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  • 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.

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!

  • 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.
  • 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.
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  • Section 1 : SQL and DDL Basics Introduction 4 Lectures 00:31:12

    • Lecture 1 :
    • Lecture 2 :
    • Understanding SQL Intuition and DDL Basics
    • Lecture 3 :
    • Authorization and DDL Commands
    • Lecture 4 :
    • Advanced DDL: ALTER, DROP, and RENAME
  • Section 2 : Deep Dive into DDL and Constraints 3 Lectures 00:15:32

    • Lecture 1 :
    • Comprehensive DDL Concepts
    • Lecture 2 :
    • DDL Commands: Read, Insert, and Update Authorization
    • Lecture 3 :
    • Primary Key Constraints and Auto-Increment
  • Section 3 : Data Manipulation and Table Alterations 3 Lectures 00:14:33

    • Lecture 1 :
    • Table and Column Alterations
    • Lecture 2 :
    • Data Manipulation Language (DML) Basics
    • Lecture 3 :
    • SELECT Command: Advantages, Limitations, and Challenges
  • Section 4 : DML Commands: INSERT, UPDATE, DELETE 3 Lectures 00:20:30

    • Lecture 1 :
    • INSERT Command: Pros and Cons
    • Lecture 2 :
    • UPDATE and DELETE Commands: Pros and Cons
    • Lecture 3 :
    • Transaction Control Language: COMMIT, ROLLBACK, SAVEPOINT
  • Section 5 : Data Control Language and Constraints 3 Lectures 00:24:16

    • Lecture 1 :
    • DCL and Domain Constraints
    • Lecture 2 :
    • Check Constraints and Table Constraints
    • Lecture 3 :
    • Primary and Foreign Key Constraints
  • Section 6 : Advanced SQL Operations 3 Lectures 00:20:46

    • Lecture 1 :
    • Foreign Key vs. Primary Key and TRUNCATE vs. DELETE
    • Lecture 2 :
    • SQL Procedures and Set Operations
    • Lecture 3 :
    • Group By, Where, Fetch, Limit, and Distinct
  • Section 7 : Advanced Filtering and Examples 3 Lectures 00:18:40

    • Lecture 1 :
    • HAVING vs. WHERE with Examples
    • Lecture 2 :
    • Match and Offset Functions
    • Lecture 3 :
    • MIN, MAX, IN, and NOT IN Operations
  • Section 8 : Logical Operations and Grouping 3 Lectures 00:22:29

    • Lecture 1 :
    • AND, OR, NOT Operations
    • Lecture 2 :
    • Grouping Sets and LIKE Operation
    • Lecture 3 :
    • Advanced Grouping and Join Operations
  • Section 9 : Limit and Offset Operations 3 Lectures 00:20:10

    • Lecture 1 :
    • LIMIT and OFFSET Clauses
    • Lecture 2 :
    • IN, NOT IN, and NULL Operations
    • Lecture 3 :
    • Join Operations: LEFT and RIGHT
  • Section 10 : Advanced Join Techniques 3 Lectures 00:19:20

    • Lecture 1 :
    • Inner and Outer Joins
    • Lecture 2 :
    • Full Outer and Cross Joins
    • Lecture 3 :
    • Hands-On: RANK and ROW_NUMBER
  • Section 11 : Views and Triggers 3 Lectures 00:19:02

    • Lecture 1 :
    • Dense Rank: Advantages and Limitations
    • Lecture 2 :
    • SQL Views: Creation, Updating, and Dropping
    • Lecture 3 :
    • SQL Triggers: Row Level vs. Statement Level
  • Section 12 : Subqueries and Optimization 3 Lectures 00:19:15

    • Lecture 1 :
    • Deleting Triggers and Complex Subqueries
    • Lecture 2 :
    • Subqueries and Query Optimization
    • Lecture 3 :
    • Comparison Operations and Introduction to Stored Procedures
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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!
View More...
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