1. Course Syllabus

     Module 1: Introduction to Data Engineering – Data Engineering Fundamentals: Covers the role and responsibilities of a Data Engineer (data pipelines, ETL/ELT, data modeling), common data architectures (data warehouse, data lake, lakehouse, streaming), and the data lifecycle (collection, storage, processing, analytics). Participants learn how data engineering underpins analytics and AI, and see examples of data-driven architectures. Hands-on exercises include exploring sample datasets and writing a simple data ingestion script. (Aligns with job roles by defining core data engineering tasks.)

     Module 2: Python for Data Engineering – Python Programming: Teaches Python fundamentals for data work (variables, data types, loops, functions) and libraries (Pandas, JSON, CSV, logging). Emphasis is on writing scripts to extract, transform, and load data. Lab activities include using Python to read/write files, call APIs or databases, and manipulate datasets with Pandas. (Note: Industry credentials like the Azure Data Engineer require solid knowledge of Python and SQL.) This module also covers version control (Git) and working in IDEs or Jupyter notebooks.

     Module 3: SQL and Relational Databases – Database Fundamentals: Introduces relational database concepts and advanced SQL. Learners practice writing queries (SELECT/JOIN, GROUP BY, window functions) and designing normalized schemas. Topics include indexing, query optimization, and using SQL for ETL (inserting/loading transformed data). Lab exercises involve querying sample databases and building a small transactional data model. The module highlights how SQL skills are essential for data engineers (many certification exams emphasize SQL competency).

     Module 4: Data Warehousing and Modeling – Analytical Data Modeling: Focuses on designing data warehouses/lakes for analytics. Students learn dimensional modeling (star/snowflake schemas), fact and dimension tables, and data normalization vs. denormalization. The module covers building a data warehouse on cloud (e.g. Redshift or Snowflake) and performing ETL into it. Hands-on labs include creating a star-schema, loading data into a warehouse, and running analytical queries. By the end, learners can design schemas optimized for reporting and link them to business needs.

     Module 5: Apache Spark and Big Data – Big Data Processing: Introduces distributed data processing with Apache Spark. Covers Spark architecture (RDD/DataFrame, cluster computing), Spark SQL, and batch vs. streaming concepts. Learners write PySpark jobs to process large datasets. Notably, Apache Spark “is a multi-language engine for executing data engineering, data science, and machine learning” tasks and is used by many large companies (80% of Fortune 500 use Spark). Lab exercises on a Spark cluster include data transformations and aggregations. This module lays the foundation for scalable data pipelines.

     Module 6: Databricks and Delta Lake – Unified Analytics Platform: Teaches the Databricks environment (cloud-hosted Spark). Covers Databricks notebooks, cluster setup, and Delta Lake (reliable data lake storage with ACID transactions). Students work with Databricks SQL and Delta tables to build pipelines. Activities include launching Databricks clusters, running jobs on AWS/Azure, and using Databricks’ Delta Live Tables for incremental loads. As part of Azure training, learners practice Azure Databricks (a key tool for data engineers). This hands-on module enables students to use a real-world managed Spark platform for data engineering.

     Module 7: Data Pipelines and Workflow Orchestration – ETL/ELT Pipelines: Covers end-to-end pipeline design and scheduling. Introduces ETL vs. ELT, data ingestion patterns (batch vs. streaming), and workflow tools. Students learn about orchestration platforms like Apache Airflow (or Azure Data Factory), configuring pipelines to extract, transform, and load data on schedule or in response to events. Labs include building an Airflow DAG or Azure Data Factory pipeline that integrates multiple steps (e.g. ingest from API, process with Spark, store to cloud storage). Emphasis is on automating workflows and monitoring data jobs, reflecting the data engineer’s responsibility to keep pipelines reliable.

     Module 8: AWS Data Engineering Tools – AWS Cloud Services: Explores key AWS services for data engineering: Amazon S3 (storage), AWS Glue (ETL), Amazon EMR (managed Hadoop/Spark), Amazon Redshift (data warehouse), Amazon Athena (interactive queries), Amazon Kinesis (stream processing), and Amazon QuickSight (BI/visualization). The module demonstrates how to use these services to build cloud data pipelines. Hands-on labs guide learners through tasks like setting up an S3 data lake, running Spark on EMR, loading data into Redshift, and querying with Athena. Topics align with AWS Data Analytics certification domains (collection, storage, processing, visualization). This module prepares students to design data solutions on AWS and illustrates best practices (e.g. security, scalability) in AWS data architectures.

     Module 9: Azure Data Engineering Tools – Azure Cloud Services: Covers Microsoft Azure tools for data workloads: Azure Data Factory (ETL orchestration), Azure Data Lake Storage (data lake), Azure Synapse Analytics (SQL data warehouse + Spark), Azure Stream Analytics (real-time processing), Azure Event Hubs (ingestion), and Azure Databricks. Learners practice creating Azure resource groups, Data Factory pipelines, and Synapse SQL pools, and use Azure Databricks notebooks. This module’s labs include copying data from on-premises (or web) into ADLS, transforming it with Databricks, and querying it in Synapse. The content maps directly to the Azure Data Engineer Associate exam, which expects proficiency in SQL/Python and services like Data Factory and Databricks. By completion, students can implement end-to-end data solutions on Azure.

     Module 10: Capstone Project – End-to-End Data Pipeline – Project-Based Learning: In a capstone project(s), learners integrate the skills from all modules. For example, a capstone might task students with building a fully operational data pipeline on a cloud platform: ingest raw data (e.g. streaming logs or batch files), process/transform it using Spark (on EMR or Databricks), store the results in a data warehouse (Redshift or Synapse), and generate reports or dashboards. The project (one on AWS, another on Azure/GCP as options) requires application of Python, SQL, cloud services, and orchestration. It simulates a real-world scenario to solidify learning and produces a portfolio piece. Guidance on project work (milestones, review) ensures students demonstrate competence in designing and deploying a complete data engineering workflow

    

Key Features

  Hands-on Labs & Exercises: Each module includes practical labs and exercises (e.g. writing ETL scripts, running Spark jobs, configuring cloud services) to reinforce learning.

  Project-Based Learning: Realistic projects and 1–2 capstone assignments let students apply skills by building complete data pipelines (e.g. ingesting data from sources, processing it, and loading into a warehouse).

  Flexible Formats: The curriculum is designed for both live instructor-led classes and self-paced study, with online resources, recorded demos, and support materials.

  Cloud Platform Exposure: Learners get experience on major cloud environments (AWS, Azure, Databricks, etc.), mirroring industry practice.

  Certification Mapping: Topics are mapped to AWS/GCP/Azure data engineer exams (covering SQL, Python, data architectures, cloud data services) so students can prepare for certifications. For example, Google’s Data Engineer exam tests designing/ingesting/storing/analyzing data.

  Career Support: Instruction includes guidance on job roles, resume preparation, and interview practice for Data Engineer careers.

  Expert Instruction & Materials: Course content is curated by industry experts, covering up-to-date technologies (e.g. Delta Lake, cloud ETL tools) and data engineering best practices.