Data Science, AI, and Advanced Analytics Nigeria

Applied Data Engineering: Building Scalable Pipelines and ML-Ready Data Systems Course

Applied Data Engineering is the systematic practice of designing and building systems for collecting, storing, and analyzing data at scale. It enables professionals to transform raw, fragmented data into reliable, high-performance assets that power advanced analytics and machine learning. But as data volumes explode and velocity increases, do you know if your current pipeline architecture can handle a 10x surge in traffic without failing or exceeding budgets? In today's landscape, a single bottleneck in an ETL process or a poorly indexed data lake can stall an entire organization's AI strategy. This course bridges the gap by moving beyond basic scripts to professional-grade engineering using Apache Spark, Apache Airflow, and Medallion Architecture while addressing modern pressures like real-time streaming and automated data governance.

This course is the definitive bridge from manual data handling to evidence-based, automated data systems. Can you demonstrate the resilience of your data infrastructure when leadership demands real-time insights for critical decision-making? Designed for Data Engineers, Backend Developers, and Analytics Architects, this program focuses on producing tangible outputs like Orchestration DAGs, Infrastructure as Code (IaC) scripts, and Feature Stores. You will move from conceptual understanding to implementing production-ready pipelines that satisfy both technical performance and business compliance requirements. Applied Data Engineering is more than just moving data; it is about building the scalable foundation for the modern digital enterprise.

Duration
10 Days
Duration
Certificate
Certificate
Included
Delivery
Instructor-Led
Delivery
Level
Intermediate
Level
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Live Online Training

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

Classroom Training

In-person sessions at premier locations

Nairobi Kenya
Mon - Fri
10 Days
USD 3,520
Kigali Rwanda
Mon - Fri
10 Days
USD 4,180
Dubai United Arab Emirates (UAE)
Mon - Fri
10 Days
USD 9,020
Zanzibar Tanzania
Mon - Fri
10 Days
USD 5,280
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In-person training at our premier venues — pick a city and date that works for you.

Location Duration Fee Language
Nairobi, Kenya Mon - Fri (10 Days) USD 3,520 English See dates & reserve →
Kigali, Rwanda Mon - Fri (10 Days) USD 4,180 English See dates & reserve →
Dubai, United Arab Emirates (UAE) Mon - Fri (10 Days) USD 9,020 English See dates & reserve →
Zanzibar, Tanzania Mon - Fri (10 Days) USD 5,280 English See dates & reserve →
Abuja, Nigeria Mon - Fri (10 Days) USD 6,160 English See dates & reserve →
Addis Ababa, Ethiopia Mon - Fri (10 Days) USD 4,900 English See dates & reserve →
Mombasa, Kenya Mon - Fri (10 Days) USD 3,740 English See dates & reserve →
Cape Town, South Africa Mon - Fri (10 Days) USD 8,580 English See dates & reserve →
Johannesburg, South Africa Mon - Fri (10 Days) USD 7,700 English See dates & reserve →
Pretoria, South Africa Mon - Fri (10 Days) USD 7,260 English See dates & reserve →
Kampala, Uganda Mon - Fri (10 Days) USD 4,180 English See dates & reserve →
Lagos, Nigeria Mon - Fri (10 Days) USD 5,500 English See dates & reserve →
Arusha, Tanzania Mon - Fri (10 Days) USD 4,400 English See dates & reserve →
Dar es Salaam, Tanzania Mon - Fri (10 Days) USD 4,180 English See dates & reserve →
Naivasha, Kenya Mon - Fri (10 Days) USD 3,740 English See dates & reserve →

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

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About the Course

Modern organizations demand data results they can prove through high-availability systems and precise data lineage. To succeed in this field, you must demonstrate proficiency in distributed computing, schema evolution, asynchronous processing, cloud cost optimization, and data observability. This course provides a structured system to master these capabilities, moving away from isolated tools toward integrated architectures. You will learn how to turn scattered data sources into a cohesive Data Lakehouse using Delta Lake and Snowflake, ensuring your systems are ready for both human analysts and automated ML models.

Throughout this 10-day intensive, you will practice hands-on with Apache Kafka for streaming and dbt (data build tool) for transformation. You will be introduced to advanced concepts like Kubernetes-based orchestration and FinOps for data at an overview level, while diving deep into pipeline construction and troubleshooting. This course teaches you how to build resilient, self-healing data pipelines through CI/CD workflows and automated testing. By the end of this training, you will have developed a portfolio of work including scalable ETL patterns, automated data quality dashboards, and a fully functional feature store for machine learning applications.

