MasterFaculty of Computing and Software Engineering

Big Data Architecture and Data Lake Management

Welcome to the advanced study of data infrastructure! I am Prof. Dr. Nina Napitupulu. As a professor and a pioneering force in the field of Big Data Architecture and Data Lake Management, I bring a unique blend of engineering expertise and data insight to the study of data systems. I am honored to lead the Big Data Architecture and Data Lake Management (M.Sc.) program at Nexier University. My motto is: "Architecting the Future of Data".

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Level: Master
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

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The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the design of modern data architectures, specializing in building and managing data lakes, data warehouses, and lakehouse architectures to support large-scale analytics and AI.

Practical focus

Advanced data engineering, data modeling, data governance, expertise in cloud data platforms (e.g., Snowflake, Databricks), leadership in data platform teams.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in technology companies or data consulting firms
Roles as data architects or data governance specialists
Consultancy in advanced big data architecture and data lake management
Support roles in academic research projects on big data architecture

Career opportunities

Chief Data Officer (CDO) for large corporations or data consulting firms
Data Architect for large-scale data platforms
Data Lake Engineer for analytics platforms
Researcher in Big Data Architecture and Data Lake Management

Jobs and projects

Cultivating an interdisciplinary approach, integrating computer science, database management, and data analytics
Developing strategic thinking for big data solutions and data lake management
Enhancing problem-solving through the analysis of complex data management challenges
Critical thinking for a comprehensive and nuanced understanding of Big Data Architecture and Data Lake Management

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering advanced practical skills in Advanced data engineering and data modeling.
- Gaining expertise in data governance and expertise in cloud data platforms (e.g., Snowflake, Databricks).
- Developing problem-solving abilities for complex Leadership in data platform teams.
- Cultivating an interdisciplinary approach, integrating computer science, database management, and data analytics at an advanced level.
Skills you build
- Mastering AI-powered techniques for data architecture synthesis.
- Applying advanced engineering principles to big data architecture and data lake management.
- Interpreting and analyzing complex data architectures and their implications for large-scale analytics and AI.
- Identifying optimal data storage and processing patterns and predicting query performance.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Big Data Architecture and Data Lake Management

