30-Day SQL and Azure SQL for Data Engineering Master Guide
For 4–10 Years Experienced Data Engineers
Objective
This guide is designed to:
Build strong SQL fundamentals
Develop advanced query writing skills
Build real-time problem-solving capability
Understand Azure SQL deeply
Learn production-level SQL optimization
Prepare for senior Data Engineering interviews
Target Audience:
Data Engineers
SQL Developers
Azure Data Engineers
ETL Developers
BI Engineers
Daily Time Commitment:
3 Hours Per Day
30 Days Total
Learning Strategy:
20% Theory
80% Hands-On SQL Coding
Goal:
Write optimized SQL queries
Solve real-time business problems
Handle production SQL incidents
Build scalable ETL logic
Understand warehouse design
Optimize millions/billions of rows
DAILY LEARNING STRUCTURE
Hour 1 – Learn Concepts
Focus on:
WHY SQL concepts exist
Query execution understanding
Real-time usage
Optimization mindset
Avoid:
Blind memorization
Watching endless tutorials
Hour 2 – Coding Practice
Focus on:
Writing queries manually
Solving analytical problems
Window function practice
ETL query development
Hour 3 – Real-Time Scenarios
Focus on:
Performance tuning
Deadlock troubleshooting
Incremental loading
Query optimization
Warehouse design
Production debugging
SECTION 1 – SQL BASICS
Topics:
SELECT
WHERE
DISTINCT
TOP
ORDER BY
GROUP BY
HAVING
CASE WHEN
NULL handling
SELECT
Purpose:
Retrieve data.
Practice:
Employee records
Sales reports
Customer details
WHERE
Purpose:
Filter records.
Practice:
Salary filtering
Date filtering
Region filtering
GROUP BY
Purpose:
Aggregation.
Practice:
Revenue by region
Department-wise employee count
Monthly sales trends
HAVING
Purpose:
Filter aggregated results.
CASE WHEN
Purpose:
Conditional logic.
Use Cases:
Risk categorization
Status mapping
Dynamic calculations
NULL HANDLING
Topics:
IS NULL
ISNULL
COALESCE
Real-Time Usage:
Missing data handling
ETL validations
SECTION 2 – JOINS
Topics:
INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL OUTER JOIN
SELF JOIN
CROSS JOIN
OUTER APPLY
INNER JOIN
Purpose:
Match records between tables.
LEFT JOIN
Purpose:
Find unmatched records.
Practice:
Customers without orders
Employees without managers
SELF JOIN
Purpose:
Compare same table data.
Use Cases:
Employee-manager hierarchy
Duplicate detection
OUTER APPLY
Critical Senior-Level Topic.
Use Cases:
Top N per group
Dynamic row processing
SECTION 3 – ADVANCED SQL
Topics:
CTEs
Subqueries
Window functions
Temp tables
Dynamic SQL
CTE (COMMON TABLE EXPRESSIONS)
Purpose:
Readable modular queries.
Practice:
Recursive hierarchy
Deduplication logic
Multi-step calculations
WINDOW FUNCTIONS
Most Important SQL Topic.
Functions:
ROW_NUMBER
RANK
DENSE_RANK
LAG
LEAD
SUM OVER
Use Cases:
Ranking
Running totals
Latest records
Trend analysis
TEMP TABLES
Purpose:
Intermediate processing.
Real-Time Usage:
ETL staging
Complex transformations
DYNAMIC SQL
Purpose:
Dynamic query generation.
Use Cases:
Metadata-driven ETL
Dynamic reporting
SECTION 4 – STORED PROCEDURES AND PROGRAMMABILITY
Topics:
Stored procedures
Functions
Views
Triggers
Transactions
Error handling
STORED PROCEDURES
Purpose:
Reusable SQL logic.
Use Cases:
ETL processing
Reporting logic
Incremental loads
UDFS
Types:
Scalar functions
Table-valued functions
TRIGGERS
Purpose:
Automatic actions.
Use Cases:
Audit tracking
Change logging
TRANSACTIONS
Critical Topic.
Topics:
BEGIN TRAN
COMMIT
ROLLBACK
Understand:
ACID properties
Consistency
Rollback behavior
ERROR HANDLING
Topics:
TRY CATCH
THROW
RAISERROR
SECTION 5 – PERFORMANCE TUNING
Topics:
Execution plans
Indexing
Query optimization
Statistics
Partitioning
EXECUTION PLANS
Critical Senior-Level Topic.
Understand:
Table scans
Index seeks
Key lookups
Hash match
Nested loops
INDEXING
Types:
Clustered
Nonclustered
Composite
Filtered indexes
Practice:
Create indexes
Analyze fragmentation
Optimize slow queries
QUERY OPTIMIZATION
Topics:
SARGability
Predicate pushdown
Join optimization
Reducing scans
STATISTICS
Purpose:
Query optimizer decisions.
