SQL at Scale: Querying, Transforming, and Governing

Did you know that a single poorly optimized SQL query can slow down an entire data warehouse, impacting dashboards, applications, and business decisions? Identifying and fixing performance bottlenecks is critical to keeping analytical systems fast and scalable. This Short Course was created to help professionals in this field master advanced SQL performance optimization techniques for maintaining scalable data warehouses and analytical platforms. By completing this course, you will be able to analyze SQL query performance, interpret execution behaviors, and diagnose bottlenecks that impact speed and efficiency—skills that enable you to optimize workloads and sustain high-performing data environments. By the end of this 3-hour long course, you will be able to: Analyze query performance to diagnose and resolve execution bottlenecks. This course is unique because it combines real-world performance tuning with deep execution analysis, giving you a practical foundation for optimizing complex SQL workloads and improving end-to-end system responsiveness. To be successful in this project, you should have: Intermediate SQL querying experience Understanding of database concepts Data warehouse experience Familiarity with execution plan basics Did you know that a single poorly optimized SQL query can slow down an entire data warehouse, impacting dashboards, applications, and business decisions? Identifying and fixing performance bottlenecks is critical to keeping analytical systems fast and scalable. This Short Course was created to help professionals in this field master advanced SQL performance optimization techniques for maintaining scalable data warehouses and analytical platforms. By completing this course, you will be able to analyze SQL query performance, interpret execution behaviors, and diagnose bottlenecks that impact speed and efficiency—skills that enable you to optimize workloads and sustain high-performing data environments. By the end of this 3-hour long course, you will be able to: Analyze query performance to diagnose and resolve execution bottlenecks. This course is unique because it combines real-world performance tuning with deep execution analysis, giving you a practical foundation for optimizing complex SQL workloads and improving end-to-end system responsiveness. To be successful in this project, you should have: Intermediate SQL querying experience Understanding of database concepts Data warehouse experience Familiarity with execution plan basics

Master the advanced operational skills that separate proficient SQL users from expert data infrastructure managers. This course tackles the critical challenges of managing enterprise-scale SQL environments through hands-on practice with resource optimization, security auditing, and systematic failure analysis. You'll gain immediate value by learning to configure resource pools that prevent query bottlenecks, audit permission structures to eliminate security gaps, and conduct post-mortem analyses that strengthen system reliability. These skills directly address the daily operational challenges faced by data engineers managing production SQL environments. By the end of this course, you will be able to: • Apply resource management techniques to optimize warehouse performance • Analyze data permissions to identify and remediate security vulnerabilities • Evaluate system failures through structured root cause analysis This course is unique because it bridges the gap between SQL syntax knowledge and real-world operational expertise, focusing on the systematic approaches used by senior data engineers in enterprise environments. To be successful in this course, you should have experience with SQL queries and database administration. This course primarily demonstrates concepts using Microsoft SQL Server (including Resource Governor) and standard SQL tooling. Some examples reference cloud data warehouse platforms such as Databricks SQL or Snowflake for conceptual contrast, but all core techniques can be applied using free/developer editions or open-source tools.

Master the critical skills for ensuring data reliability and building self-healing data systems. This course transforms your approach to data quality from reactive firefighting to proactive engineering driven reliability. This Short Course was created to help data management and engineering professionals accomplish systematic data quality assurance and error automation at enterprise scale. By completing this course, you'll be able to implement quantitative data quality measurements, establish monitoring systems that catch degradation trends before they impact business operations, and build intelligent SQL routines that automatically recover from data pipeline failures. By the end of this course, you will be able to: • Apply calculations to measure key data quality dimensions • Evaluate quality key performance indicators over time and recommend remediation • Create an automated SQL routine to handle and reprocess data errors. This course is unique because it blends quantitative data quality methods with practical automation engineering, enabling you to build self-healing data systems that maintain measurable quality standards at scale. To be successful in this course, you should have a background in SQL, data pipeline concepts, and basic data engineering principles.

Are you ready to become the guardian of enterprise data security? This course transforms data professionals into security specialists who can protect sensitive information at scale. This Short Course was created to help data management and engineering professionals accomplish comprehensive data protection through advanced security controls, monitoring, and compliance frameworks. By completing this course, you'll master the technical skills to implement column-level data masking that protects PII while maintaining data utility, analyze database audit logs to detect suspicious access patterns before breaches occur, and evaluate security architectures against SOC 2, NIST, and other critical compliance standards. These capabilities will immediately enhance your value to employers seeking professionals who can safeguard enterprise data assets. By the end of this course, you will be able to: Apply column-level masking policies to protect sensitive data Analyze query audit logs to detect unusual access patterns Evaluate security controls against industry standards and compliance requirements This course is unique because it combines hands-on technical implementation with strategic compliance evaluation, giving you both the tactical skills to secure databases and the analytical expertise to assess enterprise-wide security postures. To be successful in this course, you should have experience with SQL databases, understanding of data governance concepts, and familiarity with enterprise security principles.

Ready to take your SQL skills beyond basic queries? This course transforms intermediate SQL developers into advanced database engineers who can automate deployments, debug complex issues, and build reusable solutions. This Short Course was created to help data management and engineering professionals accomplish enterprise-level database automation and customization. By completing this course, you'll master CI/CD database deployment pipelines, systematic error handling with TRY-CATCH blocks, and custom function development. You'll apply these skills to real scenarios like Flyway migrations, production debugging workflows, and Python UDF creation in Snowflake. This course is unique because it bridges the gap between traditional SQL development and modern DevOps practices, giving you the practical skills to build robust, maintainable database systems. To be successful in this project, you should have a background in intermediate SQL, database development experience, and familiarity with version control systems."

