Python Data Engineering: Transform Yourself into a Skilled Professional
100% Placement Assistance
The Python Data Engineering program empowers you to become proficient in building and managing data pipelines using Python and big data technologies. This comprehensive training covers essential concepts such as ETL processes, data transformation, automation, and cloud data engineering. You’ll work hands-on with powerful tools like Pandas, PySpark, and Apache Airflow, while learning best practices for data modeling, pipeline optimization, and large-scale data handling. With real-world projects and practical experience, this program will prepare you for a thriving career in Python Data Engineering.
Course Overview
The Python Data Engineering Training Program empowers professionals to thrive in the world of data engineering using Python. Through hands-on projects and expert-led instruction, participants gain mastery in data manipulation, ETL processes, and data analysis techniques. This program prepares participants for the job market with practical labs and interview coaching, setting them up for success in the data engineering field.
Distinctive Advantages
Training Program Outline : Empowering Today's Data Solutions: Python Data Engineering
Module 1: Introduction to Data Engineering
Overview of Data Engineering
- Role and responsibilities of a Data Engineer
- Key concepts: Data Warehousing, ETL/ELT processes, Data Pipelines
Introduction to Python for Data Engineering
- Importance of Python in data engineering
- Overview of Python’s role in data processing and automation
Module 2: • Python Programming
Python Fundamentals
- Variables, Data Types, and Control structures
- Functions and Modules
- Error Handling and Debugging
Working with Data in Python
- Data structures: Lists, tuples, dictionaries, and sets
- File handling: Reading and writing files
Module 3: Advanced Python for Data Engineering
Object-Oriented Programming (OOP) in Python
- Classes and objects
- Inheritance, Polymorphism, and Encapsulation
Python Libraries for Data Engineering
- Pandas for data manipulation
- NumPy for numerical processing
- Working with regular expressions for data parsing
Functional Programming in Python
- Lambda functions, map, filter, and reduce
- Decorators and context managers
Module 4: Data Extraction, Transformation, and Loading
Data Extraction
- Extracting data from various sources (APIs, Databases, Files)
- Web scraping using Python (BeautifulSoup, Scrapy)
Data Transformation
- Data cleaning and preprocessing
- Handling missing data and data normalization
- Aggregation and filtering techniques
Data Loading
- Loading data into relational databases (PostgreSQL, MySQL)
- Loading data into NoSQL databases (MongoDB, Cassandra)
- Best practices for loading data into data lakes
Module 5: Working with Databases and SQL
Relational Databases and SQL
- SQL basics: Select, insert, update, delete
- Joins, subqueries, and CTEs
- Indexing and query optimization
NoSQL Databases
- Overview of NoSQL databases
- Working with MongoDB using Python
- Data modeling in NoSQL databases
Module 6: Data Pipelines and Workflow Orchestration
Building Data Pipelines
- Designing scalable data pipelines
- Introduction to Apache Airflow
- Creating DAGs (Directed Acyclic Graphs) in Airflow
Workflow Automation
- Scheduling and monitoring workflows
- Error handling and retries in data pipelines
- Managing dependencies between tasks
Module 7: Big Data Processing with Python
Introduction to Big Data
- Understanding Big Data concepts and challenges
- Overview of Hadoop and Spark ecosystems
Working with Apache Spark
- Introduction to PySpark
- RDDs (Resilient Distributed Datasets) and DataFrames
Integrating Python with Big Data Tools
- Using Python with Hadoop (Pydoop)
- Working with Hive and HDFS using Python
Module 8: : Data Warehousing and Cloud Integration
Introduction to Data Warehousing
- Concepts of data warehousing and star schema
- Cloud data warehousing platforms
Integrating Python with Cloud Services
- Working with AWS (S3, Lambda, Glue) using Boto3
- Working with Google Cloud (BigQuery, Cloud Storage)
Module 9: Data Engineering with APIs and Streaming Data
Working with APIs
- RESTful API concepts
- Consuming APIs using Python (requests, http.client)
- Building RESTful APIs with Flask/Django
Real-time Data Processing
- Introduction to data streaming concepts
- Working with Apache Kafka using Python
- Real-time analytics with Spark Streaming
Module 10: Capstone Project
Design and Implement a Complete Data Solution
- Participants will design and implement a full data solution using Python and relevant tools
- Presentation and evaluation of the project
- LinkedIn profile optimization and online presence
Module 11: Soft Skills Training, Interview Preparation, & Professional Resume Building
Soft Skills Training
- Communication skills for Data Engineers
- Collaboration in cross-functional teams
- Problem-solving and critical thinking
Interview Preparation
- Common interview questions for Data Engineering roles
- Behavioural interview techniques
- Mock interviews and feedback sessions
Professional Resume Building
- Crafting a resume tailored to Data Engineering roles
- Highlighting technical skills and project experience
- LinkedIn profile optimization and online presence