Introduction

In today’s data-driven world, organizations face the challenge of managing vast volumes of evaluation data efficiently. Data lakes and warehouses have emerged as critical solutions for storing, processing, and analyzing diverse datasets from monitoring and evaluation (M&E) programs. Data Lakes and Warehouses for Evaluation Data Training Course provides a deep dive into designing, implementing, and optimizing these systems to enhance decision-making, ensure data integrity, and improve reporting for social programs, public policy initiatives, and organizational evaluation frameworks. Participants will gain practical insights into cloud-based storage, ETL processes, real-time analytics, and governance strategies, equipping them to harness the full potential of structured and unstructured evaluation data.

By bridging theory and practice, this training empowers M&E professionals to leverage advanced data architectures, scalable storage solutions, and smart querying techniques for comprehensive evaluation insights. Learners will explore case studies illustrating successful deployment of data lakes and warehouses, learn to integrate multiple data sources seamlessly, and adopt best practices for security, compliance, and data quality. The course combines hands-on exercises, expert-led discussions, and real-world scenarios to ensure participants leave with actionable skills that improve data-driven decision-making and program impact measurement.

Programme Curriculum

Data Lakes and Warehouses for Evaluation Data Training Course

Introduction

Course Duration

10 days

Course Objectives

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

Understand the fundamentals of data lakes and data warehouses for M&E programs.
Design scalable data architectures for diverse evaluation datasets.
Implement ETL pipelines for seamless data integration.
Apply data modeling techniques for efficient query performance.
Manage structured and unstructured data effectively.
Utilize cloud storage solutions for cost-effective data management.
Implement data governance and security protocols.
Optimize data retrieval and analytics for real-time decision-making.
Analyze evaluation data using business intelligence tools.
Leverage AI and machine learning for predictive evaluation insights.
Ensure data quality, consistency, and compliance.
Interpret insights from case studies and real-world evaluation data.
Develop a roadmap for sustainable and scalable M&E data systems.

Target Audience

M&E Officers and Specialists
Data Analysts and Data Scientists
Program Managers and Evaluators
Policy Analysts and Researchers
IT and Database Professionals
Development Practitioners and NGOs
Government and Public Sector Staff
Academic Researchers and Graduate Students

Course Modules

Module 1: Introduction to Data Lakes and Warehouses

Overview of data lakes and warehouses
Differences and use cases in M&E
Components of modern data architectures
Data storage types: structured vs unstructured
Case study: National health survey data integration

Module 2: Data Governance and Security

Principles of data governance
Security frameworks and compliance
Data privacy and ethical considerations
Role-based access controls
Case study: Secure evaluation data for NGO projects

Module 3: Data Modeling for Evaluation Data

Relational and non-relational models
Star and snowflake schemas
Dimensional modeling for evaluation metrics
Metadata management best practices
Case study: Education program performance metrics

Module 4: ETL Processes and Pipelines

Extract, transform, load fundamentals
Automating ETL for large datasets
Data cleaning and transformation techniques
Scheduling and monitoring ETL jobs
Case study: Multi-source M&E data integration

Module 5: Data Storage Solutions

Cloud vs on-premise storage options
Choosing storage based on data type
Scaling storage for high-volume data
Cost optimization strategies
Case study: Government census data warehouse

Module 6: Querying and Reporting

SQL and NoSQL queries
Ad-hoc reporting techniques
Using dashboards for visualization
Best practices for fast data retrieval
Case study: NGO impact reporting dashboard

Module 7: Real-Time Data Analytics

Streaming data concepts
Real-time evaluation dashboards
Alerts and triggers for program monitoring
Integrating real-time analytics tools
Case study: Health intervention tracking in real-time

Module 8: Business Intelligence Tools for M&E

Overview of BI tools
Connecting BI tools to data warehouses
Creating visualizations for evaluation metrics
Advanced analytics features
Case study: Program success visualization

Module 9: Big Data and Unstructured Data Management

Handling semi-structured and unstructured data
Integrating multimedia, social, and IoT data
Using Hadoop, Spark, or cloud alternatives
Best practices for large-scale evaluation data
Case study: Social media data for community programs

Module 10: Cloud-Based Data Architecture

Cloud service providers overview
Designing scalable cloud data solutions
Multi-region and high-availability architecture
Cost and performance optimization
Case study: Cloud migration of NGO M&E data

Module 11: Data Quality and Validation

Importance of data quality in M&E
Data validation frameworks
Detecting and correcting anomalies
Automation tools for data cleaning
Case study: Reducing errors in survey datasets

Module 12: AI and Machine Learning Integration

Basics of AI/ML for evaluation
Predictive analytics applications
Automated insights for decision-making
Integration with data warehouses
Case study: Predictive modeling for education programs

Module 13: Data Lifecycle Management

Planning data retention and archival
Versioning and change tracking
End-to-end data lifecycle practices
Disaster recovery and backup strategies
Case study: Long-term evaluation program data management

Module 14: Case Studies in Data Lakes and Warehouses

Global best practices
Lessons from health, education, and social programs
Challenges and solutions in real-world projects
Cross-sectoral comparison
Interactive discussion: Problem-solving scenarios

Module 15: Hands-On Practical Lab

Building a small-scale data lake
Loading, transforming, and querying evaluation data
Connecting dashboards for visualization
Troubleshooting common challenges
Group project: End-to-end M&E data pipeline

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

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 Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Data storage design ETL processes Data integration Analytics optimization

Programme Fee

Duration: 10 Days

Method: Online or Physical

Online Price: $2,000

Physical Price: $3,000

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 30 Oct 2026

💻 Online / Virtual session

Online $2,000