Created for: Ritik Kumar
Focus Areas: Data Science, Data Engineering, Analytics
Current Level: Intermediate (Based on GitHub profile)
Goal: Master Python for building production-grade data pipelines and analytics solutions
- Roadmap Overview
- Phase 1: Python Fundamentals & Syntax
- Phase 2: Data Structures & Algorithms
- Phase 3: Data Manipulation with Pandas & NumPy
- Phase 4: SQL Integration & Database Connectivity
- Phase 5: Data Visualization
- Phase 6: Web Scraping & APIs
- Phase 7: Data Engineering Fundamentals
- Phase 8: ETL/ELT Pipeline Development
- Phase 9: Cloud Platforms & Big Data
- Phase 10: Machine Learning for Data Engineers
- Phase 11: Advanced Data Engineering
- Phase 12: Production & Best Practices
- Progress Tracking Template
Foundations (2-3 months) → Intermediate (3-4 months) → Advanced (4-6 months) → Expert (Ongoing)
- Beginner: 10-15 hours/week
- Intermediate: 15-20 hours/week
- Advanced: 20+ hours/week
- ✅ Complete hands-on projects
- ✅ Build portfolio repositories
- ✅ Code reviews and refactoring
- ✅ Deploy production systems
Duration: 3-4 weeks | Level: Beginner
- [DONE] Python installation and environment setup
- [DONE] IDEs: VS Code, PyCharm, Jupyter Notebooks
- [DONE] Virtual environments (venv, conda)
- [DONE] pip and package management
- [DONE] Python REPL and interactive mode
- [DONE] Variables and naming conventions
- [DONE] Numbers (int, float, complex)
- [DONE] Strings and string methods
- [DONE] Boolean and None types
- [DONE] Type conversion and casting
- [DONE] Comments and docstrings
- [DONE] Arithmetic operators (+, -, *, /, //, %, **)
- [DONE] Comparison operators (==, !=, <, >, <=, >=)
- [DONE] Logical operators (and, or, not)
- [DONE] Assignment operators (=, +=, -=, etc.)
- [DONE] Identity operators (is, is not)
- [DONE] Membership operators (in, not in)
- [DONE] Bitwise operators
- [DONE] if, elif, else statements
- [DONE] Nested conditionals
- [DONE] Ternary operators
- for loops (range, enumerate, zip)
- while loops
- break, continue, pass
- Loop else clauses
- Function definition and calling
- Parameters and arguments
- Default arguments
- *args and **kwargs
- Return statements
- Lambda functions
- Scope (local, global, nonlocal)
- Recursion basics
Description: Build a script to clean CSV data (remove duplicates, handle missing values, standardize formats)
Skills Applied:
- File I/O
- String manipulation
- Loops and conditionals
- Functions
Deliverables:
# clean_data.py
def remove_duplicates(data):
"""Remove duplicate rows from dataset"""
pass
def handle_missing_values(data, strategy='mean'):
"""Handle missing values using specified strategy"""
pass
def standardize_dates(data, date_column):
"""Convert dates to standard format"""
passDescription: Build a command-line calculator with advanced operations
Features:
- Basic operations (+, -, *, /)
- Advanced operations (power, sqrt, log)
- Memory functions
- History of calculations
Description: Organize files in a directory by type/date
Skills Applied:
- File system operations
- String methods
- Functions
- Error handling basics
Duration: 4-5 weeks | Level: Beginner to Intermediate
- Lists (creation, indexing, slicing, methods)
- Tuples (immutability, packing/unpacking)
- Sets (operations, methods, set theory)
- Dictionaries (keys, values, methods)
- List comprehensions
- Dictionary comprehensions
- Set comprehensions
- Nested data structures
-
collections.defaultdict -
collections.Counter -
collections.namedtuple -
collections.deque -
collections.OrderedDict -
collections.ChainMap
- String formatting (f-strings, .format(), %)
- Regular expressions (re module)
- String parsing and validation
- Unicode and encoding
- Reading files (read(), readline(), readlines())
- Writing files (write(), writelines())
- Context managers (with statement)
- File modes (r, w, a, r+, etc.)
