ETL-Notes

📌 What is ETL?

ETL stands for:

• Extract: Collect data from multiple sources.
• Transform: Clean, format, and apply business logic.
• Load: Insert data into a data warehouse or reporting system.

Layman Analogy: Like making lemonade:

• Extract: Get lemons (data) from the garden (source)
• Transform: Squeeze and filter seeds, mix in sugar (clean/process)
• Load: Pour into a jug (data warehouse)

🚦 ETL vs ELT

Feature	ETL	ELT
Order	Extract → Transform → Load	Extract → Load → Transform
Tools	Traditional ETL tools	Modern/cloud DWH (e.g., Snowflake)
Speed	Slower for big data	Faster (uses warehouse compute)
When to Use	Legacy, strict transformation	Scalable, modern, big data/cloud

🛠️ ETL Process Steps (With Examples)

1. Extract

• Sources: RDBMS (MySQL, SQL Server), flat files (CSV, Excel), APIs (REST, SOAP), cloud (Azure Blob, AWS S3).

Example: Read a CSV with customer orders from Azure Blob Storage.

2. Transform

• Data cleaning (remove nulls, trim spaces)
• Join datasets (customers + orders)
• Aggregate (total sales by region)
• Apply business rules (tax, category labels)
• Date formatting (12-31-2025 → 2025-12-31)

Example: Calculate TotalRevenue = Quantity * Price and flag orders where total > ₹10,000.

3. Load

• Targets: Data warehouse (Snowflake, Azure Synapse), databases (PostgreSQL, SQL Server), data lakes (Azure Data Lake, S3).

Example: Load the cleaned sales data into Azure Synapse for Power BI dashboards.

🧰 Common ETL Tools

🌐 Cloud ETL Platforms

• Azure Data Factory (ADF)
• AWS Glue
• Google Dataflow

⚙️ Open-source Tools

• Apache NiFi
• Talend, Pentaho, Airbyte
• Apache Airflow (orchestration)

💻 Code-based ETL

• Python (Pandas, PySpark)
• SQL Scripts

🏗️ ETL Architecture Layers

• Staging Layer: Temporary storage for raw extracted data. Enables initial cleansing and decouples source systems from the main warehouse.
• Data Integration/Transformation Layer: Main transformation area, where joins, cleans, and aggregations happen.
• Access/Presentation Layer: Presents analytics-ready data for BI tools and users¹².

🗂️ Staging Area Importance

• Isolates raw data for validation, quick re-runs, and recovery in case of failure.
• Handles data from multiple sources/formats and different schedules.
• Critical for auditing and performance³⁴.

🔍 Data Profiling

• Analyzes source data for quality, consistency, uniqueness, and relationships before ETL design.
• Helps identify issues (nulls, formats, outliers) early to reduce errors in data pipelines.
• Often supported by tools like Talend Data Profiler or Informatica⁵⁶.

🧪 Types of Data Transformation

Type	Description	Example
Filtering	Remove unwanted rows	country = 'India'
Aggregation	Sum, average, count	Total sales per region
Joining	Combine tables	orders JOIN customers
Mapping	Replace codes with labels	M → Male
Formatting	Change data formats	31-12-25 → 2025-12-31

🧪 ETL Testing Types

Test Type	Purpose/Example
Source-to-Target	Validate every value loaded properly
Data Transformation	Check rules are applied correctly
Performance	Ensure loads run within time/scale limits
Data Quality	Nulls, range, duplicates, and unique constraints
Regression	Ensure changes don’t break earlier logic
Metadata Testing	Check tables, datatypes, schema, and constraints
Integration Testing	Validate multiple ETL components work together
Production Validation	Confirm target matches production expected outcomes

⏳ Batch vs Streaming ETL

Type	Batch ETL	Streaming ETL
Timing	Periodic (daily/hourly)	Real-time, continuous
Example	Nightly sales load	Fraud detection from live payments
Tools	ADF, Glue, Talend	Kafka, Spark Streaming, Flink

🔁 Full Load vs Incremental Load

• Full Load: Loads all data each run (simple, but slow and resource-heavy)
• Incremental Load: Loads only new/changed data (efficient)
  • Strategies: Timestamps, CDC, row versioning

Example (CDC): Use an updated_at timestamp. Only pull rows with changes since last ETL run.

📘 Real-Life ETL Example

E-commerce Use Case

• Extract:
  • Customer info: MySQL
  • Orders: CSV
  • Product info: REST API
• Transform:
  • Join data
  • Clean nulls/duplicates
  • Calculate TotalAmount
• Load:
  • To Azure Synapse for Power BI

⭐ Data Modeling: Star vs. Snowflake Schema

Feature	Star Schema	Snowflake Schema
Structure	Fact table + denormalized dimensions	Fact table, dimensions normalized/sub-tables
Simplicity	Simple, few joins, fast	Complex, more joins, saves storage
Storage	Higher (redundant)	Lower (less redundancy)
Use Case	Dashboards, fast analytics	Complex hierarchies, normalized WH
Example	Product dimension with all info	Product → Category → Supplier sub-tables

Star Example: Sales fact table linked to Product, Customer, Date (all info in each dimension).

