SQL for Data Engineers Designing and Building Data Pipelines
SQL for Data Engineers Designing and Building Data Pipelines
Updated on Feb, 2025
Language - English
Development ,Database and Design Development,SQL
Lectures -32
Duration -3.5 hours
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Course Description
Topics include going all the way with mastery of SQL for data engineers building robust pipelines. Be you a beginner or simply wishing to add this skill to your arsenal, you will learn both how to design, implement, and optimize SQL-based data pipelines effectively.
What You'll Learn
Foundational SQL Concepts: Familiarize yourself with the foundational principles of SQL, including Data Definition Language (DDL) and Data Manipulation Language (DML).
Advanced SQL Techniques: Learn the not-so-obvious aspects of advanced SQL topics such as constraints, joins, subqueries, stored procedures, and transaction control.
Practical Data Pipeline Design: Practice designing as well as building data pipelines that are efficient, of appropriate integrity, give superb performance, and are scalable.
Hands-On Projects: Present your concepts as practical projects, simulating real-world data engineering challenges that hone your problem-solving abilities.
Optimization Strategies: Discover how to optimize SQL query and the data pipeline. Because the same becomes faster and more efficient with execution.
Key Features:
Interactive Lessons: Video lectures in which interaction is maintained and exercises in the form of quizzes are included for better understanding.
Real-World Examples: Practical examples or case studies that help show the same concepts in practice and applications.
Instruction by Experts: EEs learn directly from professionals keeping an eye on industry practices and best practices.
Flexible learning: learn any time. It can be self-paced, with lifelong access to the material.
Goals
- Aspiring Data Engineers: It refers to a data beginner who wants to venture into data engineering and learn SQL for the first time.
- Experienced Professionals: These include data analysts, developers, and engineers wanting to deepen knowledge of SQL and the ability to handle data pipelines effectively.
- Tech Enthusiasts: Anyone interested in knowing how to store and process data in an efficient way through SQL.
- You'll know how to design and build efficient data pipelines by the end of this course with SQL at your fingertips. You could now manage and analyze your data with the power of SQL. So, enrol now and take the first step towards mastering SQL for data engineering!
Prerequisites
None
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to SQL and DDL Basics
3 Lectures
-
Understanding SQL Intuition and DDL Basics
06:16
06:16
-
Authorization and DDL Commands
08:33
08:33
-
Advanced DDL: ALTER, DROP, and RENAME
08:06
08:06
Deep Dive into DDL and Constraints
3 Lectures
Data Manipulation and Table Alterations
3 Lectures
DML Commands: INSERT, UPDATE, DELETE
3 Lectures
Data Control Language and Constraints
3 Lectures
Advanced SQL Operations
3 Lectures
Advanced Filtering and Examples
3 Lectures
Logical Operations and Grouping
3 Lectures
Limit and Offset Operations
3 Lectures
Advanced Join Techniques
3 Lectures
Views and Triggers
2 Lectures
Instructor Details
AKHIL VYDYULA
Data Scientist | Data & Analytics Specialist | EntrepreneurHello, 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!
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