15-Day Python for Data Engineering Master Guide
For 4–10 Years Experienced Data Engineers
Objective
This guide is designed to:
Build strong Python fundamentals
Develop coding and logical thinking
Prepare for Data Engineering interviews
Help implement Python in real-time projects
Strengthen problem-solving ability
Build production-level coding mindset
Target Audience:
Data Engineers
ETL Developers
SQL Developers
Azure Data Engineers
PySpark Developers
Daily Time Commitment:
3 Hours Per Day
15 Days Total
Learning Strategy:
20% Theory
80% Hands-On Coding
Goal:
Think logically
Write clean Python code
Handle data processing problems
Build reusable utilities
Understand real-time engineering scenarios
Daily Learning Structure
Hour 1 – Learn Concepts
Focus on:
Understanding WHY concepts exist
Real-time use cases
Internal behavior
Best practices
Avoid:
Watching endless tutorials
Memorizing syntax blindly
Hour 2 – Coding Practice
Focus on:
Writing programs manually
Solving logical problems
Building reusable functions
Practicing multiple approaches
Hour 3 – Real-Time Scenarios
Focus on:
File processing
Data transformations
Error handling
API simulations
ETL-style coding
Optimization
SECTION 1 – PYTHON FUNDAMENTALS
Topics:
Variables
Data Types
Input/Output
Operators
Type Casting
Comments
VARIABLES
Purpose:
Store data in memory.
Examples:
Employee Name
Salary
Transaction Amount
File Path
Practice:
Store user details
Store configuration values
Store dynamic calculations
DATA TYPES
Topics:
int
float
str
bool
None
Real-Time Usage:
Transaction amounts
Employee IDs
Flags
Status tracking
Practice:
Check data types
Convert data types
Handle invalid conversions
TYPE CASTING
Topics:
int()
float()
str()
bool()
Practice:
Convert CSV values
Convert API response values
Handle NULL values
OPERATORS
Topics:
Arithmetic
Comparison
Logical
Assignment
Membership
Identity
Real-Time Usage:
Filtering records
Validation logic
Conditional processing
SECTION 2 – CONDITIONALS AND LOOPS
Topics:
if
elif
else
for loop
while loop
break
continue
pass
CONDITIONALS
Purpose:
Decision making.
Practice:
Salary eligibility
Data validation
Null checks
Threshold checks
LOOPS
Purpose:
Process repetitive tasks.
Real-Time Usage:
Processing files
Reading records
ETL row processing
API pagination
Practice:
Print patterns
Iterate over files
Process transactions
SECTION 3 – COLLECTIONS
Topics:
Lists
Tuples
Dictionaries
Sets
Arrays
LISTS
Most important collection type.
Topics:
Append
Insert
Remove
Pop
Sort
Reverse
Slicing
Nested lists
Real-Time Usage:
Store records
File processing
Batch processing
Data transformations
Practice:
Remove duplicates
Sort salaries
Filter transactions
Merge lists
TUPLES
Purpose:
Immutable collections.
Use Cases:
Coordinates
Fixed configurations
Lookup keys
DICTIONARIES
Critical for Data Engineering.
Topics:
Keys
Values
Nested dictionaries
Dictionary methods
Real-Time Usage:
JSON processing
API responses
Configuration handling
Metadata storage
Practice:
Employee mapping
JSON parsing
Aggregation logic
SETS
Purpose:
Unique values.
Use Cases:
Deduplication
Fast lookups
Comparing datasets
Practice:
Remove duplicates
Compare customer lists
ARRAYS
Learn:
Python array module
NumPy basics
Real-Time Usage:
Numerical processing
Data science integration
SECTION 4 – FUNCTIONS
Topics:
Functions
Arguments
Return values
Default arguments
Keyword arguments
Lambda functions
Built-in functions
User Defined Functions (UDFs)
FUNCTIONS
Purpose:
Reusable logic.
Practice:
Salary calculation
Tax calculation
Validation functions
File utilities
DEFAULT ARGUMENTS
Purpose:
Optional parameter handling.
Real-Time Usage:
ETL configurations
Logging utilities
File processing defaults
BUILT-IN FUNCTIONS
Important Functions:
len()
sum()
min()
max()
sorted()
type()
range()
zip()
map()
filter()
enumerate()
Practice:
Aggregation logic
Sorting logic
Filtering logic
LAMBDA FUNCTIONS
Purpose:
Short reusable logic.