We acknowledge the real-world constraints you face daily, including limited cloud budgets, complex legacy integrations, and the rapid acceleration of regulatory compliance requirements. This course is specifically designed for professionals who must deliver high-performance engineering solutions under these conditions, providing the frameworks and templates necessary to navigate technical debt while implementing cutting-edge technology.


Target Audience

This course is tailored for professionals who are responsible for the architecture, reliability, and scalability of organizational data assets.

This course is designed for:

  • Senior Data Engineers migrating legacy ETL to modern distributed systems
  • Analytics Engineers optimizing dbt transformations for warehouse performance
  • ML Engineers building automated feature pipelines for production models
  • Data Architects designing multi-cloud Lakehouse strategies and governance
  • Backend Developers transitioning into high-scale data infrastructure roles
  • Cloud Solutions Architects overseeing data-intensive application deployments
  • Data Infrastructure Managers balancing engineering velocity with FinOps
  • Reliability Engineers (SRE) specializing in data pipeline observability
  • Technical Leads implementing CI/CD for data engineering teams
  • Database Administrators evolving into cloud-native data engineering experts

Course Objectives

This course equips you to design, execute, and report on data engineering initiatives that ensure high performance, regulatory compliance, and strategic alignment.

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

  • Assess current data infrastructure using the Well-Architected Framework for Data
  • Construct multi-stage ETL pipelines using Apache Spark and Delta Lake
  • Implement real-time streaming architectures using Apache Kafka and Spark Streaming
  • Design automated workflow orchestration using Apache Airflow and Python-based DAGs
  • Execute complex data transformations using dbt (data build tool) for warehouses
  • Evaluate data pipeline performance using specialized observability and monitoring tools
  • Navigate data governance requirements using automated lineage and cataloging systems
  • Synthesize engineering findings into actionable cloud cost-optimization reports

Requirements & Prerequisites

Participants should have a working knowledge of Python and intermediate SQL skills. Familiarity with basic cloud concepts (AWS, Azure, or GCP) and command-line interfaces is highly recommended. Prior experience with data analysis or backend development will be beneficial.


Local Application and Business Return in Nigeria

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

How participants apply this

Participants can use this course to redesign fragile ETL jobs into versioned, testable pipelines that are easier to operate in production. In Nigerian organizations, that typically means building repeatable ingestion flows from core business systems, validating data quality before it reaches dashboards or ML features, and documenting lineage so teams can trace errors quickly. They can also apply medallion-style layering to separate raw, cleansed, and curated data for analytics teams. For backend and platform teams, the course supports better integration between application data, cloud storage, and downstream reporting or feature-serving systems.

Expected ROI

Within 6–12 months, the main return is usually less rework, fewer broken dashboards, and faster delivery of analytics-ready data to business users. Organizations also tend to see better reliability in scheduled jobs, lower time spent on manual data fixing, and improved collaboration between engineering and analytics teams. If the course is applied to production systems, it can shorten the path from raw data capture to trusted decision support. The broader business impact is stronger confidence in reporting and a more stable foundation for machine learning use cases.

Training Methodology

This is a practical, outcome-driven course designed to turn data engineering aspirations into measurable action and credible reporting.

Methodology includes:

  • Hands-on Spark optimization exercise using a multi-terabyte synthetic dataset
  • Scenario simulation requiring architectural decisions for a real-time fintech application
  • Data quality audit using Great Expectations framework and custom checklists
  • Stakeholder reporting workshop focused on pipeline reliability and cost metrics
  • Case study analysis of pipeline failures in E-commerce and Healthcare sectors
  • Group workshop producing a production-ready Airflow DAG for complex ETL
  • Reflection exercise benchmarking current pipeline latency against industry standards