PARTITIONING
Purpose:
Handle huge datasets.
Use Cases:
Historical tables
Archival strategies
SECTION 6 – SQL SERVER DMVS
Topics:
sys.dm_exec_requests
sys.dm_exec_query_stats
sys.dm_tran_locks
sys.dm_os_wait_stats
DMVS
Purpose:
Production monitoring.
Use Cases:
Blocking analysis
Query tuning
Resource troubleshooting
SECTION 7 – DATA WAREHOUSING CONCEPTS
Topics:
Fact tables
Dimension tables
Star schema
Snowflake schema
Surrogate keys
SCD
FACT TABLES
Purpose:
Store measurable metrics.
Examples:
Sales
Transactions
Calls
DIMENSION TABLES
Purpose:
Store descriptive attributes.
Examples:
Customer
Employee
Product
STAR SCHEMA
Critical Interview Topic.
Benefits:
Better performance
Simpler joins
SCD TYPES
Topics:
SCD Type 1
SCD Type 2
Practice:
Historical tracking
Merge logic
SECTION 8 – ETL CONCEPTS
Topics:
Incremental loads
CDC
Watermark logic
Upsert and merge
INCREMENTAL LOADS
Purpose:
Load changed data only.
CDC
Purpose:
Track inserts/updates/deletes.
WATERMARK LOGIC
Purpose:
Track latest successful load.
MERGE
Critical Topic.
Use Cases:
Upserts
SCD processing
SECTION 9 – AZURE SQL
Topics:
Azure SQL Database
Managed Instance
Elastic Pools
DTU vs vCore
Geo-replication
Query Store
AZURE SQL DATABASE
Purpose:
Managed cloud SQL service.
Benefits:
Automatic backups
HA/DR
Managed patching
MANAGED INSTANCE
Purpose:
Near full SQL Server compatibility.
ELASTIC POOLS
Purpose:
Resource sharing across databases.
DTU VS VCORE
Critical Interview Topic.
DTU:
Simpler model
vCore:
Better scalability control
GEO-REPLICATION
Purpose:
Disaster recovery.
QUERY STORE
Purpose:
Track query history and regressions.
SECTION 10 – REAL-TIME PRODUCTION SCENARIOS
Topics:
Deadlocks
Blocking
TempDB issues
CPU spikes
Long-running queries
Transaction log full
DEADLOCKS
Critical Senior-Level Topic.
Understand:
Deadlock graphs
Locking behavior
Isolation levels
BLOCKING
Practice:
Identify blocking sessions
Analyze waits
Resolve locks
Commands:
sp_who2
sys.dm_exec_requests
TEMPDB ISSUES
Topics:
Temp spills
Version store growth
Temp table overuse
TRANSACTION LOG ISSUES
Topics:
Log growth
Backup strategies
Recovery models
SECTION 11 – SQL SERVER SECURITY
Topics:
Authentication
Authorization
Roles
Permissions
TDE
Always Encrypted
Row-level security
TDE
Purpose:
Transparent Data Encryption.
ALWAYS ENCRYPTED
Purpose:
Protect sensitive columns.
ROW-LEVEL SECURITY
Purpose:
Restrict row visibility.
SECTION 12 – JSON AND XML SUPPORT
Topics:
OPENJSON
JSON_VALUE
XML parsing
XPath
JSON SUPPORT
Use Cases:
API ingestion
Semi-structured data
XML PARSING
Use Cases:
Legacy integrations
Enterprise systems
SECTION 13 – REAL-TIME ARCHITECTURE
Typical Flow:
Source Systems
↓
ADF / APIs / Kafka
↓
Landing Tables
↓
Staging Layer
↓
Transformation Layer
↓
Warehouse Layer
↓
Power BI Reporting
SECTION 14 – ENTERPRISE ETL DESIGN PATTERNS
Topics:
Audit tables
Error tables
Watermark tables
Control tables
AUDIT TABLES
Purpose:
Track ETL execution.
ERROR TABLES
Purpose:
Capture failed records.
WATERMARK TABLES
Purpose:
Track incremental processing.
SECTION 15 – DOMAIN-WISE SQL CODING QUESTIONS
SALES DOMAIN QUESTIONS
Find highest sales by region.
Find top 5 customers by revenue.
Calculate month-over-month growth.
Find products with declining sales.
Detect duplicate transactions.
Find customers with no purchases.
Calculate running revenue totals.
Find top-selling products monthly.
Generate quarterly sales trends.
Find average order value.
EMPLOYEE DOMAIN QUESTIONS
Find nth highest salary.
Find employees without managers.
Find department-wise salary ranking.
Find employees with maximum overtime.
Calculate attendance percentage.
Find duplicate employee records.
Generate payroll summaries.
Find employees hired in last 30 days.
Find longest-serving employees.
Calculate promotion trends.
CALL CENTER DOMAIN QUESTIONS
Find average handling time.