Snowflake Example: Sales fact → Product (basic) → Category (sub-table), Customer → Region.

🧠 ETL Interview Q&A

1. What is SCD (Slowly Changing Dimension)?

• SCD Type 1: Overwrite, no history.
• SCD Type 2: New record for changes, full history (track dates).

Example: Customer moves city:

• Type 1: Update the city.
• Type 2: Insert new record for the new city, keep old for history.

2. How do you ensure data quality?

• Row count validation
• Null checks
• Range validation (e.g., age must be between 0 and 120)
• Detect duplicates
• Hash/checksum comparison

3. What is data lineage?

Shows where data came from, what transformed it, and where it landed. Essential for debugging/audit.

Example: Power BI column "Net Amount" → orders CSV → merged in ETL → loaded to Synapse → renamed "TotalValue".

4. What is a data pipeline?

A series of ETL tasks run automatically, in sequence or parallel, often managed/orchestrated by tools.

Example: Extract → clean → aggregate → load → send report

🏢 Data Architecture: Data Lake vs Data Warehouse vs Data Mart

Aspect	Data Lake	Data Warehouse	Data Mart
Data Type	Raw/unstructured	Processed/structured	Subset of warehouse
Users	Data scientists	Analysts, business users	Department-specific
Example	JSON logs	Tables (Star/Snowflake)	Sales Mart, Finance Mart

⚙️ Optimization Techniques in ETL

• Predicate Pushdown: Apply filters early.
• Projection Pruning: Select only needed columns.
• Partitioning: E.g., data per month for fast scans.
• Parallel Processing: Speed up extraction and loads.
• Bulk/Batched Inserts: Improve load efficiency.
• Indexing: On source/target for faster join/searches.

Example: Store sales by partition: /sales/2025-07/, /sales/2025-08/.

🛡️ Error Handling & Monitoring

• Detect and log failures (missing fields, type errors).
• Store rejected rows separately for review.
• Alerts via email, Slack, etc., on job failure.
• Tools: Airflow UI, ADF Monitor, AWS CloudWatch, custom dashboards.

🗂️ Metadata Management

• What: Metadata = data about data (field types, source/target mapping, lineage).
• Why: Supports debugging, standards enforcement, validation, auditing, governance.
• Example: Ensures a date field is converted and loaded in correct format; flags PII columns for masking⁷⁸.

🚦 ETL Testing Best Practices

• Test both source and target for data accuracy.
• Validate transformation logic with sample queries.
• Analytics-ready data checks (for reporting).
• Automate test cases as much as possible.

🧩 Data Lineage & Data Marts

• Data Lineage: Track each field’s journey (for trust, compliance).
• Data Mart: Focused subset, e.g., separate sales mart for sales team, etc.

⏱️ Orchestration & Scheduling

• Apache Airflow: Python DAGs
• ADF Pipelines: Visual workflow scheduling
• Cron Jobs: Script automation
• AWS Step Functions: Serverless orchestration

🔒 Security in ETL

• Encrypt data at rest and in transit.
• Mask/obfuscate PII (emails, credit cards).
• Role-based access controls.
• Audit logs for sensitive changes/access.

📝 Interview Tips

• Know at least one ETL tool in depth (ADF, Glue, NiFi, etc.).
• Share real life project examples.
• Emphasize error handling, validation, and optimization.
• Be ready to discuss star/snowflake schemas and data lineage.
• Highlight your awareness of data privacy and modern cloud ETL.

✅ Quick Checklist

• Core ETL steps and concepts
• Types of loads & transformation
• Batch vs streaming ETL
• Modern DW models (star/snowflake)
• Security, error handling & optimization
• Testing & validation
• Metadata & lineage
• Real-life/project examples
• Interview-focused Q&A

Keep updating with new tools, practices, and interview trends for a complete, up-to-date ETL resource!

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Footnotes

1. https://www.sprinkledata.com/blogs/the-architecture-of-etl-processes ↩

2. https://discuss.boardinfinity.com/t/explain-the-etl-cycles-3-layer-architecture/11724 ↩

3. https://zilliz.com/ai-faq/what-is-the-role-of-a-staging-area-in-an-etl-architecture ↩

4. https://rivery.io/data-learning-center/etl-process-in-data-warehouse/ ↩

5. https://www.integrate.io/glossary/what-is-data-profiling-in-etl/ ↩

6. https://zilliz.com/ai-faq/how-can-data-profiling-be-used-to-improve-etl-outcomes ↩

7. https://milvus.io/ai-quick-reference/how-does-metadata-management-support-data-quality-in-etl ↩

8. https://zilliz.com/ai-faq/how-does-metadata-management-support-data-quality-in-etl ↩