Data Science, AI, and Advanced Analytics Sri Lanka

Data Warehousing Fundamentals Training Course

Data Warehousing Fundamentals is the systematic process of collecting, organizing, and managing data from disparate sources to provide meaningful business insights. It enables professionals to bridge the gap between raw operational data and strategic intelligence by creating a single version of truth. In an environment where AI-driven analytics and real-time data streaming are becoming standard, the ability to design robust architectures using the Kimball® Lifecycle or Inmon Corporate Information Factory is a critical competency.

This course addresses the modern pressure of migrating legacy on-premise silos to high-performance cloud environments like Snowflake or BigQuery while maintaining strict data integrity. You will move beyond theoretical concepts to master the practical application of ETL/ELT workflows, dimensional modeling, and metadata management. Designed for data engineers, BI analysts, and architects, this training provides the technical blueprints and hands-on experience required to build repositories that support complex analytical queries and machine learning workloads. By the end of this program, you will have produced tangible work products, including source-to-target mappings and star schema designs, positioning you as a practitioner capable of delivering high-value data assets that meet rigorous organizational and regulatory standards.

Duration
5 Days
Duration
Certificate
Certificate
Included
Delivery
Instructor-Led
Delivery
Level
Foundation To Intermediate
Level
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Live Online Training

Join from anywhere with interactive virtual sessions

Starts
Ends
Mon - Fri (10 Days)
USD 1,700
Starts
Ends
Mon - Fri (10 Days)
USD 1,700
Starts
Ends
Weekend (8 Wks)
USD 1,700
Starts
Ends
Mon - Fri (10 Days)
USD 1,700
Starts
Ends
Weekend (8 Wks)
USD 1,700
Starts
Ends
Mon - Fri (10 Days)
USD 1,700
Starts
Ends
Mon - Fri (10 Days)
USD 1,700

Classroom Training

In-person sessions at premier locations

Nairobi Kenya
Mon - Fri
5 Days
USD 1,600
Kigali Rwanda
Mon - Fri
5 Days
USD 1,900
Dubai United Arab Emirates (UAE)
Mon - Fri
5 Days
USD 4,100
Abuja Nigeria
Mon - Fri
5 Days
USD 2,800
Customized Content
Team Training
Flexible Dates

In-person training at our premier venues — pick a city and date that works for you.

Location Duration Fee Language
Nairobi, Kenya Mon - Fri (5 Days) USD 1,600 English See dates & reserve →
Kigali, Rwanda Mon - Fri (5 Days) USD 1,900 English See dates & reserve →
Dubai, United Arab Emirates (UAE) Mon - Fri (5 Days) USD 4,100 English See dates & reserve →
Abuja, Nigeria Mon - Fri (5 Days) USD 2,800 English See dates & reserve →
Addis Ababa, Ethiopia Mon - Fri (5 Days) USD 2,400 English See dates & reserve →
Zanzibar, Tanzania Mon - Fri (5 Days) USD 2,400 English See dates & reserve →
Mombasa, Kenya Mon - Fri (5 Days) USD 1,700 English See dates & reserve →
Cape Town, South Africa Mon - Fri (5 Days) USD 3,900 English See dates & reserve →
Johannesburg, South Africa Mon - Fri (5 Days) USD 3,500 English See dates & reserve →
Pretoria, South Africa Mon - Fri (5 Days) USD 3,300 English See dates & reserve →
Kampala, Uganda Mon - Fri (5 Days) USD 1,900 English See dates & reserve →
Lagos, Nigeria Mon - Fri (5 Days) USD 2,500 English See dates & reserve →
Arusha, Tanzania Mon - Fri (5 Days) USD 2,000 English See dates & reserve →
Dar es Salaam, Tanzania Mon - Fri (5 Days) USD 1,900 English See dates & reserve →
Nakuru, Kenya Mon - Fri (5 Days) USD 3,200 English See dates & reserve →
Naivasha, Kenya Mon - Fri (5 Days) USD 1,700 English See dates & reserve →
Accra, Ghana Mon - Fri (5 Days) USD 3,800 English See dates & reserve →
Kisumu, Kenya Mon - Fri (5 Days) USD 3,200 English See dates & reserve →

Live, instructor-led sessions you can join from anywhere — pick the next start date below.

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DWH-01 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Reserve team seats →
DWH-01 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Reserve team seats →
DWH-01 Weekend (8 Weeks) USD 1,700 Reserve my seat → Reserve team seats →
DWH-01 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Reserve team seats →
DWH-01 Weekend (8 Weeks) USD 1,700 Reserve my seat → Reserve team seats →
DWH-01 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Reserve team seats →
DWH-01 Mon - Fri (10 Days) USD 1,700 Reserve my seat → Reserve team seats →

Our instructor comes to your office — same curriculum and accredited certificate, with case studies built around the work your team actually does.

