Course Overview
Our Data Engineering program offers expertise in building scalable, high-performance data pipelines and architectures. With tools like Apache Airflow, Spark, Kafka, and cloud platforms, you’ll be industry-ready.
Curriculum
- 4 Sections
- 40 Lessons
- 10 Weeks
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- Bronze:$999, 1-2 months, beginner6
- 1.0Data Engineering Fundamentals: ETL/ELT workflows.
- 1.1SQL Basics: Querying databases and data modeling.
- 1.2Cloud Basics: Intro to AWS and GCP data storage services.
- 1.3Data Pipeline Tools: Basics of Apache Tools
- 1.4Project: Create a simple ETL pipeline with Python and SQL
- 1.5Quizzes: Basic SQL, ETL workflows, and cloud basics.
- Silver:$1,499, 2-3 months, intermediate12
- 2.0Intermediate SQL (Joins, Indexing, Performance Tuning)
- 2.1Data Modeling Concepts
- 2.2Advanced Python for Data Processing
- 2.3Introduction to Big Data (Hadoop, Spark Basics)
- 2.4Streaming Data with Kafka
- 2.5ETL Automation with Apache Airflow
- 2.6Data Warehousing
- 2.7Introduction to Data Governance
- 2.8Networking Basics and API Integration
- 2.9Securing Data Pipelines
- 2.10Project: Develop a batch pipeline to load and transform data in a cloud environment.
- 2.11Quizzes: Data integration and orchestration basics.
- Gold:$1,999, 3-4 months, advanced12
- 3.0Advanced Big Data Tools (Spark, Hive, Presto)
- 3.1Infrastructure as Code (Terraform, CloudFormation)
- 3.2Real-Time Data Pipelines with Kafka and Spark Streaming
- 3.3Distributed Data Processing Systems
- 3.4Advanced Data Governance
- 3.5Optimizing ETL Jobs for Large Datasets
- 3.6Multi-Cloud Architectures
- 3.7Advanced Data Governance and Lineage Tracking
- 3.8CI/CD for Data Pipelines
- 3.9Building Scalable and Fault-Tolerant Data Systems
- 3.10Project: Build a real-time data pipeline with Kafka and Spark.
- 3.11Quizzes: Streaming and cloud optimizations.
- Platinum Level: $2999, 4-6 months training10
- 4.0Designing Enterprise Data Architectures
- 4.1Advanced Streaming Solutions (IoT, Sensor Data)
- 4.2Machine Learning Pipelines for Engineers
- 4.3Data Engineering Leadership and Mentoring
- 4.4Managing Complex Data Systems
- 4.5Ethical Considerations in Data Engineering
- 4.6Open-Source Contributions in Data Engineering
- 4.7Advanced Monitoring and Debugging for Pipelines
- 4.8Project: Design an end-to-end scalable data architecture.
- 4.9Quizzes: Advanced architecture and MLOps concepts.
Instructor
