CERTIFICATES

This module on column store indexes in Microsoft BI covers their implementation for optimizing data retrieval and analytics. It explores how column store indexes enhance query performance by storing data in a columnar format, facilitating faster analytics and aggregation operations. Students learn to leverage column store indexes in Microsoft BI for efficient data scanning and analytical workloads, improving overall query performance in business intelligence scenarios.

In this module, students explore the advantages of Azure SQL Data Warehouse, understanding its scalability and integration benefits. They then delve into implementing and developing Azure SQL Data Warehouse, learning setup, database design, and optimization techniques.

In this module, users explore SSIS to craft efficient ETL solutions, mastering data extraction, transformation, and loading. They learn to design workflows, configure data flow tasks, and implement transformations for seamless data movement within SQL Server environments. The curriculum focuses on leveraging SSIS for robust ETL processes, enabling users to build scalable data pipelines.

This module introduces Control Flow, focusing on managing program execution through loops, conditionals, and branching. It covers the fundamentals of directing logical pathways within code, enabling students to apply control flow statements for effective program control and decision-making. Students gain a foundational understanding of how control structures govern code execution and logical pathways in programming.

Debugging and Troubleshooting SSIS Packages involve identifying and resolving issues within SQL Server Integration Services workflows. Students learn techniques to identify errors, monitor package execution, and resolve common SSIS errors using tools like breakpoints and data viewers.

Implementing an Incremental ETL (Extract, Transform, Load) Process involves designing workflows that update only the changed or new data since the last ETL run. Students learn to create strategies and methodologies within SSIS or other ETL tools to identify, extract, transform, and load only the incremental changes, minimizing processing time and resource utilization.

Within this module, students explore methodologies to enforce and maintain data quality standards. They learn techniques in SSIS or ETL tools for data validation, cleansing, and error handling. The curriculum covers data profiling, applying constraints, and cleansing practices to ensure high-quality, reliable data for operational and analytical use.

This module introduces Master Data Services (MDS), covering its role in centralizing and managing organizational master data. Students explore creating data models, managing entities, and implementing data governance within MDS. The curriculum focuses on MDS fundamentals, emphasizing its capacity to ensure data consistency and reliability across the organization.

In this module, students learn the process of deploying and configuring SSIS packages for production environments. They explore deployment strategies, package configurations, and security considerations within SQL Server Integration Services. The curriculum covers best practices for deploying, configuring, and securing SSIS packages, ensuring smooth and efficient execution in enterprise environments.

This module guides students in effectively accessing and utilizing data stored in a data warehouse. It covers querying, reporting, and extracting insights from warehouse data using different tools and methodologies. Students learn how to leverage data for business intelligence, analytics, and reporting purposes, enhancing their understanding of data utilization within a warehouse context.