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Module 03 · Page 4 of 20 · 9 sections

Data Pipeline and Data Engineering

How data moves from sources through ingestion, processing and storage to the models that consume it.

01Introduction to AI System Design04Model Training Pipeline15MLOps and LLMOps
3.1

What is a Data Pipeline?

  • A series of steps to collect, process, transform and store data from various sources for AI/ML applications.
  • It ensures the right data is available in the right format at the right time.
  • It includes ingestion, preprocessing, transformation, storage and monitoring.
3.2

Why is it Important?

  • Good quality data is critical for training and evaluating AI models.
  • Handles large and diverse data sources (databases, files, APIs, streaming data).
  • Ensures data is clean, consistent and reliable.
  • Supports scalability, automation and reproducibility.
  • Improves model performance and reduces manual effort.
3.3

Typical Data Pipeline Architecture

Data SourcesDatabases, files, APIs, streaming
Data IngestionBatch and real-time (Kafka, APIs)
Data ProcessingCleaning, transformation, features, validation
Data StorageData lake, database
Data ConsumptionTraining, serving, analytics, dashboards
3.4

Data Sources

1
Databases
MySQL, PostgreSQL, MongoDB (structured data).
2
Files
CSV, JSON, Parquet (batch data).
3
APIs
REST APIs, third-party services.
4
Streaming Data
Kafka, Kinesis, real-time logs.
5
Other Sources
Documents, images, audio, video.
3.5

Data Ingestion

1
Batch Ingestion
Load data periodically (e.g. daily CSV files).
2
Real-time Ingestion
Stream data in real time (e.g. using Kafka, APIs).
3
Formats
Handle different data formats (JSON, CSV, Avro).
4
Reliability
Ensure data reliability with retry mechanisms and error handling.
3.6

Data Processing and Transformation

1
Data Cleaning
Handle missing values, duplicates and outliers.
2
Data Transformation
Convert data to the required format (normalization, encoding).
3
Feature Engineering
Create useful features for model training.
4
Data Validation
Check data quality and schema consistency.
3.7

Data Storage

1
Data Lake
Store raw data (e.g. S3, HDFS).
2
Database
Store processed data (e.g. MySQL, MongoDB, PostgreSQL).
3
Data Warehouse
For analytics and reporting (e.g. Snowflake, BigQuery).
4
Choosing
Choose storage based on data size, access pattern and cost.
3.8

Common Tools

ToolUsed for
Apache KafkaReal-time data ingestion
Apache AirflowPipeline orchestration
SparkLarge-scale data processing
AWS S3Data lake storage
DBTData transformation
ElasticsearchSearch and indexing
3.9

Best Practices

  1. 1Automate data pipelines (use Airflow).
  2. 2Monitor pipeline health and set up alerts.
  3. 3Ensure data quality with validation checks.
  4. 4Use scalable storage and processing tools.
  5. 5Handle failures with retry and backup mechanisms.
  6. 6Maintain data lineage and proper documentation.