- CSV file operations
- JSON file operations
- Working with paths (pathlib)
- try-except blocks
- Multiple except clauses
- else and finally
- Raising exceptions
- Custom exceptions
- Exception hierarchy
- Searching (linear, binary)
- Sorting (bubble, insertion, merge, quick)
- Time and space complexity (Big O)
- Hash tables concepts
- Stack and queue implementations
Description: Parse and analyze server/application log files
Features:
- Count error types
- Extract timestamps
- Identify patterns
- Generate summary report
- Export to JSON/CSV
Sample Output:
{
"total_lines": 10000,
"errors": 245,
"warnings": 1024,
"info": 8731,
"error_types": {
"404": 120,
"500": 89,
"403": 36
},
"peak_error_time": "2024-01-15 14:23:00"
}Description: Implement common data structures from scratch
Implementations:
- Stack
- Queue
- Linked List
- Binary Search Tree
- Hash Table (basic)
Description: Convert between JSON and CSV formats with data validation
Skills Applied:
- File I/O
- JSON parsing
- CSV operations
- Exception handling
- Data validation
Duration: 5-6 weeks | Level: Intermediate
- NumPy arrays (ndarray)
- Array creation methods
- Array indexing and slicing
- Array operations (broadcasting)
- Mathematical operations
- Statistical functions
- Linear algebra basics
- Random number generation
- Array reshaping and manipulation
- Series and DataFrame
- Reading data (CSV, Excel, JSON, SQL)
- Writing data (various formats)
- Indexing and selection (.loc, .iloc)
- Data inspection (head, tail, info, describe)
- Handling missing data
- Data type conversion
- Removing duplicates
- Handling null values (fillna, dropna)
- String cleaning and normalization
- Data type validation
- Outlier detection and handling
- Data standardization
- Column operations
- apply(), map(), applymap()
- Lambda functions with pandas
- Creating new columns
- Binning and categorization
- One-hot encoding
- Label encoding
- groupby() operations
- Aggregation functions (sum, mean, count, etc.)
- Multiple aggregations
- pivot_table() and crosstab()
- Custom aggregation functions
- Transform and filter operations
- concat() function
- merge() operations
- join() method
- Different join types (inner, outer, left, right)
- Merging on multiple keys
- Handling duplicate columns
- DateTime objects
- Date ranges
- Resampling
- Rolling windows
- Time zone handling
- Date arithmetic
Description: Analyze sales data from an e-commerce platform
Dataset Structure:
order_id, customer_id, product_id, quantity, price, order_date, region, category
Analysis Tasks:
- Calculate total revenue by month
- Top 10 selling products
- Customer segmentation (RFM analysis)
- Regional sales performance
- Product category trends
- Seasonal patterns
Deliverables:
- Cleaned dataset
- Summary statistics
- Visualization-ready DataFrames
- Insights report
Description: Prepare telecom customer data for ML model
Tasks:
- Handle missing values
- Feature engineering (tenure categories, service usage)
- Encode categorical variables
- Normalize numerical features
- Create derived features
- Split into train/test sets
Description: Extract, transform, and load stock market data
Features:
- Download stock data (Yahoo Finance API)
- Calculate technical indicators (SMA, EMA, RSI)
- Identify patterns (trend, support/resistance)
- Portfolio analysis
- Export to database
Code Sample:
import pandas as pd
import numpy as np
def calculate_sma(data, window=20):
"""Calculate Simple Moving Average"""
return data['Close'].rolling(window=window).mean()
def calculate_rsi(data, period=14):
"""Calculate Relative Strength Index"""
delta = data['Close'].diff()
gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
rs = gain / loss
return 100 - (100 / (1 + rs))Duration: 4-5 weeks | Level: Intermediate
- Relational database concepts
- SQL review (SELECT, JOIN, WHERE, GROUP BY)
- Database normalization
- Primary and foreign keys
- Indexes and performance
- sqlite3 (built-in)
- psycopg2 (PostgreSQL)
- pymysql (MySQL)
- pyodbc (SQL Server)
- SQLAlchemy ORM
- Database connection pooling
- INSERT statements
- SELECT queries
- UPDATE operations
- DELETE operations
- Parameterized queries
- Batch operations
- Engine creation
- Table definitions
- ORM models
- Sessions
- Query building
- Relationships (One-to-Many, Many-to-Many)
- Migrations with Alembic
- read_sql() and read_sql_query()
- to_sql() method
- Chunking large datasets
- Query optimization
- Data type mapping
Description: Build a mini data warehouse with fact and dimension tables
Schema:
Fact Table: sales_fact
- sale_id (PK)
- date_id (FK)
- product_id (FK)
- customer_id (FK)
- store_id (FK)
- quantity
- total_amount
Dimension Tables:
- dim_date
- dim_product
- dim_customer
- dim_store
Features:
- Extract data from CSV files
- Transform and validate
- Load into PostgreSQL
- Create aggregated views
- Query optimization
Description: Create a tool to migrate data between different databases
Capabilities:
- Support multiple database types
- Schema comparison
- Data type conversion
- Progress tracking
- Error handling and rollback
- Logging
Description: Sync data between operational database and analytics database
Features:
- Detect changes (CDC pattern)
- Incremental loads
- Conflict resolution
- Scheduling
- Monitoring
Duration: 3-4 weeks | Level: Intermediate
- Figure and axes
- Line plots
- Scatter plots
- Bar charts and histograms
- Pie charts
- Subplots and layouts
- Customization (colors, labels, legends)
- Saving figures
- Statistical plots
- Distribution plots (histplot, kdeplot)
- Categorical plots (boxplot, violinplot)
- Regression plots
- Matrix plots (heatmap)
- Pairplots
- Color palettes and themes
- Interactive plots
- Line and scatter plots
- Bar and histogram charts
- 3D visualizations
- Dashboard creation
- Plotly Express
- Exporting to HTML
- Choosing the right chart type
- Color theory for data viz
- Accessibility considerations
- Dashboard design principles
- Storytelling with data
Description: Create an interactive sales dashboard using Plotly
Visualizations:
- Revenue trend over time
- Top products bar chart
- Geographic sales map
- Customer segments pie chart
- Correlation heatmap
- Filters and dropdowns
Description: Generate weekly/monthly business reports with visualizations
Features:
- PDF report generation
- Multiple chart types
- Summary statistics tables
- Conditional formatting
- Email distribution
Description: Monitor data quality metrics
Metrics:
- Missing value percentages
- Data type violations
- Outlier detection
- Data freshness
- Schema changes
- Historical trends
Duration: 3-4 weeks | Level: Intermediate
- HTML/CSS basics
- requests library
- BeautifulSoup
- Scrapy framework
- Selenium (dynamic content)
- XPath and CSS selectors
- robots.txt and ethics
- Rate limiting and headers
- RESTful API concepts
- HTTP methods (GET, POST, PUT, DELETE)
- requests library advanced
- Authentication (API keys, OAuth)
- Response handling (JSON, XML)
- Error handling and retries
- Pagination
- Rate limiting
- Async requests (aiohttp)
- Concurrent scraping
- Data validation
- Storage strategies
- Incremental updates
Description: Scrape job postings from multiple job boards
Features:
- Multiple sources (LinkedIn, Indeed, etc.)
