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Data Engineering

Our Data Engineering course is designed to equip you with the skills, tools, and techniques needed to work with large-scale data systems and build robust data pipelines in modern cloud environments. Whether you're transitioning into a data engineering role or enhancing your data skills, this course provides a complete pathway from beginner to intermediate level.

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🔹 Master End-to-End Data Engineering
From SQL and Python to Big Data and Cloud technologies, our course covers everything needed to become a successful Data Engineer.

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🔹 Industry-Relevant Curriculum
Designed by industry experts, our syllabus includes the latest tools and platforms such as Apache Airflow, Google BigQuery, Apache Spark, Kafka, AWS, GCP, and Azure.

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🔹 Hands-On Experience
Practical assignments, real-world case studies, and project-based learning will help you build a strong, job-ready portfolio.


Suitable for aspiring:

  • Data Engineers

  • Cloud Data Engineers

  • ETL Developers

  • Big Data Engineers

  • Analytics Engineers

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Level

Beginner to Intermediate

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Duration

8 Weeks

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Lectures

60+ Sessions

Course Prerequisites

  • Basic knowledge of databases and SQL

  • Understanding of data formats like CSV, JSON, etc.

  • Curiosity to work with large-scale data and cloud technologies.

Curriculum

60+ Sessions

  • Introduction to Data Engineering

    What is Data Engineering and why is it important?

    Difference between Data Engineer, Data Scientist, and Data Analyst.

    The role of Data Engineers in ETL, data pipelines, and data warehousing.

    Fundamentals of Databases

    Types of Databases

    Relational Databases (SQL) – MySQL, PostgreSQL, SQL Server

    NoSQL Databases – MongoDB, Cassandra, DynamoDB

    Database Concepts

    Primary Key, Foreign Key, Indexing, Normalization

    ACID (Atomicity, Consistency, Isolation, Durability) vs. BASE principles

    OLTP (Online Transaction Processing) vs. OLAP (Online Analytical Processing)

    SQL for Data Engineering

    Writing Basic Queries (SELECT, INSERT, UPDATE, DELETE).

    Joins & Subqueries (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN).

    Aggregation Functions (SUM, AVG, COUNT, GROUP BY).

    Window Functions (RANK, DENSE_RANK, ROW_NUMBER).

    Query Optimization & Performance Tuning (Indexing, Execution Plans)

    Programming for Data Engineering (Python & SQL)

    Python Basics: Variables, Loops, Functions, Exception Handling.

    Working with Pandas & NumPy for data manipulation.

    Writing SQL Queries in Python using SQLAlchemy, psycopg2

    Data serialization formats: JSON, Avro, Parquet, ORC.

  • Introduction to GCP

    Introduction to Getting Started with GCP (1 min)

    Essential Skills Required for GCP Data Analytics Course (2 min)

    Understanding Cloud & GCP Fundamentals

    Introduction to Cloud Platforms (4 min)

    Overview of Google Cloud Platform (GCP) (3 min)

    Creating a GCP Account

    Signing Up for a GCP Account (2 min)

    Creating a Google Account with a Non-Gmail ID (2 min)

    Signing Up for GCP Using a Google Account (3 min)

    GCP Account & Project Setup

    Understanding GCP Credits (4 min)

    Introduction to GCP Projects and Billing (2 min)

    Exploring Google Cloud Shell (3 min)

    Installing Google Cloud SDK on Windows (5 min)

    Initializing gcloud CLI with a GCP Project (3 min)

    Reinitializing Google Cloud Shell with a Project ID (3 min)

    Introduction to GCP Analytics Services

    Overview of Analytics Services on GCP (2 min)

    Final Thoughts

    Conclusion: Getting Started with GCP for Data Engineering

  • Extract, Transform, Load (ETL) Concepts

    Batch vs. Real-time ETL and when to use each

    Popular ETL Tools

    Apache Airflow (Python-based workflow scheduler)

    Talend, Informatica (GUI-based ETL tools).

    Writing custom ETL scripts in Python.

    Data Warehousing Basics

    What is a Data Warehouse and how is it different from a Database?

    Data Warehouse Architectures

    Star Schema vs. Snowflake Schema

    Fact & Dimension Tables

    Popular Data Warehouses: Amazon Redshift, Google BigQuery, Snowflake

    Partitioning & Clustering for performance improvement.

    Big Data & Distributed Systems

    Introduction to Hadoop & HDFS

    How Hadoop stores and processes big data

    Understanding the MapReduce framework

    Introduction to Apache Spark

    Spark vs. Hadoop (Why Spark is faster?)

    PySpark for Data Engineering

    Batch vs. Streaming Processing

    Kafka vs. Flink vs. Spark Streaming.

    Use cases for real-time data processing

    Data Modeling & Schema Design

    What is Data Modeling?

    Schema Design for Data Warehousing

    Normalized vs. Denormalized Data

    Star Schema vs. Snowflake Schema

    Slowly Changing Dimensions (SCD) for historical data tracking

  • Cloud Technologies for Data Engineering

    Overview of Cloud Computing and its benefits

    Key Cloud Providers

    AWS: S3 (Storage), Glue (ETL), Lambda (Serverless), Redshift (Data Warehouse)

    Azure: Azure Data Factory, Azure Databricks, Synapse Analytics.

    Google Cloud: BigQuery, Cloud Storage, DataFlow.

    Building scalable data pipelines on Cloud

    Data Engineering DevOps Practices

    CI/CD (Continuous Integration/Continuous Deployment) for Data Pipelines

    Infrastructure as Code (IaC): Terraform, CloudFormation

    Containerization & Orchestration: Docker, Kubernetes

    Monitoring & Logging

    Prometheus, Grafana for monitoring

    AWS CloudWatch for logging

    Data Security & Governance

    Understanding Data Security Best Practices

    Role-Based Access Control (RBAC) in Cloud Platforms

    Data Privacy & Compliance (GDPR, HIPAA)

    Data Lineage & Metadata Management (Tracking data sources & transformations)

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