Team Training

Train your entire team together in a familiar environment for better collaboration

Fully Customized

Content tailored to your industry, tools, and specific business challenges

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Save on travel & accommodation costs when training multiple employees

Flexible Scheduling

Choose dates that work best for your team's availability and projects

How It Works
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2
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About the Course

Modern organizations struggle with fragmented data landscapes that hinder accurate reporting and predictive modeling. This course solves that challenge by providing a structured framework for building integrated data environments. You will learn to transform scattered operational records into a cohesive analytical ecosystem using industry-standard methodologies. The curriculum focuses on the practical capabilities you need to demonstrate daily, including dimensional modeling, ETL pipeline optimization, slowly changing dimension (SCD) management, data profiling, and OLAP cube design. We distinguish between the conceptual understanding of data architecture and the hands-on practice of constructing fact and dimension tables that perform under high-concurrency workloads.

What you will learn: This course provides a comprehensive roadmap for the entire data warehouse development lifecycle. You will practice designing star and snowflake schemas, configuring automated data integration workflows, and implementing metadata repositories. The training covers the transition from traditional ETL to modern ELT patterns, ensuring you can leverage the compute power of cloud-native platforms. You will be introduced to advanced concepts like Data Vault 2.0 and Data Mesh at an overview level while spending the majority of your time applying core dimensional modeling techniques to real-world business scenarios. This approach ensures you leave with a toolkit of templates and checklists ready for immediate deployment in your production environment.

We acknowledge the real-world constraints you face, such as poor source data quality, shifting stakeholder requirements, and the need for high availability. This course is specifically engineered for professionals who must deliver reliable data platforms within these complex operational realities. By focusing on evidence-based practices and internationally recognized standards like ISO/IEC 25012 for data quality, we ensure your technical outputs are both scalable and defensible to leadership.


Target Audience

This course is essential for technical professionals and decision-makers who are responsible for the lifecycle of analytical data within their organizations.

This course is designed for:

  • Data Warehouse Architects responsible for designing scalable enterprise data models
  • ETL Developers tasked with building and optimizing data integration pipelines
  • Business Intelligence Analysts needing to understand underlying data structures for reporting
  • Data Engineers implementing cloud-native data warehousing solutions and ELT workflows
  • Database Administrators managing the performance and security of analytical databases
  • Data Quality Specialists focused on profiling and cleansing source system data
  • Analytics Managers overseeing the delivery of cross-functional reporting platforms
  • Data Governance Officers ensuring compliance and metadata standards across the warehouse
  • Systems Analysts documenting source-to-target mappings and business transformation rules
  • Solution Architects integrating data warehouses with downstream AI and ML applications

Course Objectives

The curriculum is designed to move you from foundational concepts to intermediate implementation skills through a series of structured technical milestones.

By the end of this course, you'll be able to:

  • Assess current data landscapes using the Kimball® Dimensional Modeling techniques
  • Apply Star Schema and Snowflake Schema patterns to complex business processes
  • Construct robust ETL/ELT pipelines using source-to-target mapping documentation
  • Design Slowly Changing Dimension (SCD) strategies to track historical data accuracy
  • Evaluate data warehouse performance using indexing, partitioning, and materialized views
  • Navigate data governance requirements using ISO/IEC 38505-1 governance frameworks
  • Implement automated data quality checks within the integration workflow
  • Synthesize technical requirements into a comprehensive Data Warehouse implementation roadmap

Requirements & Prerequisites

Participants should have a foundational understanding of Relational Database Management Systems (RDBMS) and basic proficiency in SQL. Familiarity with general business reporting requirements and data analysis concepts is recommended. No prior experience with specific data warehousing tools is required, as the course focuses on platform-agnostic methodologies.


Local Application and Business Return

How participants can apply the training in local operating conditions, and the return their organisation can plan for.

How participants apply this

Participants apply this course by designing warehouse structures that consolidate data from finance, sales, operations, and customer systems into a single analytical layer. In Sri Lankan organisations, that typically means defining source-to-target mappings, building star schemas, and setting up ETL or ELT pipelines that keep reporting data consistent across departments. They also work on metadata, data quality rules, and history handling so analysts can trust month-end, compliance, and management dashboards. When teams move from fragmented spreadsheets or legacy databases to cloud analytics platforms, these skills help them preserve data integrity while improving query performance and reuse.

Expected ROI

Within 6–12 months, the main return is usually faster reporting cycles, fewer manual reconciliation steps, and more reliable metrics for management decisions. Teams that standardise dimensional models and ETL/ELT logic typically spend less time fixing inconsistent reports and more time supporting analysis. The business impact is strongest where multiple departments previously kept separate versions of the truth. Organisations also benefit from better auditability and easier migration from legacy systems to cloud warehouses.

Training Methodology

This is a practitioner-led course that prioritizes the creation of tangible technical deliverables over passive theory.

