Data Warehouse Problems & The Need for ETL
📌 Large e-commerce companies like Amazon rely heavily on customer feedback, logistics, and marketing data, highlighting the complexity of managing massive datasets.
📉 Storing data in disparate databases leads to the "dreadful scenario" of inconsistent data formats, duplicate/incorrect records, and missing/incomplete data.
❌ These data quality issues lead to incorrect analysis reports, resulting in bad business decisions that can ultimately cause the business to fail.
💡 The solution lies in the Data Warehouse, conceived by Bill Inmon ("The Father of Data Warehousing"), to create a centralized, structured, and organized data store ready for business analysis.

The ETL Process Overview
⚙️ ETL stands for Extract, Transform, and Load, a core process in Data Engineering designed to move and cleanse data from various sources into the Data Warehouse, analogous to water passing through a filter pipeline.
💪 The Transformation phase is highlighted as the most crucial part of ETL, as failure to handle data issues here results in flawed analysis regardless of subsequent steps.
📊 The ETL process ensures that the data loaded into the Data Warehouse is organized, standardized, and ready for reporting and analytics.

Extraction Methods and Types
🔄 Full Extraction involves pulling all data from the source every time and is suitable only for small tables (e.g., under 100,000 rows) or the initial data load.
📈 Incremental Extraction is more efficient, pulling only new or changed data, typically managed using a timestamp column (e.g., `created_at` or `updated_at`).
🗣️ Extraction methods include Pull Extraction (the ETL pipeline requests data) and Push Extraction (the source system sends data, often facilitated by tools like Kafka for real-time streaming).

Transformation Techniques (Data Cleansing and Structuring)
🧹 Data Cleansing focuses on resolving issues like removing duplicates, handling NULL/missing values (by deleting the row or filling with an average/placeholder), and correcting invalid values (e.g., negative age).
📏 Data Standardization involves unifying formats across all records, such as ensuring phone numbers follow a standard pattern or names are consistently in upper or lower case.
🧮 Data Aggregation summarizes detailed data (e.g., millions of transactions) into smaller, more readable tables (e.g., total sales per category).

Loading and Slowly Changing Dimensions (SCD)
📥 The Load phase moves the cleansed, structured data into the Data Warehouse, using either Batch Processing (scheduled loads, usually daily) or Stream Processing (real-time, requiring complex infrastructure like Kafka or Apache Flink).
💾 Full Load methods include TRUNCATE INSERT (delete everything, then insert) or DROP CREATE INSERT (useful when the schema changes).
🕰️ Slowly Changing Dimensions (SCD) manages how historical changes in dimensional data are tracked:
* SCD Type 0 (No Change): Data is static and cannot be updated (e.g., ID numbers).
* SCD Type 1 (Overwrite): Overwrites the old value with the new one, resulting in loss of history (useful for correcting typos).
* SCD Type 2 (New Row): Adds a new record for the change while marking the old record as expired (usually with `current` flags or date ranges), preserving full historical tracking.

Key Points & Insights
➡️ The ETL process is the backbone of any Data Warehouse; without clean, transformed data, analysis and business decisions will be fundamentally flawed.
➡️ For efficiency, prioritize Incremental Extraction over Full Extraction for large datasets by leveraging timestamp columns.
➡️ When dealing with changes in dimensional attributes (like customer addresses), utilize SCD Type 2 to maintain a complete history for accurate point-in-time analysis.

📸 Video summarized with SummaryTube.com on Dec 25, 2025, 03:50 UTC