Data Mesh Implementation with Domain-Oriented Ownership & Open Metadata Tools
Build decentralized, domain-driven data platforms with Data Mesh principles and open metadata frameworks like OpenMetadata & Amundsen.
Course Duration: 10 Hours
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Data Mesh Implementation with Domain-Oriented Ownership & Open Metadata Tools – Online Course
As data platforms evolve to handle increasing scale and complexity, the centralized monolithic data lake architecture begins to show its limitations—bottlenecks in ownership, quality, and agility. This course, Data Mesh Implementation with Domain-Oriented Ownership & Open Metadata Tools, helps you break the silos and embrace decentralized data architecture through practical implementation of Data Mesh principles supported by open-source metadata and governance tooling.
Data Mesh is not a technology—it is a socio-technical paradigm that treats data as a product, assigns ownership to domain teams, and enables discoverability and interoperability across the enterprise. To make this vision a reality, organizations must establish strong data governance practices, federated architecture, and metadata management capabilities using tools like OpenMetadata, Amundsen, DataHub, or Marquez.
This course equips you with the foundational understanding and engineering skills required to implement Data Mesh in your organization. You'll learn to set up domains, assign ownership, build productized data assets, and enable data discoverability through metadata catalogs and lineage tracking systems. You’ll also explore how to ensure federated governance, security, and observability in a decentralized ecosystem.
What is Data Mesh?
Data Mesh is a decentralized data architecture that enables domain teams to own, serve, and govern their data products autonomously. It replaces centralized data teams and pipelines with federated teams empowered by standards, platforms, and self-serve tooling—transforming data into a truly scalable and productized asset.
How to Use This Course
- Begin with foundational concepts of Data Mesh theory and motivations.
- Design domain-aligned data teams and identify product boundaries.
- Implement open-source tools for metadata, cataloging, and observability.
- Apply product thinking to your datasets with SLAs, ownership, and documentation.
- Govern and standardize across teams with federated governance models.
By the end of the course, you’ll be prepared to lead or contribute to Data Mesh initiatives within enterprise organizations using practical, open technologies.
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
Course Objectives Back to Top
Upon completion of this course, learners will be able to:
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Understand the four principles of Data Mesh and their organizational implications.
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Design domain-oriented team structures and identify data product boundaries.
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Implement data product thinking with SLAs, contracts, and metadata.
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Use OpenMetadata, Amundsen, or DataHub for metadata management and discovery.
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Enable data lineage and observability for decentralized pipelines.
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Govern metadata and quality through federated governance standards.
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Implement self-serve infrastructure for data ingestion and publishing.
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Monitor compliance, access control, and lifecycle of data products.
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Integrate metadata tools with Snowflake, Airflow, Kafka, and dbt.
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Build a Data Mesh MVP with real domain-aligned datasets.
Course Syllabus Back to Top
Course Syllabus
Module 1: Data Mesh Fundamentals
- Why centralized data architectures fail
- Overview of Data Mesh: principles and anti-patterns
- From data lakes to domain-aligned platforms
Module 2: Data as a Product
- Product thinking in data
- Ownership, documentation, SLAs
- Defining data contracts and quality metrics
Module 3: Domain-Oriented Data Teams
- Organizing by domain
- Building cross-functional data product teams
- Communication between producers and consumers
Module 4: Federated Computational Governance
- Policy standardization without centralization
- Automated governance at scale
- Access control and data privacy
Module 5: Platform Thinking for Data Infrastructure
- Designing self-serve data platforms
- Developer experience for data teams
- CI/CD and infrastructure as code (IaC)
Module 6: Metadata Management with OpenMetadata & Amundsen
- Setting up OpenMetadata with Airflow, Snowflake, and dbt
- Using Amundsen for lineage, search, and documentation
- Role of metadata in discoverability and compliance
Module 7: Data Lineage & Quality Observability
- Automating lineage capture
- Data quality checks with Great Expectations and Soda
- Alerting and monitoring for SLA breaches
Module 8: Catalogs, Tags, and Collaboration
- Data discovery and taxonomy
- Custom metadata fields and annotations
- Managing tags, certifications, and owners
Module 9: Project – Building a Domain-Centric Data Platform MVP
- Identify domains (e.g., HR, Sales, Finance)
- Deploy OpenMetadata or Amundsen
- Model and expose 3–5 data products with contracts and lineage
Module 10: Scaling Data Mesh in the Enterprise
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Measuring success and maturity
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Change management and cultural transformation
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Avoiding vendor lock-in and tool sprawl
Certification Back to Top
Upon successful completion of this course, learners will be awarded the Uplatz Certificate in Data Mesh Architecture & Metadata Engineering. This credential reflects your ability to design and implement decentralized data systems based on the principles of Data Mesh using real-world, open-source metadata management platforms.
