What will you learn in Learn Data Engineering Course
Understand the full data engineering lifecycle from ingestion to analytics.
Work with key tools like Kafka, Airflow, Spark, and Snowflake.
Design and build data pipelines using batch and streaming methods.
Handle data transformation, warehousing, and orchestration in real-world scenarios.
Build foundational skills for modern data stacks and cloud-based data workflows.
Program Overview
Module 1: Introduction to Data Engineering
⏳ 1.5 hours
Topics: What is data engineering, role in the data team, lifecycle overview.
Hands-on: Identify components of a modern data stack and project workflow.
Module 2: Ingestion Layer
⏳ 2.5 hours
Topics: Batch vs. streaming ingestion, Kafka basics, file sources, APIs.
Hands-on: Simulate ingestion using Kafka and flat files.
Module 3: Transformation Layer
⏳ 2.5 hours
Topics: Data cleaning, enrichment, ETL vs. ELT, SQL and Python tools.
Hands-on: Build basic transformation logic using pandas and SQL.
Module 4: Orchestration with Airflow
⏳ 2 hours
Topics: DAGs, scheduling, monitoring, retries, dependencies.
Hands-on: Set up and deploy a basic Airflow DAG.
Module 5: Storage and Warehousing
⏳ 2 hours
Topics: Columnar vs. row-based storage, warehouse concepts, intro to Snowflake.
Hands-on: Load data into a Snowflake warehouse and query using SQL.
Module 6: Processing with Spark
⏳ 3 hours
Topics: Spark architecture, RDDs vs. DataFrames, parallelism.
Hands-on: Process large datasets using PySpark.
Module 7: Real-World Project: End-to-End Pipeline
⏳ 3.5 hours
Topics: Combining tools in a real pipeline from source to dashboard.
Hands-on: Build a full pipeline using ingestion, transformation, orchestration, and warehousing.
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Job Outlook
Data engineers are in high demand across industries including tech, healthcare, finance, and e-commerce.
Strong salaries ranging from $100K–$160K+ depending on experience and stack.
Skills in Airflow, Kafka, Spark, and cloud platforms are increasingly sought-after.
Freelance and remote roles growing in data infrastructure and analytics engineering.
Specification: Learn Data Engineering Course
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FAQs
- Basic SQL and Python experience is recommended.
- No need for deep software engineering or advanced coding.
- Focus is on applying tools like Airflow, Kafka, Spark, and Snowflake.
- Beginners with analytical or BI background can follow along.
- Ideal for developers, analysts, or anyone moving into infrastructure roles.
- Course emphasizes tool-based, hands-on exercises.
- Includes real-world ingestion, transformation, and orchestration tasks.
- Learners simulate full pipelines with Kafka, Airflow, and Spark.
- Ends with an end-to-end project building a production-like data pipeline.
- Minimal time spent on abstract theory without application.
- Junior Data Engineer or Associate Data Engineer roles.
- Data Infrastructure Engineer or Analytics Engineer positions.
- Helpful for Software Developers transitioning into data roles.
- Growing demand across tech, finance, healthcare, and e-commerce.
- Salaries typically range from $100K–$160K+ in mature markets.
- Focuses on building and managing data infrastructure, not analytics.
- Emphasizes pipelines, orchestration, and storage systems.
- Covers streaming and batch data workflows instead of modeling.
- Prepares learners to make data usable for data scientists and analysts.
- Complements but does not overlap heavily with machine learning or AI courses.
- Some tools like Spark may need additional system resources.
- Cloud-based options like Snowflake are introduced with trial accounts.
- Learners can practice orchestration and ingestion locally on small datasets.
- Hardware requirements are manageable for most modern laptops.
- Optional cloud integration prepares learners for enterprise-scale setups.
