Data Engineering for Beginners: Learn SQL, Python & Spark

🚀 Master Data Engineering with Python, SQL, and PySpark! 📊

Course Title: Data Engineering Essentials using SQL, Python, and PySpark

Course Headline: Learn key Data Engineering Skills such as SQL, Python, Apache Spark (Spark SQL and Pyspark) with Exercises and Projects

Dive into the World of Data Engineering!

Data Engineering is a critical domain that involves processing data to meet downstream needs efficiently. It's not just about ETL (Extract, Transform, Load) or Data Warehouse Development; it's about understanding the full lifecycle of data operations and crafting robust data pipelines—be they batch or streaming.

Why Learn Data Engineering Skills?

As a professional, you'll face several challenges when learning Data Engineering skills:

✅ Setting up a conducive environment with tools like Apache Hadoop, Apache Spark, and Apache Hive. ✅ Accessing high-quality content that's well-supported and aligned with industry standards. ✅ Engaging with a variety of tasks and exercises to reinforce your learning and skills.

This course is meticulously designed to help you conquer these challenges and acquire the essential Data Engineering Skills in Python, SQL, and Apache Spark. Whether you're starting out or looking to enhance your expertise, this course will guide you through every step of the way. 🎓

Course Highlights:

1. Environment Setup for Data Engineering Essentials, including SQL (using Postgres) and Python.
2. Practical SQL Exercises with real-world scenarios, covering basic to advanced queries, troubleshooting, and performance tuning.
3. Python Programming Basics, perfect for beginners or those looking to refine their skills.
4. Data Processing with Pandas, a key library for data manipulation and analysis in Python.
5. Two Real-Time Python Projects that will give you hands-on experience in file format conversion and database loading.
6. Troubleshooting and Debugging techniques for both databases and Python applications, with an emphasis on performance tuning.
7. Setting Up Spark Environment using Google Cloud Platform's Databricks to ensure a comprehensive learning experience.
8. Spark SQL Queries with practical examples, including the use of WHERE, JOIN, GROUP BY, HAVING, and ORDER BY clauses.
9. Delta Tables CRUD Operations, enabling you to perform insertions, updates, deletes, and merges within Spark SQL.
10. Integration of Spark SQL and Pyspark, providing a seamless experience between the two for complex data engineering tasks.
11. Catalyst Optimizer Coverage for performance tuning in Apache Spark.
12. Explain Plans Analysis to read and understand the execution plans of your queries and data frames.
13. Columnar File Formats Understanding, including partitioning techniques for optimization and performance enhancement.

What You Will Learn:

• SQL Mastery: From basic to advanced queries, you'll learn to handle complex data with confidence using WHERE, JOIN, GROUP BY, HAVING, ORDER BY, and more.
• Python Skills: Dive into Python programming, collections for data engineering, and effective data processing with Pandas.
• PySpark Proficiency: Write Spark SQL queries, create Delta Tables, integrate Spark SQL and Pyspark, and leverage the Catalyst Optimizer for peak performance.
• Real-World Projects: Bring your learning to life with two Python projects that will test your skills and enhance your portfolio.
• Performance Tuning: Learn how to optimize your SQL queries and data engineering applications for maximum efficiency.
• Troubleshooting and Debugging: Master the art of identifying and resolving issues in both databases and Python applications.

Enroll now to embark on your journey to becoming a Data Engineering expert! 🌟 With this comprehensive course, you'll be equipped with the skills and knowledge to excel in the field of data engineering. Sign up today and transform your career with the power of Python, SQL, and PySpark!