store
Materialization	Sync features from offline to online	Spark jobs, Dataflow, Flink

Streaming vs Batch Pipelines

Most AI systems need both streaming and batch data pipelines:

• Batch pipelines - Process large historical datasets for training. Run hourly or daily. Use Spark, Dataflow, or Glue.
• Streaming pipelines - Process real-time events for online features. Use Kafka + Flink or Kinesis + Lambda.
• Lambda architecture - Combine batch and streaming for both historical completeness and real-time freshness.
• Kappa architecture - Stream-only approach where all data is processed as a stream, simplifying the architecture.

Data Quality and Validation

Data quality issues are the leading cause of model degradation in production:

• Schema validation - Enforce data schemas at ingestion to catch structural issues early
• Statistical validation - Monitor feature distributions and alert on drift using tools like Great Expectations
• Freshness monitoring - Track data arrival times and alert on delays that affect model performance
• Completeness checks - Verify expected data volumes and flag missing partitions