Methodology includes:

  • Hands-on dimensional modeling exercise using a retail or financial dataset
  • Scenario simulation requiring schema redesign for a rapidly changing business
  • Data profiling diagnostic using a standardized data quality checklist
  • Source-to-target mapping workshop for a multi-source integration project
  • Case study analysis of cloud migration failures in the healthcare sector
  • Group workshop building a functional ETL pipeline prototype in class
  • Benchmarking exercise comparing query performance across different indexing strategies

Upcoming Sessions

Next available dates worldwide

Virtual

(Zoom) Training
USD 1,700
29th Jun-10th Jul 2026

Nairobi

Kenya
USD 2,900
20th Jul-31st Jul 2026

Kigali

Rwanda
USD 3,800
20th Jul-31st Jul 2026

Dubai

United Arab Emirates (UAE)
USD 7,800
29th Jun-10th Jul 2026

Addis Ababa

Ethiopia
USD 2,500
22nd Jun-3rd Jul 2026

Abuja

Nigeria
USD 2,800
22nd Jun-26th Jun 2026

Zanzibar

Tanzania
USD 4,300
6th Jul-17th Jul 2026

Mombasa

Kenya
USD 3,200
22nd Jun-3rd Jul 2026

Cape Town

South Africa
USD 7,500
27th Jul-7th Aug 2026

Johannesburg

South Africa
USD 6,000
13th Jul-24th Jul 2026

Kampala

Uganda
USD 3,700
29th Jun-10th Jul 2026

Pretoria

South Africa
USD 5,900
20th Jul-31st Jul 2026

Lagos

Nigeria
USD 2,500
6th Jul-10th Jul 2026

Certification

Recognized credentials that advance your career

Participants who complete the Data Warehousing Fundamentals Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.

NITA Accredited

Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.

CPD Certified

Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.

Why this course earns its place on your CV

Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.

Career Advancement

  • Gain in-demand data warehousing skills, boosting your career potential significantly.
  • Equip yourself with expertise sought after by top tech companies worldwide.
  • Master data warehousing to elevate your professional profile and marketability.

Expert Delivery

  • Learn from industry leaders with over 20 years of real-world data warehousing experience.
  • Courses designed by experts to ensure practical knowledge that applies immediately.
  • Benefit from personalized feedback from seasoned professionals in the field.

Practical Skills Application

  • Engage in hands-on projects that simulate real data warehousing challenges.
  • Transform raw data into actionable insights with expert-led training.
  • Acquire skills that directly improve data management efficiency in your organization.

Tools and platforms relevant to this field

Examples Sri Lanka teams may encounter, and that may be featured in training where they support the confirmed course scope.

5

These are field-relevant examples, not a promise that every tool will be covered. Exact coverage depends on the confirmed course scope, participant needs, and delivery format.

  • Snowflake Snowflake Inc.
    Used for cloud data warehousing, scalable analytical queries, and separating storage from compute in modern warehouse architectures.
  • Google BigQuery Google
    Used for serverless analytics on large datasets and for building SQL-based reporting layers without managing warehouse infrastructure.
  • Microsoft Power BI Microsoft
    Used to connect warehouse tables to dashboards and operational reporting for business users.
  • Microsoft SQL Server Microsoft
    Used as a source system or on-premises warehouse platform in legacy environments that are being migrated to cloud analytics stacks.
  • Oracle Database Oracle
    Used as an enterprise source system or reporting repository when integrating operational data into a warehouse.

Real Results from Real Professionals

Thousands of professionals have transformed their careers through our training programs. Now, it's your turn.

Local market advisory

Course relevance for Sri Lanka

A country-specific view of market pressure, regulatory context, and practical business return behind this training.

  • Market context
  • Regulatory fit
  • Business application

Regulatory context in Sri Lanka

The local regulators, laws, and frameworks shaping this discipline, with the curriculum mapped to what teams need to know.

1

Regulators

  • PDPA Relevant for warehouse projects that store, transform, or report on personal data and need lawful processing, security, and retention controls.

Frameworks the course aligns with

  • 01 Personal Data Protection Act, No. 9 of 2022 · 2022

Frequently Asked Questions

Got questions? We've gathered the answers to common queries to help you feel confident and informed.

Yes. SQL and BI tools help you query and present data, but this course focuses on how the warehouse is designed, loaded, and governed. That includes dimensional modelling, ETL/ELT design, and metadata management.

Yes. The core concepts are the same, but the implementation changes across platforms. The course is relevant whether you are working with legacy databases or modern cloud warehouses such as Snowflake or BigQuery.

Typical deliverables include source-to-target mappings, star schema designs, and documented transformation rules. These outputs are directly useful for implementation teams and for governance review.

Yes. Those topics are essential in warehouse projects because they affect trust, lineage, and maintainability. Good metadata and validation rules make it easier to trace data from source systems to reports.

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