Overview
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.
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.
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.
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!
Akhil Vydyula
Hello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.
My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.
I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.
Currently, I am deeply involved in an exciting Amazon cloud project, focusing on the end-to-end development of ETL processes. I write ETL code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, and execute scripts via EMR services. The processed data is then loaded into Postgres SQL (RDS/Redshift) in full, incremental, and live modes. To streamline operations, I’ve automated this process by setting up jobs in Step Functions, which trigger EMR instances in a specified sequence and provide execution status notifications. These Step Functions are scheduled through EventBridge rules.
Moreover, I've extensively utilized AWS Glue to replicate source data from on-premises systems to raw-layer S3 buckets using AWS DMS services. One of my key strengths is understanding the intricacies of data and applying precise transformations to convert data from multiple tables into key-value pairs. I’ve also optimized stored procedures in Postgres SQL to efficiently perform second-level transformations, joining multiple tables and loading the data into final tables.
I am passionate about harnessing the power of data to generate actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, I would love to connect. Let’s explore the endless possibilities that data analytics can offer!