README.md

🛠️ Skills & Knowledge

Core SQL Foundations

• Data Definition (DDL): Creating and managing schemas and tables within the Databricks environment.
• Data Manipulation (DML): Inserting, updating, and maintaining data integrity.
• Data Filtering & Matching: Utilizing WHERE, BETWEEN, IN, and LIKE for precise pattern matching and data retrieval.
• Data Transformation:
  • Calculating derived columns (e.g., Price * Qty).
  • Handling NULL values and ensuring data quality.
• Aggregations & Logic: Applying GROUP BY with SUM, AVG, COUNT, MAX, MIN, and filtering results using the HAVING clause.

Advanced Analytical Engineering

• Data Cleansing (Silver Layer): Standardizing inconsistent date formats and stripping string prefixes using REGEXP_REPLACE and COALESCE.
• Set Operations: Performing gap analysis using EXCEPT to identify missing records across datasets.
• Window Functions:
  • Running Totals: Calculating cumulative revenue using SUM() OVER.
  • Delta Analysis: Using LAG() to compute order-over-order performance.
  • Deduplication: Isolating the latest records using QUALIFY with ROW_NUMBER().
• Data Reshaping: Transposing data dimensions using the PIVOT operator for regional reporting.

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📂 Project Structure

1. sql-basic-ddl-dml.sql

• Content: 12 exercises covering the core DDL/DML and aggregation skills.
• Focus: Covers the primary Skills & Knowledge section, including DDL, DML, basic transformations, and core aggregations

2. sql-advanced.sql

• Content: Medallion Architecture (Bronze → Silver → Gold) workflows
• Focus: Advanced cleansing, window functions, and business-ready analytics.

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