Python for Data Engineering Training

Build production data pipelines with this intensive 3-day training. Master pandas and NumPy for data manipulation, design robust ETL processes, and learn to handle real-world data at scale with Python’s powerful data ecosystem.

Training Details


Duration	3 days (24 hours)
Level	Intermediate
Delivery	In-person, Live online, Hybrid
Certification	N/A

Who Is This For?

Python developers moving into data engineering
Data analysts scaling beyond spreadsheets and SQL
Backend engineers building data pipelines
DevOps engineers automating data workflows
Anyone responsible for data quality and delivery

Learning Outcomes

After completing this training, participants will be able to:

Manipulate and transform data efficiently with pandas DataFrames
Perform numerical computations with NumPy arrays and vectorized operations
Design and implement ETL pipelines for batch and incremental processing
Read and write data across formats including CSV, JSON, Parquet, and databases
Validate data quality with schema checks and automated tests
Orchestrate multi-step pipelines with error handling and retry logic

Detailed Agenda

Day 1: Data Manipulation with pandas and NumPy

Module 1: NumPy Foundations

ndarray creation and data types
Indexing, slicing, and boolean masking
Vectorized operations and broadcasting
Hands-on: Process sensor data with NumPy

Module 2: pandas DataFrames

Series and DataFrame creation
Indexing with loc, iloc, and boolean selection
Column operations, dtypes, and missing data
Hands-on: Clean and explore a messy dataset

Module 3: Data Transformation

Filtering, sorting, and grouping
Aggregations, pivot tables, and crosstabs
Merging, joining, and concatenating DataFrames
Hands-on: Combine and summarize multi-source sales data

Day 2: ETL Pipeline Design

Module 4: Reading and Writing Data

CSV, JSON, and Excel I/O with pandas
Working with Parquet and columnar formats
Database connections with SQLAlchemy
Hands-on: Build a multi-format data ingestion layer

Module 5: Data Validation and Quality

Schema validation and type enforcement
Detecting duplicates, outliers, and anomalies
Data quality metrics and reporting
Hands-on: Build a data validation framework

Module 6: ETL Pipeline Patterns

Extract-Transform-Load vs ELT approaches
Incremental loading and change data capture
Idempotent pipeline design
Hands-on: Implement a complete ETL pipeline from raw files to database

Day 3: Production Pipelines

Module 7: Pipeline Orchestration

Task dependencies and execution order
Scheduling with cron and APScheduler
Error handling, retries, and dead-letter queues
Hands-on: Orchestrate a multi-step pipeline with dependency management

Module 8: Performance Optimization

Chunked processing for large datasets
Memory optimization with dtypes and categories
Parallel processing with multiprocessing and Dask
Hands-on: Optimize a slow pipeline to handle 10x data volume

Module 9: Testing and Monitoring

Unit testing data transformations with pytest
Integration testing with test fixtures and sample data
Pipeline monitoring, logging, and alerting
Hands-on: Add comprehensive tests and monitoring to a production pipeline

Prerequisites

Python Fundamentals or equivalent programming experience
Comfort with functions, classes, and file I/O
Basic understanding of SQL and relational databases
Familiarity with CSV and JSON data formats

Delivery Formats

Format	Description
In-Person	On-site at your company’s location, hands-on with direct interaction
Live Online	Interactive virtual sessions with screen sharing and real-time labs
Hybrid	Combination of on-site and remote sessions, flexible scheduling

All formats include hands-on labs, course materials, sample datasets, and post-training support.