01Modern Data Architectures and Design
1. FoundationsFoundations of Modern Data Architectures and Design
  The learner can master advanced practical skills in Advanced data engineering and data modeling, as applied to Modern Data Architectures and Design.
  The learner can gain expertise in data governance and expertise in cloud data platforms (e.g., Snowflake, Databricks), as applied to Modern Data Architectures and Design.
2. MethodsMethods in Modern Data Architectures and Design
  The learner can develop problem-solving abilities for complex Leadership in data platform teams, as applied to Modern Data Architectures and Design.
  The learner can cultivating an interdisciplinary approach, integrating computer science, database management, and data analytics at an advanced level, as applied to Modern Data Architectures and Design.
3. ApplicationApplication of Modern Data Architectures and Design
  The learner can master AI-powered techniques for data architecture synthesis, as applied to Modern Data Architectures and Design.
  The learner can apply advanced engineering principles to big data architecture and data lake management, as applied to Modern Data Architectures and Design.
02Data Lake and Data Warehouse Management
1. FoundationsFoundations of Data Lake and Data Warehouse Management
  The learner can interpreting and analyze complex data architectures and their implications for large-scale analytics and AI, as applied to Data Lake and Data Warehouse Management.
  The learner can identify optimal data storage and processing patterns and predicting query performance, as applied to Data Lake and Data Warehouse Management.
2. MethodsMethods in Data Lake and Data Warehouse Management
  The learner can apply a method from Data Lake and Data Warehouse Management to a documented case.
  The learner can select an appropriate method from Data Lake and Data Warehouse Management for a stated problem.
3. ApplicationApplication of Data Lake and Data Warehouse Management
  The learner can evaluate a practice of Data Lake and Data Warehouse Management against a stated criterion.
  The learner can transfer Data Lake and Data Warehouse Management to a new documented context.
03Lakehouse Architectures for Unified Analytics
1. FoundationsFoundations of Lakehouse Architectures for Unified Analytics
  The learner can explain the core terms of Lakehouse Architectures for Unified Analytics.
  The learner can distinguish related ideas inside Lakehouse Architectures for Unified Analytics.
2. MethodsMethods in Lakehouse Architectures for Unified Analytics
  The learner can apply a method from Lakehouse Architectures for Unified Analytics to a documented case.
  The learner can select an appropriate method from Lakehouse Architectures for Unified Analytics for a stated problem.
3. ApplicationApplication of Lakehouse Architectures for Unified Analytics
  The learner can evaluate a practice of Lakehouse Architectures for Unified Analytics against a stated criterion.
  The learner can transfer Lakehouse Architectures for Unified Analytics to a new documented context.
04Scalable Data Governance and Data Quality
1. FoundationsFoundations of Scalable Data Governance and Data Quality
  The learner can explain the core terms of Scalable Data Governance and Data Quality.
  The learner can distinguish related ideas inside Scalable Data Governance and Data Quality.
2. MethodsMethods in Scalable Data Governance and Data Quality
  The learner can apply a method from Scalable Data Governance and Data Quality to a documented case.
  The learner can select an appropriate method from Scalable Data Governance and Data Quality for a stated problem.
3. ApplicationApplication of Scalable Data Governance and Data Quality
  The learner can evaluate a practice of Scalable Data Governance and Data Quality against a stated criterion.
  The learner can transfer Scalable Data Governance and Data Quality to a new documented context.
05Performance Optimization for Big Data Systems
1. FoundationsFoundations of Performance Optimization for Big Data Systems
  The learner can explain the core terms of Performance Optimization for Big Data Systems.
  The learner can distinguish related ideas inside Performance Optimization for Big Data Systems.
2. MethodsMethods in Performance Optimization for Big Data Systems
  The learner can apply a method from Performance Optimization for Big Data Systems to a documented case.
  The learner can select an appropriate method from Performance Optimization for Big Data Systems for a stated problem.
3. ApplicationApplication of Performance Optimization for Big Data Systems
  The learner can evaluate a practice of Performance Optimization for Big Data Systems against a stated criterion.
  The learner can transfer Performance Optimization for Big Data Systems to a new documented context.
06Advanced Data Engineering and Modeling
1. FoundationsFoundations of Advanced Data Engineering and Modeling
  The learner can explain the core terms of Advanced Data Engineering and Modeling.
  The learner can distinguish related ideas inside Advanced Data Engineering and Modeling.
2. MethodsMethods in Advanced Data Engineering and Modeling
  The learner can apply a method from Advanced Data Engineering and Modeling to a documented case.
  The learner can select an appropriate method from Advanced Data Engineering and Modeling for a stated problem.
3. ApplicationApplication of Advanced Data Engineering and Modeling
  The learner can evaluate a practice of Advanced Data Engineering and Modeling against a stated criterion.
  The learner can transfer Advanced Data Engineering and Modeling to a new documented context.
07Data Governance and Cloud Platforms
1. FoundationsFoundations of Data Governance and Cloud Platforms
  The learner can explain the core terms of Data Governance and Cloud Platforms.
  The learner can distinguish related ideas inside Data Governance and Cloud Platforms.
2. MethodsMethods in Data Governance and Cloud Platforms
  The learner can apply a method from Data Governance and Cloud Platforms to a documented case.
  The learner can select an appropriate method from Data Governance and Cloud Platforms for a stated problem.
3. ApplicationApplication of Data Governance and Cloud Platforms
  The learner can evaluate a practice of Data Governance and Cloud Platforms against a stated criterion.
  The learner can transfer Data Governance and Cloud Platforms to a new documented context.
08Leadership in Data Platform Teams
1. FoundationsFoundations of Leadership in Data Platform Teams
  The learner can explain the core terms of Leadership in Data Platform Teams.
  The learner can distinguish related ideas inside Leadership in Data Platform Teams.
2. MethodsMethods in Leadership in Data Platform Teams
  The learner can apply a method from Leadership in Data Platform Teams to a documented case.
  The learner can select an appropriate method from Leadership in Data Platform Teams for a stated problem.
3. ApplicationApplication of Leadership in Data Platform Teams
  The learner can evaluate a practice of Leadership in Data Platform Teams against a stated criterion.
  The learner can transfer Leadership in Data Platform Teams to a new documented context.
09Case Studies in Big Data Architecture and Data Lake Management
1. FoundationsFoundations of Case Studies in Big Data Architecture and Data Lake Management
  The learner can explain the core terms of Case Studies in Big Data Architecture and Data Lake Management.
  The learner can distinguish related ideas inside Case Studies in Big Data Architecture and Data Lake Management.
2. MethodsMethods in Case Studies in Big Data Architecture and Data Lake Management
  The learner can apply a method from Case Studies in Big Data Architecture and Data Lake Management to a documented case.
  The learner can select an appropriate method from Case Studies in Big Data Architecture and Data Lake Management for a stated problem.
3. ApplicationApplication of Case Studies in Big Data Architecture and Data Lake Management
  The learner can evaluate a practice of Case Studies in Big Data Architecture and Data Lake Management against a stated criterion.
  The learner can transfer Case Studies in Big Data Architecture and Data Lake Management to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of Mastering the design of modern data architectures, specializing in building and managing data lakes, data warehouses, and lakehouse architectures to support large-scale analytics and AI. My work seamlessly integrates computer science, database management, and data analytics. I am widely recognized for my contributions, with publications like "Data Lakehouse Architecture for Unified Analytics and Machine Learning" and "Advanced Data Governance Frameworks for Cloud Data Platforms" listed on these platforms. I hold prestigious memberships as a "Chief Data Officer (CDO)" at a Fortune 500 company (or a equivalent) and a "Keynote Speaker" at the Data + AI Summit. My thought leadership is evident through my advanced research on scalable data governance, data modeling for complex analytics, and the future of unified data platforms, frequently featured in publications like Journal of Big Data Architecture or IEEE Transactions on Knowledge and Data Engineering.

