[100% Off] Certified Data Engineering &Amp; Pipelines
Master Airflow, Spark, and Data Lakes to build & deploy robust ETL pipelines on AWS & GCP Cloud.
What you’ll learn
- Design
- implement
- and optimize end-to-end ETL/ELT data pipelines using modern engineering principles and best practices.
- Master Apache Airflow for scheduling
- monitoring
- and managing complex Directed Acyclic Graphs (DAGs) in a production setting.
- Utilize Python and SQL effectively for data extraction
- cleansing
- transformation
- and loading operations.
- Implement distributed processing using Apache Spark (PySpark) to handle large-scale
- massive datasets efficiently.
Requirements
- Foundational knowledge of Python programming (loops
- functions
- and basic data structures).
- Basic proficiency in SQL and familiarity with relational database concepts.
- Access to a computer capable of running cloud environments and local development tools.
Description
Certified Data Engineering & Pipelines This comprehensive course is designed to take you from foundational concepts to advanced, production-ready data engineering practices. We focus heavily on modern, cloud-native solutions, ensuring you gain hands-on experience deploying and managing complex data pipelines that handle petabytes of data efficiently and reliably.
What Makes This Course Unique? Unlike typical courses, we provide a deep dive into the complete lifecycle of a data project, integrating key tools like Python, SQL, Apache Spark, and leading cloud services (AWS/GCP) within a structured pipeline orchestration framework (Apache Airflow). You won’t just learn *what* these tools do, but *how* to integrate them into scalable, industry-standard ETL/ELT solutions. We emphasize best practices for monitoring, error handling, and performance tuning crucial for certification and real-world success.
Core Areas Covered We cover three main pillars: 1. **Pipeline Orchestration (Airflow):** Designing, scheduling, and monitoring complex Directed Acyclic Graphs (DAGs). 2. **Data Processing & Transformation (Spark/Cloud Services):** Mastering distributed computing for massive datasets using PySpark and serverless ETL tools. 3. **Cloud Data Infrastructure:** Building secure and scalable data lakes and data warehouses (S3/GCS, Snowflake/Redshift) using Infrastructure as Code principles. By the end of this certification track, you will have built a portfolio-ready project demonstrating your capability to design, deploy, and maintain robust, high-availability data pipelines, positioning you for top roles in the Data Engineering field.