Real-Time Usage:
Sorting
Filtering
Transformations
UDFS (USER DEFINED FUNCTIONS)
Critical for Data Engineering.
Practice:
Data cleaning
Null handling
Standardization
Transformation logic
SECTION 5 – STRING HANDLING
Topics:
String methods
Split
Join
Replace
Strip
Find
Formatting
Regex basics
Real-Time Usage
Data cleaning
File parsing
Log processing
CSV transformations
JSON formatting
Practice:
Extract domains from emails
Parse log files
Clean messy text
SECTION 6 – FILE HANDLING
Topics:
Reading files
Writing files
CSV handling
JSON handling
File paths
Context managers
CSV PROCESSING
Critical for Data Engineering.
Practice:
Read CSV
Write CSV
Filter rows
Aggregate data
Validate columns
JSON PROCESSING
Most important for APIs.
Topics:
json.loads
json.dumps
Nested JSON
Practice:
Parse API responses
Flatten JSON
Convert JSON to dictionaries
CONTEXT MANAGERS
Topics:
with open()
Purpose:
Automatic resource management.
SECTION 7 – ERROR HANDLING
Topics:
try
except
finally
raise
custom exceptions
Real-Time Usage
File failures
API failures
Database failures
Invalid data
Missing columns
Practice:
Handle missing files
Handle divide-by-zero
Handle invalid JSON
SECTION 8 – OBJECT ORIENTED PROGRAMMING (OOP)
Topics:
Classes
Objects
Constructors
Instance variables
Methods
Inheritance
Encapsulation
Polymorphism
CLASSES
Purpose:
Reusable object-based design.
Real-Time Usage:
ETL pipelines
Utility frameworks
Database connectors
Logging utilities
Practice:
Employee class
Transaction processor
File reader utility
INHERITANCE
Purpose:
Code reusability.
Real-Time Usage:
Base ETL class
Child pipeline classes
SECTION 9 – DATA STRUCTURES
Topics:
Linked Lists
Stacks
Queues
Searching
Sorting
LINKED LISTS
Purpose:
Understand memory structures.
Interview Importance:
High for logical thinking.
Practice:
Insert node
Delete node
Reverse linked list
STACKS
Use Cases:
Undo operations
Parsing
Backtracking
QUEUES
Use Cases:
ETL processing
Streaming pipelines
Scheduling
SORTING ALGORITHMS
Topics:
Bubble sort
Merge sort
Quick sort
Purpose:
Improve logical thinking.
SECTION 10 – LIST COMPREHENSIONS
Topics:
List comprehensions
Dictionary comprehensions
Set comprehensions
Real-Time Usage
Fast transformations
Filtering datasets
ETL processing
Practice:
Filter active employees
Extract failed records
Transform datasets
SECTION 11 – PYTHON FOR DATA ENGINEERING
Topics:
CSV Processing
JSON Processing
Logging
Config Files
APIs
Database Connections
ETL Pipelines
DATABASE CONNECTIVITY
Learn:
pyodbc
sqlalchemy
Practice:
Connect to SQL Server
Read data
Insert records
Execute stored procedures
LOGGING
Topics:
logging module
Purpose:
Production debugging.
Practice:
Error logs
Audit logs
ETL logs
CONFIG FILES
Learn:
JSON configs
Environment variables
Purpose:
Reusable ETL development.
