What will you learn in Data Engineering Foundations in Python Course
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Data engineering lifecycle & architecture: Master extraction, loading, transformation, orchestration, and data modeling across cloud‑based pipelines.
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Hands‑on tools & technologies: Work with Python, SQL, PySpark, Apache Kafka, Apache Airflow, and dbt to build end‑to‑end pipelines.
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Cloud data warehouse & ingestion skills: Learn GCP basics, dimensional modeling (Kimball), and ingestion patterns like CDC, API, batch, and streaming processes.
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Quality, orchestration, and automation: Configure data quality checks with schema validation (Avro/Protobuf), and orchestrate workflows using Airflow, Dagster, and dbt.
Program Overview
Module 1: Getting Started
~30 minutes
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Topics: Introduction to data engineering roles, team structures, and GCP setup.
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Hands‑on: Set up GCP and review data engineering lifecycle stages.
Module 2: Team Structures
~45 minutes
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Topics: Understand embedded vs centralized data teams and role responsibilities.
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Hands‑on: Quiz to assess team configuration strategies.
Module 3: Data Lifecycle & Cloud Arch
~1h 15m
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Topics: End‑to‑end lifecycle, data lakes, warehouses, architecture patterns (Lambda/Kappa).
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Hands‑on: Quiz on architecture models and data lifecycle checkpoints.
Module 4: Data Ingestion
~1h 30m
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Topics: Batch vs streaming ingestion, CDC, APIs, file systems, pandas/PySpark pipes.
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Hands‑on: Quizzes and code pads for ingestion pipelines.
Module 5: Data Modeling & SQL
~1h
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Topics: Dimensional modeling (Kimball), DDL/DML, SQL query lifecycle in BigQuery.
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Hands‑on: Solve SQL challenges in a BigQuery sandbox.
Module 6: Orchestration Tools
~1h 30m
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Topics: DAG design in Airflow, overview of Dagster and dbt.
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Hands‑on: Create full DAG pipelines and build dbt workflows.
Module 7: Data Quality
~45 minutes
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Topics: Schema validation, testing with Avro/Protobuf, dbt checks.
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Hands‑on: Setup and test quality pipelines with quizzes.
Module 8: Capstone & Epilogue
~30 minutes
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Topics: End‑to‑end Formula‑1 data pipeline, billing management, next steps.
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Hands‑on: Build capstone pipeline and review GCP billing setup.
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Job Outlook
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Core data engineering readiness: Ideal for Data Engineer, Data Pipeline Engineer, ETL Developer, and DataOps roles.
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In‑demand skill stack: Valuable across domains like finance, healthcare, marketing, and IoT.
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Hands‑on portfolio builder: Includes a built-from-scratch pipeline project suitable for resumes/GitHub.
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Language & tool relevance: Experience with Python, Spark, Kafka, Airflow, dbt, and GCP is highly sought.
Explore More Learning Paths
Take your data engineering and Python skills to the next level with these hand-picked programs designed to strengthen your expertise in building scalable data pipelines and managing big data workflows.
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Related Reading
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What Is Data Management? – Understand how effective data management underpins successful data engineering and ensures high-quality data workflows.