- Data extraction (title, company, salary, location)
- Deduplication
- SQLite storage
- Daily updates
- Export to CSV
Sample Code:
import requests
from bs4 import BeautifulSoup
import pandas as pd
class JobScraper:
def __init__(self, base_url):
self.base_url = base_url
self.jobs = []
def scrape_page(self, url):
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
# Scraping logic
return jobs_list
def save_to_database(self):
df = pd.DataFrame(self.jobs)
df.to_sql('jobs', engine, if_exists='append')Description: Collect financial data from multiple APIs
Data Sources:
- Alpha Vantage (stock prices)
- News API (financial news)
- Twitter API (sentiment)
- Economic indicators
Output:
- Consolidated database
- Daily updates
- Alerting system
Description: Build pipeline to collect and analyze weather data
Steps:
- Fetch from OpenWeatherMap API
- Store historical data
- Calculate trends
- Generate forecasts
- Visualize patterns
Duration: 4-5 weeks | Level: Intermediate to Advanced
- ETL vs ELT
- Batch vs streaming
- Data lineage
- Data quality
- Idempotency
- Error handling strategies
- Classes and objects
- Inheritance
- Encapsulation
- Polymorphism
- Abstract classes
- Design patterns (Factory, Singleton, Observer)
- SOLID principles
- unittest framework
- pytest
- Test fixtures
- Mocking
- Code coverage
- Integration tests
- Test-driven development (TDD)
- logging module
- Log levels
- Formatters and handlers
- Configuration files
- Structured logging
- Log aggregation concepts
- Environment variables
- Config files (YAML, JSON, TOML)
- python-dotenv
- Secret management
- Configuration validation
- PEP 8 style guide
- Black formatter
- pylint and flake8
- Type hints (mypy)
- Documentation (Sphinx)
- Git best practices
Description: Build a configurable ETL framework
Architecture:
# etl_framework.py
from abc import ABC, abstractmethod
class Extractor(ABC):
@abstractmethod
def extract(self):
pass
class Transformer(ABC):
@abstractmethod
def transform(self, data):
pass
class Loader(ABC):
@abstractmethod
def load(self, data):
pass
class ETLPipeline:
def __init__(self, extractor, transformer, loader):
self.extractor = extractor
self.transformer = transformer
self.loader = loader
def run(self):
# Extraction
data = self.extractor.extract()
# Transformation
transformed_data = self.transformer.transform(data)
# Loading
self.loader.load(transformed_data)Features:
- Pluggable components
- Configuration-driven
- Comprehensive logging
- Unit tests (>80% coverage)
- Documentation
Description: Create a library for data quality checks
Validations:
- Schema validation
- Range checks
- Null checks
- Uniqueness constraints
- Format validation (email, phone, etc.)
- Cross-field validation
- Custom rules
Example Usage:
from data_validator import Validator, Rule
validator = Validator()
validator.add_rule(Rule.not_null('email'))
validator.add_rule(Rule.email_format('email'))
validator.add_rule(Rule.range('age', 0, 120))
results = validator.validate(dataframe)Description: Build monitoring for data pipelines
Features:
- Pipeline execution tracking
- Performance metrics
- Data quality metrics
- Alerting (email, Slack)
- Dashboard (Streamlit)
- Historical analysis
Duration: 5-6 weeks | Level: Advanced
- Apache Airflow concepts
- DAGs (Directed Acyclic Graphs)
- Operators (BashOperator, PythonOperator, etc.)
