Data Engineering with Python and SQL Training Course

 Data Engineering with Python and SQL Training Course 

Introduction 

Data Engineering with Python and SQL Training Course is designed to equip participants with practical and advanced skills in modern data engineering, database management, data integration, and automated data processing. As organizations increasingly depend on big data, artificial intelligence, cloud computing, and real-time analytics, data engineering has become essential for building reliable data infrastructure and supporting data-driven decision-making. This course explores Python programming, Structured Query Language (SQL), relational databases, data modelling, Extract, Transform, Load (ETL) processes, data pipelines, data warehousing, and database optimization. Participants will develop competencies in designing scalable data architectures, automating data workflows, managing complex datasets, and transforming raw information into high-quality, analytics-ready data. 

The training further examines modern data engineering technologies, including Apache Spark, Pandas, PostgreSQL, Apache Airflow, and cloud-based data platforms. Through practical exercises, industry applications, and international case studies, participants will learn how to develop automated data pipelines, implement data quality frameworks, integrate multiple data sources, and optimize database performance. The course also addresses data governance, data security, cloud data engineering, and emerging trends in artificial intelligence-driven data management. By the end of the programme, participants will be equipped to support enterprise data infrastructure, business intelligence, predictive analytics, and digital transformation initiatives across different industries. 

Course Objectives 

By the end of this course, participants should be able to: 

1. Develop Python programming skills for modern data engineering applications.
2. Design and optimize SQL queries for efficient database management.
3. Build automated Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) pipelines.
4. Apply advanced data modelling and relational database management techniques.
5. Develop scalable data pipelines for big data processing.
6. Integrate structured, semi-structured, and unstructured data sources.
7. Implement data cleaning, validation, and quality assurance frameworks.
8. Apply Apache Spark and Python libraries for large-scale data processing.
9. Design enterprise data warehouses and modern data lake architectures.
10. Automate data workflows using Apache Airflow.
11. Implement cloud-based data engineering and data integration solutions.
12. Strengthen data governance, cybersecurity, and database performance optimization.
13. Apply modern data engineering techniques to support artificial intelligence, business intelligence, and predictive analytics.

Organizational Benefits
 

1. Improved enterprise data management and accessibility.
2. Increased operational efficiency through automated data pipelines.
3. Reduced data processing costs and manual workloads.
4. Enhanced data quality, consistency, and reliability.
5. Stronger business intelligence and evidence-based decision-making.
6. Improved integration of organizational information systems.
7. Enhanced scalability of enterprise data infrastructure.
8. Strengthened data security and governance practices.
9. Better support for artificial intelligence and machine learning initiatives.
10. Increased organizational competitiveness through data-driven innovation.

Target Audiences
 

1. Data engineers and database administrators.
2. Data analysts and business intelligence professionals.
3. Software developers and Python programmers.
4. Information technology and systems administrators.
5. Database architects and data scientists.
6. Cloud computing and big data specialists.
7. Monitoring, evaluation, and research professionals.
8. Project managers and digital transformation specialists.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Data Engineering and Modern Data Architectures
 

• Fundamentals of data engineering and enterprise data ecosystems.
• Data engineering lifecycle, workflows, and infrastructure.
• Structured, semi-structured, and unstructured data management.
• Modern data architectures, data lakes, and data warehouses.
• Case study: Netflix's data engineering architecture for large-scale content recommendations.
• Practical introduction to Python, SQL, and data engineering environments.

Module 2: Python Programming for Data Engineering
 

• Python fundamentals, variables, functions, loops, and data structures.
• Data manipulation using Pandas and NumPy.
• File handling, JSON, CSV, XML, and API data extraction.
• Python automation, exception handling, and reusable scripts.
• Case study: Spotify's use of Python-based data processing for music analytics.
• Practical development of Python scripts for data ingestion and transformation.

Module 3: SQL and Advanced Database Management
 

• Relational database concepts and database design principles.
• SQL commands, joins, subqueries, and aggregate functions.
• Advanced SQL queries, indexing, and query optimization.
• Stored procedures, views, transactions, and database security.
• Case study: Amazon's database management and large-scale transactional data processing.
• Practical database development using PostgreSQL and MySQL.

Module 4: Data Extraction, Transformation, and Integration
 

• Designing Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) workflows.
• Data cleaning, normalization, validation, and transformation.
• Integrating APIs, databases, spreadsheets, and external data sources.
• Implementing automated data ingestion and processing pipelines.
• Case study: Walmart's enterprise data integration and supply chain analytics.
• Practical development of Python and SQL-based ETL pipelines.

Module 5: Big Data Processing and Distributed Computing
 

• Introduction to big data technologies and distributed processing.
• Apache Spark architecture and PySpark programming.
• Large-scale data transformation and parallel processing.
• Data partitioning, performance optimization, and distributed storage.
• Case study: Uber's use of distributed data processing for transportation analytics.
• Practical big data processing using Python and Apache Spark.

Module 6: Data Warehousing, Data Lakes, and Pipeline Automation
 

• Data warehouse architecture and dimensional data modelling.
• Data lakes, lakehouse concepts, and modern storage solutions.
• Data pipeline scheduling and workflow orchestration.
• Automated pipeline monitoring using Apache Airflow.
• Case study: Airbnb's data infrastructure for operational and business intelligence.
• Practical development of automated data workflows.

Module 7: Cloud Data Engineering, Security, and Governance
 

• Cloud data engineering fundamentals and platform architecture.
• Introduction to Amazon Web Services, Microsoft Azure, and Google Cloud.
• Data security, access control, encryption, and privacy management.
• Data governance, metadata management, and regulatory compliance.
• Case study: Capital One's cloud-based data infrastructure transformation.
• Practical cloud data integration and secure database management.

Module 8: Advanced Data Engineering Applications and Capstone Project
 

• Real-time data processing and streaming data architectures.
• Data quality monitoring and pipeline performance optimization.
• Integrating data engineering with artificial intelligence and machine learning.
• Developing scalable enterprise data solutions and analytics-ready datasets.
• Case study: LinkedIn's data infrastructure for large-scale professional networking analytics.
• Capstone project: Building an end-to-end data pipeline using Python, SQL, and modern data engineering tools.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical Python programming and SQL database exercises.
• Hands-on development of ETL and automated data pipelines.
• Real-world industry case studies and group assignments.
• Demonstrations of big data, cloud computing, and data engineering technologies.
• Individual capstone projects, practical assessments, and knowledge-sharing sessions.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.