DEData Engineering

ETL PipelinesApache KafkadbtApache Spark

Learn data engineering to build scalable data pipelines, streaming systems, and analytics platforms. From SQL to Kafka, dbt, Spark, and Airflow.

Free Tutorial

Learn Data Engineering - Pipelines, Streaming, and Analytics

Master data engineering from fundamentals to production. Learn to build ETL/ELT pipelines, stream data with Apache Kafka, transform with dbt, process at scale with Apache Spark, model data for analytics, orchestrate with Airflow, and architect modern lakehouses.

Prerequisites

Before learning data engineering, you should have a solid foundation in SQL and basic Python programming. Familiarity with relational databases is strongly recommended.

PostgreSQL Tutorial System Design Tutorial

What You'll Learn

✓ Data engineering fundamentals & lifecycle
✓ ETL/ELT pipeline design patterns
✓ Apache Kafka & stream processing
✓ Apache Spark for distributed processing
✓ Data modeling & dimensional design
✓ dbt for SQL transformations & testing
✓ Advanced SQL: window functions & CTEs
✓ Data quality & governance practices
✓ Apache Airflow orchestration
✓ Lakehouse architecture with Delta Lake

Course Topics

Lesson 1

Beginner

15 min

Data Engineering Fundamentals

Understand the role of data engineering, core concepts, and how data flows through modern organizations

ETL vs ELT: Data Integration Patterns

Compare Extract-Transform-Load and Extract-Load-Transform approaches, their trade-offs, and when to use each

Data Warehouses vs Data Lakes

Compare data warehouses, data lakes, and the emerging lakehouse architecture to choose the right storage strategy

Batch vs Stream Processing

Understand the differences between batch and stream processing, when to use each, and how to combine them in modern data architectures

Apache Kafka Fundamentals

Learn Kafka's architecture, core concepts like topics, partitions, and brokers, and understand how distributed event streaming works

Kafka Producers and Consumers

Build robust Kafka producers and consumers in Python, handle serialization, consumer groups, offset management, and error handling

Kafka Streams and Stream Processing

Build real-time stream processing applications with Kafka Streams, including stateful operations, windowing, and joins

Event Streaming Patterns

Master event sourcing, CQRS, CDC, and other essential patterns for building event-driven data architectures

Learn Spark's architecture, DataFrames, transformations, and actions for large-scale distributed data processing

Data Pipeline Design Patterns

Learn how to design reliable, scalable, and maintainable data pipelines with proper error handling, idempotency, and monitoring

Data Modeling Fundamentals

Learn the principles of data modeling for analytics, including normalization, denormalization, and choosing the right model for your use case

Master the star schema design pattern with fact tables, dimension tables, and grain definition for analytics-optimized data warehouses

Slowly Changing Dimensions (SCD)

Learn SCD Type 1, 2, and 3 strategies for tracking historical changes in dimension tables

Learn dbt (data build tool) for SQL-based transformations, project structure, materialization strategies, and the transformation workflow

dbt Models, Tests, and CI/CD

Build production-grade dbt projects with incremental models, custom tests, macros, packages, and CI/CD pipelines

Advanced SQL: Window Functions

Master SQL window functions including ROW_NUMBER, RANK, LAG/LEAD, running totals, and moving averages for analytical queries

Advanced SQL: CTEs and Recursive Queries

Master Common Table Expressions, recursive CTEs for hierarchical data, and advanced query composition patterns

Data Quality and Testing

Implement comprehensive data quality frameworks with automated testing, anomaly detection, and data observability

Implement data governance practices including access control, data cataloging, lineage tracking, privacy compliance, and organizational policies

Data Orchestration with Apache Airflow

Build and manage data pipeline workflows with Apache Airflow, including DAG design, operators, scheduling, and monitoring

Build real-time analytics systems with streaming ingestion, materialized views, OLAP engines, and live dashboards

Lakehouse Architecture

Understand the lakehouse paradigm combining data lake storage with warehouse capabilities using Delta Lake and Apache Iceberg

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Frequently Asked Questions

What is data engineering?

Data engineering is the discipline of designing, building, and maintaining the infrastructure and systems that collect, store, process, and serve data at scale. Data engineers build ETL/ELT pipelines, streaming systems, data warehouses, and data lakes that enable analytics, machine learning, and business intelligence across organizations.

What is the difference between ETL and ELT?

ETL (Extract, Transform, Load) transforms data before loading it into the destination, which was common with traditional data warehouses that had limited compute. ELT (Extract, Load, Transform) loads raw data first and transforms it in the destination using modern cloud data warehouses like Snowflake or BigQuery. ELT is now preferred because it preserves raw data and leverages the scalable compute of modern platforms.

What is Apache Kafka used for?

Apache Kafka is a distributed event streaming platform used for building real-time data pipelines and streaming applications. It handles high-throughput, fault-tolerant messaging between systems, enabling use cases like event-driven architectures, log aggregation, real-time analytics, change data capture (CDC), and microservice communication. Kafka can process millions of events per second with low latency.

What skills do data engineers need?

Data engineers need proficiency in SQL for data manipulation, Python or Scala for building pipelines, and knowledge of tools like Apache Kafka, Apache Spark, dbt, and Airflow. Understanding data modeling, dimensional design, data warehousing concepts, and cloud platforms (AWS, GCP, Azure) is essential. Strong skills in data quality, testing, and governance round out a complete data engineering toolkit.

Ready to Learn Data Engineering?

Begin your data engineering journey with the fundamentals. You'll learn what data engineering is, the data lifecycle, and the essential skills for building data systems.

Start Learning Data Engineering →