Build the Data Infrastructure That Powers AI Systems
From batch pipelines and Spark to real-time feature stores and data observability - the complete data engineering curriculum for AI engineers.
10 Modules. Full Data Stack.
From foundations to real-time systems. Every module links directly to the lessons.
Data Engineering Foundations
The modern data stack, pipeline patterns, data modelling for ML, storage formats, and Python tooling for data engineers.
What you'll master
- Data Engineering Landscape for AI
- Pipeline Patterns - ETL, ELT, Lambda, Kappa
- Data Modelling & Point-in-Time Joins
- Storage Formats - Parquet, Avro, Delta Lake
- Serialization & Schema Registry
- Python for Data Engineering
- Cost & Performance Trade-offs
7 lessons
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Batch Processing for ML
Apache Spark architecture, distributed joins, partitioning strategies, PySpark best practices, and dbt for ML pipelines.
What you'll master
- Apache Spark Architecture Deep Dive
- DataFrames, Datasets & Spark SQL
- Partitioning, Shuffling & Optimization
- Spark Joins - Broadcast, Sort-Merge, Skew
- PySpark Best Practices
- dbt for ML Data Pipelines
- Delta Lake & Lakehouse Patterns
- Spark on Kubernetes
8 lessons
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Stream Processing for Real-Time AI
Kafka architecture, Flink stateful processing, exactly-once semantics, and streaming patterns for real-time ML inference.
What you'll master
- Streaming Concepts & Event-Driven Architecture
- Apache Kafka Architecture & Internals
- Kafka Producers, Consumers & Consumer Groups
- Apache Flink - Stateful Stream Processing
- Windowing, Watermarks & Late Data
- Exactly-Once Semantics
- Stream Processing for ML Features
- Kafka Streams & ksqlDB
8 lessons
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Data Quality & Contracts
Data contracts, quality dimensions, Great Expectations, dbt tests, schema evolution, and ML-specific quality patterns.
What you'll master
- Data Quality Dimensions for ML
- Data Contracts - Schema, Freshness, Volume
- Great Expectations in Production
- dbt Tests & Data Validation
- Schema Evolution Strategies
- Monitoring Data Quality at Scale
- ML-Specific Quality Patterns
7 lessons
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Feature Stores
Why feature stores exist, Feast architecture, online vs offline stores, point-in-time retrieval, and production patterns.
What you'll master
- Why Feature Stores Exist
- Feast Architecture & Components
- Online vs Offline Feature Stores
- Point-in-Time Feature Retrieval
- Feature Engineering Pipelines
- Tecton, Hopsworks & Commercial Options
- Feature Store Production Patterns
7 lessons
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Data Lakehouse Architecture
Delta Lake, Apache Iceberg, Apache Hudi - ACID transactions, time travel, schema evolution, and when each table format wins.
What you'll master
- Data Lake vs Warehouse vs Lakehouse
- Delta Lake - ACID & Time Travel
- Apache Iceberg - Hidden Partitioning
- Apache Hudi - Incremental Processing
- Choosing a Table Format
- Compaction, Vacuuming & Maintenance
- Lakehouse Architecture for ML
7 lessons
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Pipeline Orchestration
Airflow architecture, DAG design patterns, Dagster, Prefect, dynamic pipelines, and orchestrating ML training workflows.
What you'll master
- Apache Airflow Architecture & DAGs
- Airflow Operators, Sensors & Hooks
- DAG Design Patterns for ML
- Dagster - Software-Defined Assets
- Prefect & Modern Orchestration
- Dynamic Pipelines & Parametrization
- Orchestrating ML Training & Inference
7 lessons
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Cloud Data Platforms
Snowflake, BigQuery, Redshift - architecture deep dives, cost optimization, and choosing the right platform for ML workloads.
What you'll master
- Snowflake for ML - Architecture & Optimization
- BigQuery - Internals & Cost Control
- Redshift - MPP & Spectrum
- Comparing Cloud Warehouses for ML
- Data Sharing & Marketplace Patterns
- Cost Optimization Across Platforms
6 lessons
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Data Observability
The five pillars of data observability, Monte Carlo, lineage tracking, anomaly detection on pipelines, and incident response.
What you'll master
- Five Pillars of Data Observability
- Data Lineage - Column-Level Tracking
- Anomaly Detection on Data Pipelines
- Monte Carlo & Observability Platforms
- Incident Response for Data Teams
- Building Custom Observability
6 lessons
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Real-Time Feature Engineering
Online vs offline features, low-latency serving, streaming feature pipelines, and real-time ML at Uber, DoorDash, and LinkedIn scale.
What you'll master
- Online vs Offline Features
- Low-Latency Feature Serving
- Streaming Feature Pipelines with Flink
- Redis & ScyllaDB for Feature Stores
- Point-in-Time Joins in Real Time
- Training-Serving Skew at Scale
- Real-Time ML at Uber & DoorDash Scale
7 lessons
Start for Free →
Build the data infrastructure your AI systems deserve.
From Spark and dbt to Kafka, feature stores, and real-time streaming - the complete data engineering curriculum for AI teams.
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