- Tasks and dependencies
- XComs for data passing
- Sensors
- Hooks for external systems
- Scheduling and triggers
- Backfilling
- Incremental loads
- Full refresh vs upsert
- Slowly Changing Dimensions (SCD)
- Change Data Capture (CDC)
- Partitioning strategies
- Data deduplication
- Data reconciliation
- Chunking large datasets
- Parallel processing (multiprocessing)
- Asynchronous operations (asyncio)
- Memory profiling
- Query optimization
- Indexing strategies
- Compression
- CSV vs Parquet vs Avro
- JSON and JSON Lines
- ORC format
- Compression algorithms
- Schema evolution
Description: Build end-to-end ETL pipeline using Apache Airflow
Workflow:
Extract from API → Validate → Transform → Load to DWH → Generate Report → Send Email
DAG Structure:
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
with DAG(
'sales_etl_pipeline',
start_date=datetime(2024, 1, 1),
schedule_interval='@daily',
catchup=False
) as dag:
extract_task = PythonOperator(
task_id='extract_sales_data',
python_callable=extract_sales
)
validate_task = PythonOperator(
task_id='validate_data',
python_callable=validate_data
)
transform_task = PythonOperator(
task_id='transform_data',
python_callable=transform_data
)
load_task = PythonOperator(
task_id='load_to_warehouse',
python_callable=load_to_warehouse
)
extract_task >> validate_task >> transform_task >> load_taskRequirements:
- Handle failures gracefully
- Retry logic
- Email alerts on failure
- Data quality checks
- Logging
Description: Implement CDC for real-time data sync
Approach:
- Track changes in source database
- Incremental updates only
- Timestamp-based or log-based CDC
- Conflict resolution
- Performance monitoring
Description: Integrate data from multiple sources into data warehouse
Sources:
- REST APIs (JSON)
- CSV files (FTP server)
- Database tables (PostgreSQL)
- Cloud storage (S3)
Features:
- Unified schema
- Data quality rules
- SCD Type 2 implementation
- Monitoring dashboard
- Automated testing
Duration: 6-8 weeks | Level: Advanced
- Cloud service models (IaaS, PaaS, SaaS)
- AWS, Azure, GCP overview
- Cloud storage (S3, Azure Blob, GCS)
- Cloud databases (RDS, Cloud SQL)
- Serverless computing (Lambda, Cloud Functions)
- IAM and security
- boto3 library
- S3 operations (upload, download, list)
- RDS and Redshift
- AWS Glue
- Lambda functions
- DynamoDB
- Kinesis for streaming
- azure-storage-blob library
- Azure Data Factory (ADF)
- Azure Synapse Analytics
- Azure Databricks
- Azure SQL Database
- Event Hubs
- Spark architecture
- RDDs and DataFrames
- Transformations and actions
- Spark SQL
- Reading/writing data
- Performance tuning
- Partitioning and caching
- UDFs (User Defined Functions)
- Data lake concepts
- Delta Lake
- Data catalog
- Data governance
- Medallion architecture (Bronze, Silver, Gold)
Description: Build scalable data lake on AWS
Components:
- S3 for storage (raw, processed, curated)
- Glue for ETL
- Athena for querying
- Lambda for automation
- CloudWatch for monitoring
Data Flow:
Raw Data (S3) → Glue ETL → Processed Data (S3) → Athena Queries → QuickSight Dashboards
Code Sample:
import boto3
import pandas as pd
from io import StringIO
class S3DataLake:
def __init__(self, bucket_name):
self.s3_client = boto3.client('s3')
self.bucket = bucket_name
def upload_dataframe(self, df, key, folder='raw'):
csv_buffer = StringIO()
df.to_csv(csv_buffer, index=False)
full_key = f"{folder}/{key}"
self.s3_client.put_object(
Bucket=self.bucket,
Key=full_key,
Body=csv_buffer.getvalue()
)
def read_dataframe(self, key):
obj = self.s3_client.get_object(Bucket=self.bucket, Key=key)
return pd.read_csv(obj['Body'])Description: Process large datasets using PySpark
Use Case: Process 10+ GB of e-commerce transaction data
Operations:
- Read from S3/HDFS
- Data cleaning and transformation
- Aggregations and joins
- Write to partitioned Parquet
- Performance optimization
Sample Code:
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, avg, count