Detect SLA violations.
Find top-performing agents.
Find repeated escalations.
Generate call resolution trends.
Find abandoned call percentages.
Find peak call hours.
Calculate customer satisfaction metrics.
Find agents with lowest closure rate.
Generate weekly performance reports.
HEALTHCARE DOMAIN QUESTIONS
Detect duplicate claims.
Find patients with repeated admissions.
Calculate average treatment costs.
Find top diagnoses by region.
Detect insurance fraud patterns.
Find doctor-wise patient counts.
Generate hospital occupancy reports.
Find delayed claims.
Calculate medicine utilization.
Generate patient recovery trends.
STOCK MARKET DOMAIN QUESTIONS
Find highest stock growth.
Calculate moving averages.
Detect unusual trading activity.
Find top-performing stocks.
Calculate daily volatility.
Generate trend analysis.
Detect price spikes.
Find stock correlation.
Calculate cumulative returns.
Generate sector performance reports.
SECTION 16 – MID-LEVEL SQL PROJECTS
PROJECT 1 – SALES DATA WAREHOUSE
Requirements:
Create fact/dimension tables
Build incremental loads
Create reporting views
Concepts Used:
Star schema
MERGE
Window functions
PROJECT 2 – EMPLOYEE ANALYTICS SYSTEM
Requirements:
Track employee history
Build payroll reports
Detect anomalies
Concepts Used:
SCD Type 2
Aggregations
Stored procedures
PROJECT 3 – CALL CENTER REPORTING
Requirements:
Process call records
Generate SLA dashboards
Detect escalations
Concepts Used:
Window functions
Performance tuning
PROJECT 4 – INCREMENTAL ETL FRAMEWORK
Requirements:
Build CDC pipeline
Maintain audit logs
Implement watermarking
Concepts Used:
Transactions
MERGE
Error handling
PROJECT 5 – HEALTHCARE CLAIM ANALYTICS
Requirements:
Process millions of claims
Detect duplicates
Optimize queries
Concepts Used:
Partitioning
Indexing
Advanced joins
SECTION 17 – 30-DAY EXECUTION PLAN
WEEK 1 – CORE SQL
Day 1
SELECT
WHERE
GROUP BY
HAVING
Day 2
CASE WHEN
NULL handling
DISTINCT
TOP
Day 3
INNER JOIN
LEFT JOIN
RIGHT JOIN
Day 4
FULL JOIN
SELF JOIN
CROSS JOIN
Day 5
Subqueries
CTEs
Day 6
Window functions
Day 7
Coding practice + mock interview
WEEK 2 – ADVANCED SQL
Day 8
Temp tables
Dynamic SQL
Day 9
Stored procedures
Functions
Day 10
Transactions
Error handling
Day 11
Execution plans
Day 12
Indexing
Statistics
Day 13
Query optimization
Day 14
Advanced coding practice
WEEK 3 – DATA ENGINEERING + SQL
Day 15
Fact tables
Dimension tables
Day 16
Star schema
Snowflake schema
Day 17
Incremental loads
CDC
Day 18
SCD Type 1 & 2
Day 19
MERGE
Watermark logic
Day 20
Audit frameworks
Day 21
Real-time ETL scenarios
WEEK 4 – AZURE SQL + PRODUCTION SCENARIOS
Day 22
Azure SQL basics
Managed Instance
Day 23
Geo-replication
Query Store
Day 24
Deadlocks
Blocking
Day 25
TempDB issues
CPU troubleshooting
Day 26
Security
TDE
Always Encrypted
Day 27
JSON support
XML parsing
Day 28
SQL Agent
Maintenance jobs
Day 29
Domain-wise mock interview
Day 30
FINAL MOCK INTERVIEW + REVISION
REAL-TIME BEST PRACTICES
Always Follow:
Avoid SELECT *
Use proper indexing
Optimize joins
Use explicit transactions carefully
Update statistics regularly
Use partitioning for huge tables
Monitor execution plans
Use incremental processing
Build modular ETL logic
Implement audit logging
MOST IMPORTANT SKILLS FOR SENIOR ENGINEERS
You must become strong in:
Query optimization
Warehouse design
Incremental ETL design
Performance tuning
Deadlock troubleshooting
Production debugging
Scalability thinking
SQL + Azure integration
Real-time analytics support
Enterprise architecture understanding
FINAL INTERVIEW EXPECTATIONS
At 4–10 years experience, interviewers expect:
Strong SQL fundamentals
Advanced analytical query writing
Performance tuning capability
Real-time troubleshooting ability
Warehouse modeling understanding
ETL design capability
Azure SQL understanding
Production support mindset
Scalability thinking
They do NOT expect only SELECT query knowledge.
They expect:
Engineering mindset
Optimization capability
Production troubleshooting
Business problem solving
Enterprise SQL architecture understanding
END OF DOCUMENT
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