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 Singapore
How participants can apply the training in local operating conditions, and the return their organisation can plan for.
How participants apply this
Expected ROI
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
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 Singapore teams may encounter, and that may be featured in training where they support the confirmed course scope.
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.
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Apache Airflow Apache Software FoundationUsed to schedule and monitor data pipelines through DAG-based orchestration, which is central to repeatable production workflows.
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Apache Spark Apache Software FoundationUsed for large-scale batch and streaming data processing where distributed compute is needed for transformation and feature engineering.
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Databricks Lakehouse Platform DatabricksUsed to unify engineering and analytics workflows for batch, streaming, and ML-ready data preparation in a managed environment.
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Microsoft Fabric MicrosoftUsed to centralise data ingestion, transformation, governance, and BI-style consumption in organisations standardising on Microsoft tooling.
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Snowflake Data Cloud SnowflakeUsed for scalable warehousing and governed data sharing when teams need separated compute and storage with easier cross-team access.