SECTION 12 – MID-LEVEL PROJECTS
These projects build:
Logical thinking
Reusability mindset
Real-time engineering understanding
Interview confidence
PROJECT 1 – EMPLOYEE DATA PROCESSOR
Requirements:
Read employee CSV
Clean invalid records
Remove duplicates
Generate department reports
Export processed CSV
Concepts Used:
CSV
Functions
Loops
Error handling
Dictionaries
PROJECT 2 – SALES REPORT GENERATOR
Requirements:
Read sales data
Calculate monthly revenue
Find top customers
Generate summary report
Concepts Used:
Aggregations
Sorting
File handling
Functions
PROJECT 3 – API JSON PROCESSOR
Requirements:
Read API JSON response
Flatten nested JSON
Validate fields
Export CSV
Concepts Used:
JSON
Dictionaries
Exception handling
List comprehensions
PROJECT 4 – ETL PIPELINE SIMULATION
Requirements:
Read source CSV
Apply transformations
Validate data
Insert into SQL Server
Generate logs
Concepts Used:
Database connectivity
Logging
Functions
Classes
Error handling
PROJECT 5 – CALL CENTER ANALYTICS
Requirements:
Process call records
Find SLA violations
Generate agent performance metrics
Create escalation reports
Concepts Used:
Lists
Dictionaries
Aggregations
Reporting logic
SECTION 13 – PYTHON INTERVIEW QUESTIONS
BASIC QUESTIONS
Difference between list and tuple.
Difference between set and dictionary.
Mutable vs immutable.
Difference between append and extend.
Difference between remove and pop.
Difference between deep copy and shallow copy.
Explain Python memory management.
Difference between == and is.
Explain list comprehensions.
Explain lambda functions.
INTERMEDIATE QUESTIONS
Reverse a string.
Find duplicate elements.
Count word frequency.
Find second highest number.
Merge two dictionaries.
Flatten nested lists.
Remove duplicates while maintaining order.
Sort dictionary by values.
Find missing numbers.
Implement queue using list.
ADVANCED QUESTIONS
Build mini ETL pipeline.
Process large CSV efficiently.
Parse nested JSON.
Build reusable logger.
Handle millions of records.
Optimize memory usage.
Explain generators.
Explain iterators.
Explain decorators.
Explain multithreading basics.
SECTION 14 – 15-DAY EXECUTION PLAN
WEEK 1 – PYTHON FOUNDATION
Day 1
Variables
Data Types
Operators
Type Casting
Practice:
40 Problems
Day 2
Conditionals
Loops
Break
Continue
Practice:
50 Problems
Day 3
Lists
Tuples
Sets
Practice:
50 Problems
Day 4
Dictionaries
Nested dictionaries
Dictionary methods
Practice:
40 Problems
Day 5
Functions
Built-in functions
Lambda functions
Practice:
40 Problems
Day 6
String handling
Regex basics
Practice:
40 Problems
Day 7
File handling
CSV
JSON
Practice:
Mini Project
WEEK 2 – INTERMEDIATE + REAL-TIME PYTHON
Day 8
Error handling
Logging
Day 9
Classes
Objects
Inheritance
Day 10
Linked Lists
Stacks
Queues
Day 11
List comprehensions
Dictionary comprehensions
Day 12
Database connectivity
SQL integration
Day 13
ETL coding patterns
Config files
Utility functions
Day 14
Mid-level projects
Day 15
FINAL MOCK INTERVIEW + REVISION
SECTION 15 – REAL-TIME PYTHON STRUCTURE FOR DATA ENGINEERING
Typical Project Structure:
project/
│
├── config/
│ └── config.json
│
├── logs/
│ └── app.log
│
├── src/
│ ├── extract.py
│ ├── transform.py
│ ├── load.py
│ ├── validations.py
│ ├── db_connection.py
│ └── utils.py
│
├── data/
│ ├── input/
│ └── output/
│
├── tests/
│ └── test_pipeline.py
│
└── main.py
REAL-TIME ENGINEERING BEST PRACTICES
Always Follow:
Write modular code
Avoid hardcoding
Use logging
Handle exceptions properly
Validate inputs
Use reusable functions
Use configuration files
Write readable code
Use meaningful variable names
Optimize loops
MOST IMPORTANT SKILLS FOR DATA ENGINEERS
You must become strong in:
File processing
JSON handling
SQL integration
ETL logic
Data validation
Exception handling
Reusable utilities
Modular coding
Logical problem solving
Performance thinking
FINAL INTERVIEW EXPECTATIONS
At 4–10 years experience, interviewers expect:
Strong logical thinking
Clean coding practices
Real-time debugging ability
Reusable utility development
ETL understanding
SQL + Python integration
Production mindset
Problem-solving ability
They do NOT expect only syntax memorization.
They expect:
Engineering thinking
Real-time coding capability
Debugging mindset
Optimization mindset
Scalable development understanding
END OF DOCUMENT
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