Data Engineering - Course #410

Build AI-ready data pipelines

Data Engineering is the backbone of analytics, machine learning and enterprise AI. This course teaches students to collect, clean, model, orchestrate, govern and serve data using modern cloud, lakehouse, streaming and DataOps practices.

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production_data_pipeline.yaml

Sources APIs, OLTP, files, events

Ingest Batch, CDC, streaming

Transform Spark, dbt, quality checks

Serve Warehouse, lakehouse, BI, AI

dag: retail_daily_pipeline

extract -> validate -> transform -> publish

checks: freshness, schema, nulls, duplicate keys

targets: Snowflake + Delta Lake + BI semantic layer

2026 Market Pulse

Why Data Engineering Is Trending Now

AI projects are only as good as their data foundations. Modern employers need engineers who can make data reliable, governed, fresh, discoverable and ready for analytics, machine learning and agentic AI workflows.

34% Projected growth

BLS projects related data scientist employment to grow much faster than average from 2024 to 2034.

$112K Median US pay

BLS reported $112,590 median annual pay for data scientists in May 2024, showing the premium on data skills.

75% Leadership priority

MIT Technology Review and Snowflake research reported that business leaders increasingly see data engineers as integral to company success.

61% AI project workload

Surveyed teams expect AI-related work to reach most of a data engineer workday within two years.

Market signals reflect 2025-2026 public labour data and industry research. Data Engineering is treated as the infrastructure side of the broader data and AI hiring market.

What You Master

Production Data Engineering Skills

The course is built around hands-on pipelines, not only theory. Students learn the workflow used by data teams in product, finance, healthcare, retail, SaaS and AI companies.

Data Modeling and SQL

Design normalized, dimensional and analytics-ready models with strong SQL foundations.

Python for Pipelines

Use Python for extraction, transformation, validation, APIs, file processing and automation.

Streaming and Real Time

Build event-driven pipelines with Kafka concepts, stream processing and freshness checks.

Cloud Warehouses

Work with Snowflake, BigQuery, Redshift and cloud storage patterns for scalable analytics.

Lakehouse Engineering

Understand Delta Lake, Databricks, Spark, bronze-silver-gold layers and table optimization.

Governance and DataOps

Implement lineage, testing, monitoring, access control, cost awareness and reliable releases.

Curriculum

Data Engineering Course Curriculum

A practical roadmap from SQL and Python foundations to cloud-scale pipelines, lakehouse architecture, streaming, governance and capstone delivery.

01. Data Engineering Foundations

Role of data engineers in analytics and AI teams
Batch vs streaming, ETL vs ELT, OLTP vs OLAP
Modern stack overview: source systems, pipelines, warehouse, lakehouse, BI and ML

02. Advanced SQL and Data Modeling

Joins, CTEs, windows, views and stored procedures
Star schema, snowflake schema and slowly changing dimensions
Performance tuning, partitions, indexes and query plans

03. Python for Data Pipelines

Files, APIs, JSON, CSV, Parquet and logging
Pandas and PySpark fundamentals for transformations
Reusable extraction and validation scripts

04. Data Warehousing and Analytics Engineering

Warehouse design, marts, semantic layers and BI-ready tables
dbt models, tests, snapshots, seeds and documentation
ELT workflows and version-controlled transformations

05. Apache Spark and Big Data Processing

Spark architecture, DataFrames, joins and aggregations
Partitioning, caching, shuffle, skew and optimization
Parquet, Delta tables and large-scale data processing

06. Orchestration with Airflow

DAG design, scheduling, dependencies and retries
Sensors, variables, connections and backfills
Operational patterns for reliable production pipelines

07. Streaming and Event Pipelines

Kafka topics, producers, consumers and partitions
Stream processing concepts, late data and idempotency
Real-time dashboards, alerts and CDC patterns

08. Cloud Data Platforms

Snowflake, BigQuery, Redshift and Databricks patterns
Object storage, compute separation, cost controls and IAM basics
Data lakehouse bronze, silver and gold architecture

09. Data Quality, Governance and DataOps

Schema checks, freshness checks, duplicates and anomaly detection
Lineage, catalogs, access control and privacy basics
CI/CD, monitoring, incident handling and documentation

10. Capstone Production Project

Design and build an end-to-end retail or fintech pipeline
Ingest batch plus streaming data into a warehouse/lakehouse
Publish dashboards, quality reports and deployment documentation

Roadmap

Learning Roadmap

FoundationsStrengthen SQL, Python, Linux basics, Git and data formats.

Warehouse thinkingModel data for reporting, build marts, write performant analytical queries.

Pipeline buildingExtract from APIs/files/databases, transform data and automate repeatable workflows.

Scale and orchestrationProcess larger datasets with Spark and schedule pipelines with Airflow.

Cloud and lakehouseDeploy warehouse and lakehouse patterns across cloud storage and compute.

Production readinessAdd tests, monitoring, lineage, documentation, cost controls and capstone evidence.

Tools Covered

Modern Data Engineering Stack

SQLPostgreSQL, MySQL, warehouse SQL

PythonAPIs, automation, Pandas

SparkPySpark, DataFrames, optimization

KafkaTopics, partitions, streaming

AirflowDAGs, schedules, retries

dbtModels, tests, docs, lineage

SnowflakeWarehouse and ELT workflows

DatabricksLakehouse and Delta tables

BigQueryServerless analytics patterns

AWS/Azure/GCPCloud storage and IAM basics

Docker and GitReproducible project delivery

Great ExpectationsData quality validation

Career Outcomes

Roles This Course Prepares For

Students build a portfolio that shows pipeline design, SQL modeling, orchestration, cloud data platform usage, data quality and production documentation.

Data EngineerBuild batch and streaming pipelines that power BI, analytics and AI products.

Analytics EngineerOwn dbt models, metrics layers, data marts, tests and business-ready datasets.

Cloud Data EngineerDesign scalable data workloads on AWS, Azure, Google Cloud, Snowflake or Databricks.

DataOps EngineerAutomate deployments, monitoring, data quality, lineage and pipeline reliability.

Streaming Data EngineerCreate event-driven systems for real-time dashboards, alerts and applications.