spark = SparkSession.builder \
.appName("EcommerceETL") \
.getOrCreate()
# Read data
df = spark.read.parquet("s3://bucket/raw/transactions/")
# Transformations
daily_sales = df.groupBy("date", "product_id") \
.agg(
sum("quantity").alias("total_quantity"),
sum("amount").alias("total_revenue"),
count("order_id").alias("order_count")
)
# Write partitioned data
daily_sales.write \
.partitionBy("date") \
.mode("overwrite") \
.parquet("s3://bucket/processed/daily_sales/")Description: Build medallion architecture on Databricks
Layers:
- Bronze: Raw data ingestion
- Silver: Cleaned and validated
- Gold: Business-level aggregates
Features:
- Delta Lake tables
- Streaming ingestion
- Incremental processing
- Data quality checks
- Unity Catalog integration
Duration: 5-6 weeks | Level: Advanced
- Supervised vs unsupervised learning
- Classification vs regression
- Train/test split
- Cross-validation
- Evaluation metrics
- Overfitting and underfitting
- Data preprocessing
- StandardScaler, MinMaxScaler
- Encoding categorical variables
- Feature selection
- Model training
- Model evaluation
- Pipeline creation
- Hyperparameter tuning
- Creating new features
- Binning and discretization
- Polynomial features
- Interaction features
- Date/time features
- Text features (TF-IDF)
- Model serialization (pickle, joblib)
- Model versioning
- Model serving
- Batch predictions
- Real-time predictions
- A/B testing
- Model monitoring
- ML pipelines
- Experiment tracking (MLflow)
- Model registry
- CI/CD for ML
- Data versioning
- Model drift detection
Description: Build ML pipeline for customer segmentation
Steps:
- Data collection from database
- Feature engineering
- K-means clustering
- Segment profiling
- Visualization
- Automated reporting
Deliverables:
- Jupyter notebook with analysis
- Production Python scripts
- Airflow DAG for automation
- Dashboard in Streamlit
Description: Predict future sales using time series
Models:
- ARIMA
- Prophet
- XGBoost
- LSTM (optional)
Pipeline:
- Historical data extraction
- Feature engineering (lag features, moving averages)
- Model training
- Prediction generation
- Results storage
- Accuracy monitoring
Description: Detect anomalies in system logs
Approach:
- Parse log files
- Feature extraction
- Isolation Forest algorithm
- Alert generation
- Dashboard visualization
Use Cases:
- Security threats
- System failures
- Performance degradation
Duration: 6-8 weeks | Level: Expert
- Apache Kafka concepts
- Kafka producers and consumers
- kafka-python library
- Stream processing
- Exactly-once semantics
- Kafka Connect
- Schema Registry
- Apache Flink (PyFlink)
- Spark Structured Streaming
- Window operations
- Stateful processing
- Watermarking
- Data lineage tracking
- Data cataloging
- Metadata management
- Data quality frameworks
- Compliance (GDPR, CCPA)
- Access control
- Window functions
- CTEs (Common Table Expressions)
- Recursive queries
- Pivot and unpivot
- Query optimization
- Execution plans
- CAP theorem
- Consistency models
- Replication strategies
- Sharding
- Message queues
- Docker basics
- Docker Compose
- Kubernetes fundamentals
- Deploying on K8s
- Helm charts
Description: Build real-time analytics using Kafka and Spark Streaming
Architecture:
Data Sources → Kafka → Spark Streaming → PostgreSQL/Redis → Dashboard
Use Case: Real-time website analytics
Metrics:
- Page views per second
- Active users
- Geographic distribution
- Top pages
- Error rates
Tech Stack:
- Kafka for streaming
- Spark Structured Streaming for processing
- Redis for caching
- PostgreSQL for storage
- Grafana for visualization
Description: Track data lineage across pipelines
Features:
- Automatic dependency detection
- Visualization (graph)
- Impact analysis
- Metadata storage
- API for querying
Description: Build scalable multi-tenant data platform
Requirements:
- Tenant isolation
- Resource quotas
- Custom schemas per tenant
- Usage monitoring
- Billing integration
Duration: Ongoing | Level: Expert
