🎬 Overview of Data Warehousing Concepts – Understand the architecture and design of data warehouses.

• Key components of data warehouses such as staging, integration, and presentation layers.

• The role of metadata in data warehouses.

🎬 Types of Data Warehouses – Explore various types of data warehouses.

• Enterprise Data Warehouses (EDW) and how they centralize organizational data.

• Operational Data Stores (ODS) – How they differ from traditional data warehouses.

• Data Marts – How they focus on specific areas of business.

🎬 Understanding ETL Process – Learn the ETL process and its significance.

• Key steps: Extraction, Transformation, and Loading, and how they work together.

• Extracting data from various sources like databases, flat files, and APIs.

• Transformation rules, data cleansing, and validation methods.

• Loading data into different destinations such as Data Warehouses or Data Lakes.

🎬 Role of Business Intelligence in Data Warehousing – How BI helps leverage data.

• How data warehouses serve as the foundation for BI tools and reporting.

• Real-time and historical data analysis using BI tools like Power BI, Tableau.

⌨ COURSE CHEAT SHEET DOWNLOAD

🎬 Introduction to SQL and Relational Databases – Basic concepts of SQL and its importance.

• Relational database structure, tables, rows, and columns.

• Data types, primary and foreign keys in relational databases.

🎬 Writing SQL Queries for Data Extraction – Practical SQL querying techniques.

• SELECT statements, filtering data with WHERE clauses.

• Using ORDER BY and GROUP BY for sorting and grouping data.

🎬 SQL Joins for Data Validation – Combining tables for comprehensive validation.

• Understanding INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

• Practical examples for validating data across multiple tables.

🎬 Data Manipulation with SQL – INSERT, UPDATE, DELETE commands for manipulating data.

• Inserting data into tables for testing purposes.

• Updating existing data to simulate real-time scenarios.

• Deleting unwanted records to ensure data integrity.

🎬 Advanced SQL for Data Transformation – Master advanced SQL features.

• Aggregation functions (SUM, COUNT, AVG) for reporting.

• CASE statements and conditional queries for complex transformations.

🎬 SQL for Data Comparison and Validation – Techniques for validating data.

• Comparing data from source and target systems using SQL queries.

• Using COUNT, GROUP BY, and HAVING clauses for data verification.

✍ Quiz 2: SQL Practical Applications

🎬 Introduction to ETL Testing – Importance of testing in ETL processes.

• Ensuring data accuracy, completeness, and integrity in ETL pipelines.

🎬 Extracting Data from Multiple Sources – Data extraction techniques and challenges.

• Extracting data from structured (databases) and unstructured sources (text files, APIs).

• Validating extracted data for consistency and completeness.

🎬 Validating Data Transformation Rules – Verifying transformation accuracy.

• Testing business rules for transforming data.

• Handling data format changes, and data enrichment during transformation.

🎬 Testing Data Loading into Target Warehouse – Techniques to ensure accurate data loads.

• Verifying data completeness and integrity during the load process.

• Testing incremental and full data loads.

🎬 Common ETL Challenges and Solutions – Tackling ETL-specific challenges.

• Handling large data volumes, slow performance, and scheduling errors.

• Addressing data duplication and missing data during ETL processes.

✍ Quiz 3: ETL Testing Knowledge Check

🎬 Ensuring Data Accuracy, Completeness, and Consistency – Techniques to test data integrity.

• Validating that data is accurate, complete, and consistent across systems.

🎬 Data Profiling Techniques – Profiling data to identify issues.

• Profiling data to identify duplicates, missing values, and outliers.

🎬 Data Cleansing for Data Quality – Methods for cleansing data.

• Standardizing and formatting data to meet data warehouse requirements.

🎬 Identifying and Fixing Data Quality Issues – Troubleshooting data issues in ETL.

• Using tools and scripts to automatically detect and fix data quality problems.

✍ Quiz 4: Data Quality Concepts

🎬 Introduction to Automation in ETL Testing – Benefits of automating ETL testing.

• Automating repetitive ETL tasks to save time and reduce human error.

🎬 Tools for Automating ETL Testing – Industry-standard ETL automation tools.

• Using Informatica, Talend, and Apache NiFi for ETL automation.

🎬 Creating Automated Test Cases for Data Validation – Writing automation scripts.

• Automating validation of data transformation, extraction, and loading processes.

🎬 Integrating Automated Testing into CI/CD Pipelines – Continuous testing and integration.

• Setting up automated tests in CI/CD pipelines for ongoing data validation.

✍ Quiz 5: Automating ETL Testing

🎬 Introduction to BI Report Testing – Importance of validating business reports.

• Ensuring BI reports accurately represent underlying data from the warehouse.

🎬 Validating OLAP Data for BI Reports – Testing OLAP cubes for data aggregation accuracy.

• Testing roll-ups, drill-downs, and slicing and dicing operations in OLAP cubes.

🎬 Testing BI Reports for Accuracy and Consistency – Techniques for report validation.

• Validating reports for consistency across multiple dimensions, filters, and formats.

🎬 Ensuring Real-Time Updates in BI Reports – Validating real-time data reporting.

• Ensuring that reports reflect the most current data, preventing outdated insights.

🎬 Testing Report Filters and Aggregations – Verifying the functionality of BI tool filters.

• Testing how filters and aggregations affect data views and report accuracy.

✍ Quiz 6: BI Report Testing

🎬 Defining an End-to-End ETL Testing Process – Apply all concepts to create a complete ETL process.

• Plan and define each stage of the ETL process with data validation points.

🎬 Developing Test Cases for Data Extraction, Transformation, and Loading – Building test cases for each step.

• Testing data at each stage: extraction, transformation, and loading.

🎬 BI Report Testing and Validation – Validating generated BI reports.

• Ensuring the final BI reports reflect accurate and complete data from the warehouse.

📜 Download Final Project Code and Resources

✍ Final Quiz: End-to-End Testing Knowledge Check