Introduction

In today’s rapidly evolving data-driven research landscape, traditional data management systems are no longer sufficient to meet the needs of modern research workflows. The Data Lakehouse Architecture is revolutionizing how research institutions, universities, and organizations handle vast and diverse datasets by combining the best features of data lakes and data warehouses. Data Lakehouse Architecture for Research Data Training Course is designed to equip researchers, data architects, and data analysts with the critical knowledge and technical expertise needed to implement and manage a high-performance data lakehouse system that supports real-time analytics, scalable storage, and structured/unstructured data processing.

Through hands-on labs, real-world case studies, and expert-led modules, participants will explore the integration of big data frameworks (like Apache Spark and Delta Lake) with cloud platforms, ensuring governance, data quality, and enhanced collaboration in research environments. Whether you are dealing with genomic data, climate simulations, or behavioral research, this course offers a comprehensive guide to unlocking the full potential of data lakehouse architecture for scientific research.

Programme Curriculum

Data Lakehouse Architecture for Research Data Training Course

Introduction

Course Objectives

Understand the core concepts of data lakehouse architecture and its relevance in research.
Compare and contrast data lakes, warehouses, and lakehouses.
Learn to design scalable data lakehouse infrastructures using cloud-native tools.
Implement Delta Lake and Apache Iceberg for optimized research data storage.
Enable real-time research analytics using Apache Spark in a lakehouse.
Apply data governance and compliance best practices in research data management.
Leverage machine learning workflows within the lakehouse framework.
Integrate structured and unstructured research data seamlessly.
Perform ETL/ELT operations and automation in a lakehouse environment.
Secure multi-tenant access and ensure role-based data security in academic institutions.
Manage metadata and schema evolution effectively for evolving research needs.
Explore interoperability with scientific tools and languages (Python, R, SQL).
Develop real-world research solutions through guided case studies and team projects.

Target Audiences

Data Scientists involved in scientific or academic research.
Academic Researchers working with large-scale datasets.
University IT Administrators handling research infrastructure.
Data Engineers building big data solutions in research.
Bioinformatics Analysts managing genomic data pipelines.
Climate and Environmental Scientists needing scalable storage systems.
Government Research Institutions adopting cloud-based analytics.
PhD Students and Postdoctoral Researchers exploring advanced data architectures.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Data Lakehouse Architecture

Define data lakehouse and its evolution
Advantages over traditional data lakes/warehouses
Key components: storage, processing, governance
Technologies powering lakehouses (Delta Lake, Apache Spark)
Challenges in research data storage and how lakehouses help
Case Study: Transitioning from a university data warehouse to lakehouse

Module 2: Building a Scalable Lakehouse Infrastructure

Cloud vs on-premise lakehouse platforms
Storage formats and optimization (Parquet, ORC)
Lakehouse with AWS, Azure, Google Cloud
Data ingestion and streaming techniques
Cost management strategies
Case Study: Lakehouse setup for a national weather research center

Module 3: Research Data Processing with Apache Spark

Introduction to Apache Spark for research
Batch vs. stream processing
Research query optimization
Spark SQL for scientific data analytics
Integrating Spark MLlib for predictive modeling
Case Study: Real-time analysis of COVID-19 datasets

Module 4: Delta Lake and Apache Iceberg in Research

ACID transactions for research data
Time travel and version control
Schema evolution in scientific datasets
Partitioning and performance tuning
Iceberg vs. Delta Lake for reproducibility
Case Study: Longitudinal health data storage using Delta Lake

Module 5: Data Governance and Security

Data privacy regulations (HIPAA, GDPR)
Encryption and secure data access
Audit trails and role-based permissions
Metadata cataloging and lineage tracking
Governance tools: Unity Catalog, Apache Ranger
Case Study: Secure access in clinical trial data lakehouse

Module 6: ETL, Automation & Workflow Orchestration

Data wrangling and ETL pipelines with Databricks
Airflow and dbt for workflow orchestration
Automating data quality checks
Data validation and testing frameworks
Continuous integration/continuous deployment (CI/CD)
Case Study: Automating lab sensor data ETL in academic labs

Module 7: Machine Learning and Advanced Analytics

ML lifecycle in a lakehouse environment
Feature store for reproducible research
Model versioning and deployment
Integration with Jupyter, MLflow, TensorFlow
Use of AI for scientific discovery
Case Study: Predicting disease spread using lakehouse ML workflows

Module 8: Future Trends & Real-World Applications

Data mesh and decentralized research data management
Lakehouse for open science and FAIR principles
Multi-language support (SQL, Python, R)
Collaboration tools in research ecosystems
Evaluating ROI and sustainability of lakehouses
Case Study: Implementing a cross-university data mesh lakehouse

Training Methodology

Hands-on Lab Sessions using cloud-based environments
Live Expert Lectures with industry case examples
Group Activities and Peer Learning for collaboration
Downloadable Course Materials & Resources
Final Capstone Project to evaluate application of knowledge
Q&A and Feedback Sessions for clarity and improvement

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 lakehouse design integration analytics

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000