Data quality failures cost organizations millions in bad decisions and lost trust. This advanced course transforms you into a data quality architect who can prevent these failures before they happen. This Short Course was created to help data engineers and analysts accomplish bulletproof data validation automation that catches issues before they impact business decisions. By completing this course, you'll be able to embed automated quality checks directly into your data pipelines, systematically diagnose validation failures to their root cause, and build reusable SQL frameworks that scale across your entire data ecosystem. By the end of this course, you will be able to: - Apply automated data quality tests to data models - Analyze validation failures to pinpoint the root cause - Create a reusable SQL validation framework based on table statistics This course is unique because it focuses on building systematic, code-based validation solutions rather than manual testing approaches, giving you the skills to automate data governance at enterprise scale. To be successful in this project, you should have a background in SQL, data pipeline concepts, and database system fundamentals.

Data pipeline failures cost organizations millions in lost revenue and broken decisions. This course empowers data management professionals with practical skills to build bulletproof data quality systems using industry-standard frameworks and automated testing approaches. This Short Course was created to help data engineers and analysts accomplish robust data validation that prevents costly pipeline failures and ensures reliable analytics. By completing this course, you'll be able to implement comprehensive data quality tests that automatically catch issues before they impact downstream systems, write YAML-based validation suites that monitor null rates and row counts, and establish automated quality gates that protect your data infrastructure. By the end of this course, you will be able to: Apply a data quality framework to define tests for data integrity Implement automated validation for volume, completeness, and uniqueness requirements Write YAML test suites that enforce quality standards across data pipelines This course is unique because it focuses on practical, hands-on implementation of enterprise-grade data quality frameworks using real-world scenarios and industry-standard tools like Great Expectations and dbt testing. To be successful in this project, you should have a background in basic data concepts, familiarity with SQL queries, and understanding of data pipeline fundamentals.

Master the power of SQL window functions to unlock advanced analytical capabilities in your data engineering workflow. This course transforms beginners into confident practitioners who can compute rolling averages, assign rankings, and perform complex calculations while preserving row-level detail. This Short Course was created to help data engineering professionals accomplish sophisticated analytical queries that traditional GROUP BY statements simply cannot handle. By completing this course, you'll be able to define analytical windows using OVER() clauses, compute 7-day rolling sales metrics, create dynamic leaderboards with ranking functions, and build time-series analysis queries that you can deploy immediately in production environments. By the end of this course, you will be able to: Apply SQL window functions to compute rolling and rank-based metrics This course is unique because it focuses on hands-on application with real-world scenarios from data engineering practice, using the exact ROW_NUMBER() and AVG() OVER patterns that professionals use daily. To be successful in this course, you should have basic SQL knowledge and experience with data analysis concepts.

SQL: Build & Trace Pipelines Did you know that even small inefficiencies or errors in SQL pipelines can cascade across an entire data warehouse, impacting dashboards, models, and business decisions? Mastering SQL-based workflow automation and traceability is essential for reliable data operations. This Short Course was created to help data engineering professionals build automated data processing workflows and systematically analyze pipeline dependencies for enterprise data infrastructure. By completing this course, you will be able to write parameterized SQL for scheduled ELT jobs and trace multi-step SQL pipelines to understand data flow, transformation logic, and upstream-downstream relationships—skills that strengthen both accuracy and maintainability in production systems. By the end of this 4-hour long course, you will be able to: Apply parameterized SQL to create scheduled ELT jobs for data processing. Analyze a multi-step SQL pipeline to trace data flow and transformation logic. This course is unique because it combines automation, traceability, and SQL craftsmanship, giving you the tools to build scalable pipelines while developing deep insight into how data moves and transforms across complex enterprise systems. To be successful in this project, you should have: Advanced SQL skills Data warehousing knowledge Basic ETL concepts Familiarity with scheduling tools

Master the advanced SQL techniques that separate proficient data engineers from the experts. This course transforms your ability to reshape complex data structures and compute sophisticated analytical metrics that power enterprise data pipelines. This Short Course was created to help data management and engineering professionals accomplish seamless data transformation and advanced analytical computations. By completing this course, you'll be able to confidently normalize wide-format data structures, implement complex window functions for time-series analysis, and optimize data pipeline performance across major cloud platforms including Azure Synapse, SQL Server, Teradata, and Snowflake. By the end of this course, you will be able to: Analyze the transformation of pivoted data into a normalized format and its associated trade-offs Apply SQL window functions to compute rolling and rank-based metrics This course is unique because it bridges the gap between basic SQL operations and enterprise-grade data transformation techniques, providing hands-on experience with real-world scenarios that mirror production data engineering challenges. To be successful in this project, you should have a background in intermediate SQL, experience with data warehousing concepts, and familiarity with at least one enterprise database platform.

Did you know that poor data quality costs organizations an average of $15 million annually? This Short Course was created to help data engineers and analysts accomplish bulletproof data validation and hierarchical data navigation. By completing this course, you'll be able to implement comprehensive testing suites that catch data errors before they impact downstream systems and create sophisticated recursive queries that unlock organizational hierarchies and complex relationships. By the end of this course, you will be able to: - Evaluate data transformations by implementing unit, schema, and data quality tests - Create recursive Common Table Expressions (CTEs) to traverse hierarchical data This course is unique because it combines rigorous testing methodologies with advanced SQL techniques that mirror enterprise data engineering practices, giving you production-ready skills for building trustworthy data systems. To be successful in this project, you should have a background in intermediate SQL, data modeling concepts, and experience with data transformation workflows. Transform your data engineering capabilities with enterprise-grade validation and hierarchical querying techniques that ensure data reliability and unlock complex organizational insights.