SQL for Data Engineers Designing and Building Data Pipelines
What you'll learn
- Master SQL fundamentals Gain a deep understanding of SQL basics, including data definition and manipulation.
- Design and build efficient data pipelines Learn how to create robust and scalable data pipelines to manage and transform data.
- Implement advanced SQL techniques Explore complex SQL queries, joins, and performance optimization strategies.
- Ensure data integrity and security Understand how to enforce data integrity and apply security best practices in SQL.
Requirements
- Basic understanding of databases Familiarity with basic database concepts is recommended but not required.
- Computer with internet access You'll need a computer with an internet connection to access course materials and SQL environments.
- Willingness to learn No prior programming experience needed. All concepts will be explained from the ground up.
Description
This comprehensive course is tailored for data engineers looking to master SQL and build robust data pipelines. Whether you're just starting or aiming to enhance your existing skills, this course will provide you with the knowledge and tools needed to design, implement, and optimise SQL-based data pipelines effectively.
What You'll Learn:
Foundational SQL Concepts: Gain a solid understanding of SQL and its core principles, including Data Definition Language (DDL) and Data Manipulation Language (DML).
Advanced SQL Techniques: Dive deep into advanced SQL topics such as constraints, joins, subqueries, stored procedures, and transaction control.
Practical Data Pipeline Design: Learn to design and build efficient data pipelines, ensuring data integrity, performance, and scalability.
Hands-On Projects: Apply your knowledge through practical projects that simulate real-world data engineering challenges, enhancing your problem-solving skills.
Optimization Strategies: Discover techniques to optimize SQL queries and data pipelines, improving performance and efficiency.
Key Features:
Interactive Lessons: Engaging video lectures and interactive exercises to reinforce learning.
Real-World Examples: Practical examples and case studies to illustrate key concepts and their applications.
Expert Instruction: Learn from experienced professionals who bring industry insights and best practices.
Flexible Learning: Self-paced course with lifetime access to materials, allowing you to learn at your convenience.
Target Audience:
Aspiring Data Engineers: Beginners looking to enter the field of data engineering and learn SQL from scratch.
Experienced Professionals: Data analysts, developers, and engineers seeking to deepen their SQL knowledge and enhance their data pipeline skills.
Tech Enthusiasts: Anyone interested in understanding how to manage and process data efficiently using SQL.
By the end of this course, you will have the skills and confidence to design and build efficient data pipelines, leveraging the power of SQL to manage and analyze data effectively. Enrol now and take the first step towards mastering SQL for data engineering!
Who this course is for:
- Aspiring data engineers Perfect for those looking to break into data engineering and build strong foundational skills.
- Current data professionals Ideal for data analysts, BI developers, and database administrators seeking to enhance their SQL knowledge.
- Tech enthusiasts Anyone with a keen interest in data and technology who wants to learn how to manage and manipulate data using SQL.
- Students and graduates Great for students and recent graduates aiming to add a valuable skill to their resume and increase their employability.
Instructor
- 4.0 Instructor Rating
- 1,128 Reviews
- 70,108 Students
- 46 Courses
Hello, I'm Akhil Vydyula — Lead Data Engineer at Publicis Sapient, and Former Senior Data Scientist at PwC.
With over 5 years of rich industry experience and a strong focus on the BFSI sector, I’ve led and delivered end-to-end data and analytics solutions that power strategic decisions and transform business outcomes.
At Publicis Sapient, I currently lead complex data engineering initiatives, leveraging my deep expertise in cloud-native platforms like AWS to architect robust, scalable data pipelines. My work spans across developing and optimizing ETL workflows using PySpark and Spark SQL, orchestrating data flows via EMR, Step Functions, and EventBridge, and driving real-time and batch data processing into PostgreSQL (RDS/Redshift) environments. I've also implemented AWS Glue and DMS to seamlessly replicate and transform large-scale on-premise data into cloud-native formats.
Previously, at PwC, I specialized in advanced analytics and machine learning within the Advisory Consulting practice. I’ve built and deployed predictive models using statistical analysis, regression, classification, clustering, and text mining—particularly for risk identification and decision modeling. My passion lies in transforming raw data into actionable insights through effective data storytelling and visualization.
In parallel to my corporate career, I bring over 5 years of teaching experience, mentoring hundreds of aspiring data professionals. I’m deeply committed to helping students break into the data industry by translating real-world challenges into practical learning experiences.
Whether it's building data pipelines, uncovering business insights, or shaping the next generation of data talent, I thrive at the intersection of technology, strategy, and impact.
Let’s connect if you're passionate about data, eager to learn, or looking to collaborate on meaningful, data-driven initiatives.