- CI/CD pipelines (GitHub Actions, Jenkins)
- Infrastructure as Code (Terraform)
- Configuration management (Ansible)
- Blue-green deployments
- Canary releases
- Rollback strategies
- Prometheus metrics
- Grafana dashboards
- ELK stack (Elasticsearch, Logstash, Kibana)
- Distributed tracing
- SLIs and SLOs
- Alerting best practices
- Encryption (at rest and in transit)
- Secrets management (Vault, AWS Secrets Manager)
- OAuth and JWT
- SQL injection prevention
- Input validation
- Audit logging
- Resource right-sizing
- Auto-scaling
- Spot instances
- Storage tiering
- Query optimization
- Cost monitoring
- Architecture diagrams
- API documentation
- Runbooks
- README best practices
- Code comments
- Knowledge base
- Code reviews
- Git workflows
- Agile methodologies
- Technical documentation
- Knowledge sharing
Description: Build enterprise-grade data platform
Features:
- Multi-environment (dev, staging, prod)
- Automated deployment
- Comprehensive monitoring
- Disaster recovery
- Security hardening
- Documentation
- Cost tracking
Description: Contribute to open-source data engineering projects
Targets:
- Apache Airflow
- Pandas
- Great Expectations
- DBT
- Data quality tools
Contributions:
- Bug fixes
- Documentation
- New features
- Tests
## Month: [Month Year]
### Topics Covered
- [ ] Topic 1
- [ ] Topic 2
- [ ] Topic 3
### Projects Completed
- [ ] Project Name 1
- GitHub Repo: [link]
- Status: ✅ Complete / 🔄 In Progress / ⏸️ Paused
- Key Learnings:
### Skills Acquired
- Technical Skills:
- Skill 1
- Skill 2
- Soft Skills:
- Skill 1
### Challenges Faced
1. Challenge:
- Solution:
### Next Month Goals
- [ ] Goal 1
- [ ] Goal 2
### Resources Used
- Course: [Name - Platform]
- Books: [Title]
- Articles: [Links]- Python Crash Course - Eric Matthes
- Fluent Python - Luciano Ramalho
- Python for Data Analysis - Wes McKinney
- Designing Data-Intensive Applications - Martin Kleppmann
- The Data Warehouse Toolkit - Ralph Kimball
- DataCamp - Data Engineer with Python Track
- Coursera - IBM Data Engineering Professional Certificate
- Udemy - Complete Python Bootcamp & Data Engineering Masterclass
- LinkedIn Learning - Python Essential Training
- r/dataengineering (Reddit)
- Data Engineering Weekly Newsletter
- Local meetups and conferences
- Stack Overflow
- IDEs: VS Code, PyCharm, Jupyter
- Version Control: Git, GitHub
- Containers: Docker
- Cloud: AWS/Azure/GCP free tiers
- Databases: PostgreSQL, MongoDB
- Orchestration: Airflow (local setup)
- Complete all Phase 1-3 topics
- Build 5+ projects
- Comfortable with Pandas and NumPy
- Understand Python syntax and data structures
- Complete all Phase 4-7 topics
- Build 8+ projects
- Deploy pipelines with Airflow
- Integrate databases effectively
- Write clean, tested code
- Complete all Phase 8-10 topics
- Build 5+ production-grade projects
- Work with cloud platforms
- Implement ML pipelines
- Handle big data with Spark
- Complete all Phase 11-12 topics
- Build real-time systems
- Contribute to open source
- Design scalable architectures
- Mentor others
- Build Portfolio: Showcase projects on GitHub with documentation
- Certifications: AWS Certified Data Analytics, Azure Data Engineer Associate
- Networking: Attend conferences, join communities
- Contribute: Open source projects, write blogs
- Apply: Data Engineer positions, freelance projects
- Continue Learning: Stay updated with latest tools and trends
- Pace yourself: This is a comprehensive roadmap. Adjust timeline based on your availability
- Practice daily: Consistency is key. Even 1 hour daily is better than 7 hours once a week
- Build projects: Don't just consume tutorials. Build real projects
- Ask for help: Use Stack Overflow, communities, forums
- Review regularly: Revisit older topics to reinforce learning
- Document everything: Maintain a learning journal
Created by: Ritik
For: Ritik Kumar - Data Engineering Journey
Last Updated: January 2026
Version: 1.0
Happy Learning! 🎉