Upcoming Sessions

Next available dates worldwide

Virtual

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

Nairobi

Kenya
USD 3,520
6th Jul-17th Jul 2026

Kigali

Rwanda
USD 4,180
6th Jul-17th Jul 2026

Dubai

United Arab Emirates (UAE)
USD 9,020
6th Jul-17th Jul 2026

Addis Ababa

Ethiopia
USD 4,900
29th Jun-10th Jul 2026

Abuja

Nigeria
USD 6,160
29th Jun-10th Jul 2026

Zanzibar

Tanzania
USD 5,280
13th Jul-24th Jul 2026

Mombasa

Kenya
USD 3,740
29th Jun-10th Jul 2026

Cape Town

South Africa
USD 8,580
6th Jul-17th Jul 2026

Johannesburg

South Africa
USD 7,700
27th Jul-7th Aug 2026

Kampala

Uganda
USD 4,180
29th Jun-10th Jul 2026

Pretoria

South Africa
USD 7,260
6th Jul-17th Jul 2026

Lagos

Nigeria
USD 5,500
20th Jul-31st Jul 2026

Certification

Recognized credentials that advance your career

Participants who complete the Applied Data Engineering: Building Scalable Pipelines and ML-Ready Data Systems 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.

In-Demand Technical Mastery

  • Build production-grade data pipelines hiring managers actively seek on every job posting.
  • Master scalable architectures that power real-world ML systems at leading companies.
  • Bridge the critical gap between raw data and ML-ready feature stores hands-on.

Career Acceleration

  • Data engineers command top-tier salaries — this course fast-tracks your qualification.
  • Graduate with a portfolio of deployable pipeline projects that prove your expertise.
  • Transition from analyst or developer to high-impact data engineering roles confidently.

Applied, Industry-Aligned Learning

  • Every module mirrors actual enterprise workflows — zero theoretical filler, pure application.
  • Train on modern tools like Spark, Airflow, and cloud-native platforms professionals use daily.
  • Solve messy, real-dataset challenges that textbook courses conveniently avoid teaching you.

Tools and platforms relevant to this field

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

2

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.

  • Apache Spark Apache Software Foundation
    Used for distributed data processing when teams need to transform large datasets efficiently.
  • Apache Airflow Apache Software Foundation
    Used to schedule, monitor, and retry pipeline tasks through orchestration DAGs.

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 Nigeria

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

  • Market context
  • Regulatory fit
  • Business application

Why this course matters in Nigeria

A market-specific advisory on the operating pressures this course helps teams address.

Applied data engineering matters in Nigeria because organizations increasingly need reliable pipelines that can support analytics, automation, and machine learning without breaking under scale or inconsistent source data. Teams in banking, telecoms, retail, and public-sector digital programs should pay attention because pipeline failures, poor data quality, and slow orchestration directly affect decision speed, compliance, and customer experience. For leaders, the course helps answer a practical question: whether current data infrastructure can deliver trusted, timely data fast enough to support growth and AI adoption.
ML-ready data is the real bottleneck

In Nigerian organizations pursuing analytics and AI, the limiting factor is often not model choice but whether data is clean, versioned, and consistently delivered through dependable pipelines.

Scale amplifies operational risk

As transaction volumes and digital channels grow, weak orchestration, brittle ETL logic, and manual handoffs create outage risk, delayed reporting, and higher cloud or infrastructure cost.

Cross-functional teams need shared standards

Data engineers, backend developers, analytics architects, and governance teams all benefit from the same operating model: reproducible pipelines, auditable transformations, and documented data ownership.

This training is timely because Nigerian firms are pushing further into real-time analytics, automation, and AI while also facing stronger expectations around data governance and operational resilience. Building scalable pipelines and ML-ready datasets is now a practical capability gap, not a specialist luxury.

Frequently Asked Questions

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

Yes. The course is most useful when teams have outgrown manual scripts and need pipelines that are testable, observable, and easier to maintain in production. It adds the engineering discipline needed to scale beyond one-off workflows.

It is relevant to both. Analytics teams benefit from cleaner, more reliable datasets, while software and platform teams learn how to build data flows that are easier to deploy, monitor, and integrate with applications.

Machine learning projects depend on stable, well-defined data inputs. The course helps teams build pipelines and feature-oriented datasets that reduce drift, duplication, and manual preparation before model training or inference.

They can design ingestion and transformation pipelines, schedule jobs with orchestration tools, structure data into layered architectures, and create the operational controls needed for production data systems.

Customize Training Duration

The standard duration for Applied Data Engineering: Building Scalable Pipelines and ML-Ready Data Systems is 10 Days. The options below are alternative durations with adjusted pricing.

Looking for the standard 10 Days schedule? Use the button below.

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