Unlike general-purpose data engineering certificates, this course focuses on the emerging capabilities required to build scalable, governed, and domain-owned data platforms. From aligning teams to domains to implementing metadata systems and lineage tracking, this certification validates both strategic insight and technical proficiency.
Earning this certificate positions you as a data leader who understands how to scale data capabilities beyond central teams—one who can drive collaboration, trust, and governance at the intersection of engineering, analytics, and architecture.
Career & Jobs Back to Top
Data Mesh is one of the most disruptive and strategic shifts in modern data architecture. Enterprises seeking to scale their data capabilities and reduce bottlenecks in central data teams are rapidly adopting decentralized approaches—with a growing need for skilled professionals who can lead and implement this transition.
This course prepares you for roles such as:
- Data Mesh Architect
- Metadata Engineer
- Data Platform Product Manager
- Analytics Engineer (Governance Focused)
- Data Product Owner
- Enterprise Data Strategist
Organizations are actively searching for professionals who understand how to combine domain-driven design, platform thinking, and metadata tooling to build resilient and trustworthy data systems. With experience in tools like OpenMetadata, Amundsen, and DataHub, you'll be qualified to build or contribute to self-serve, scalable, and compliant data platforms.
This training is especially valuable for companies in regulated industries—finance, healthcare, insurance, and government—where governance, lineage, and accountability are critical.
Consultants, architects, and senior engineers will also find this skillset valuable in leading modernization efforts across siloed departments and legacy systems. You’ll be prepared to help your organization transition from centralized ETL scripts to an ecosystem of discoverable, managed, and high-quality data products.
Interview Questions Back to Top
1. What are the four principles of Data Mesh?
Domain-oriented ownership, data as a product, self-serve data infrastructure, and federated computational governance.
Domain-oriented ownership, data as a product, self-serve data infrastructure, and federated computational governance.
2. What is a data product in the context of Data Mesh?
A discoverable, trustworthy, and self-contained dataset with defined ownership, SLAs, documentation, and contracts.
A discoverable, trustworthy, and self-contained dataset with defined ownership, SLAs, documentation, and contracts.
3. How does OpenMetadata help in a Data Mesh setup?
It enables data discovery, lineage tracking, quality monitoring, and ownership assignment across domains using open standards.
It enables data discovery, lineage tracking, quality monitoring, and ownership assignment across domains using open standards.
4. What’s the role of federated governance in Data Mesh?
It ensures consistency and compliance through shared policies and automation, without centralizing decision-making.
It ensures consistency and compliance through shared policies and automation, without centralizing decision-making.
5. What is domain-oriented ownership?
Each domain team is responsible for the data it produces and serves, ensuring accountability and quality at the source.
Each domain team is responsible for the data it produces and serves, ensuring accountability and quality at the source.
6. How does metadata support data contracts?
Metadata captures schema, lineage, ownership, and documentation, forming the foundation of machine-readable contracts.
Metadata captures schema, lineage, ownership, and documentation, forming the foundation of machine-readable contracts.
7. What is the difference between OpenMetadata and Amundsen?
Both are metadata platforms, but OpenMetadata offers deeper integration with modern data tools and native support for contracts and quality metrics.
Both are metadata platforms, but OpenMetadata offers deeper integration with modern data tools and native support for contracts and quality metrics.
8. What are some examples of data mesh anti-patterns?
Centralizing platform ownership, skipping product SLAs, and treating data as a byproduct rather than a product.
Centralizing platform ownership, skipping product SLAs, and treating data as a byproduct rather than a product.
9. How can lineage data improve trust in data products?
By showing the data’s source, transformation steps, and usage, it provides transparency and aids debugging and compliance.
By showing the data’s source, transformation steps, and usage, it provides transparency and aids debugging and compliance.
10. How do you scale Data Mesh across multiple business units?
Through platform standardization, federated governance, robust tooling, and cultural alignment around product thinking.
Through platform standardization, federated governance, robust tooling, and cultural alignment around product thinking.
Course Quiz Back to Top
FAQs
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Q1. What are the payment options?
A1. We have multiple payment options:
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A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month.
Q17. Does your course guarantee a job?
A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience.
Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!
Q18. Do you provide placement services?
A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field.
However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
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b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.
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Q21. Can I get help from a tutor if I have doubts while learning from a video course?
A21. Tutor support is not available for our video course. If you believe you require assistance from a tutor, we recommend considering our live class option. Please contact our team for the most up-to-date availability. The pricing for live classes typically begins at USD 999 and may vary.