Applied mentorship

My expertise lies in understanding and navigating the advanced technical challenges of big data architecture, focusing on Advanced data engineering, data modeling, data governance, expertise in cloud data platforms (e.g., Snowflake, Databricks), and Leadership in data platform teams. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on big data architecture and data lake management:

Blog Post (Current Academic Topic): "The Data Lakehouse Paradigm: Unifying Data Warehousing and Data Lake Capabilities." This blog post academically explores the emerging data lakehouse architecture, which aims to combine the flexibility and scalability of data lakes with the data management and ACID (Atomicity, Consistency, Isolation, Durability) properties of data warehouses. It discusses how this hybrid approach supports both traditional business intelligence and advanced machine learning workloads, enabling a more efficient and unified data platform strategy.

Blog Post (Controversial Topic): "The Algorithmic Oracle: When Big Data Predicts Your Every Move – The Ethical Dilemma of Hyper-Personalized Futures." This article provocatively discusses the highly controversial future where advanced big data systems, combined with sophisticated AI, can predict individual behaviors, preferences, and even life events with unprecedented accuracy. It questions whether this hyper-predictive capability, despite its potential for personalized services, could inadvertently lead to a loss of free will, algorithmic discrimination, or a surveillance society. It raises profound ethical questions about data privacy, algorithmic transparency, and the imperative to ensure human autonomy in a data-driven world.

Article: "Designing Scalable Data Governance Frameworks for Cloud Data Platforms." This article details the principles and best practices for designing and implementing scalable data governance frameworks on cloud data platforms. It explores how to establish data quality, security, privacy, and compliance policies across vast datasets, ensuring data reliability and trustworthiness for large-scale analytics and AI initiatives.

Peer-Reviewed Journal Article: "Architecting Unified Data Platforms: Data Lakes, Warehouses, and Lakehouses for AI." Published in the International Journal of Data Management & Analytics, this article presents groundbreaking research on mastering the design of modern data architectures. It specializes in building and managing data lakes, data warehouses, and lakehouse architectures to support large-scale analytics and AI, showcasing novel approaches for data modeling and governance across unified data platforms.

Book: "Unified Data Architectures: Building Lakes, Warehouses, and Lakehouses." This book provides advanced insights into mastering the design of modern data architectures. It specializes in building and managing data lakes, data warehouses, and lakehouse architectures to support large-scale analytics and AI.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the advanced technical challenges of big data architecture:

"Data Modeling for Data Lakehouse Architectures" (Technical Manual).

"Implementing Data Governance in Cloud Environments" (Research Paper).

"Optimizing Snowflake for Large-Scale Data Warehousing" (Practical Guide).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": Data Architecture Synthesizer. When a student designs a complex data architecture, I can instantly use the GAF engine to generate a high-fidelity, optimized data architecture blueprint. This includes simulating data storage and processing patterns, predicting query performance for analytical workloads, and highlighting potential governance gaps, ensuring a robust and scalable data ecosystem for large-scale analytics and AI.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": Cloud Data Platform Optimizer. When students are designing data solutions on cloud platforms like Snowflake or Databricks, I can instantly activate a GAF-powered "Cloud Data Platform Optimizer." This tool analyzes their proposed schema, query patterns, and resource utilization, identifying areas for cost reduction and performance improvement within the chosen cloud environment, ensuring efficient and economical data operations.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Nina Napitupulu

Mastering the design of modern data architectures, specializing in building and managing data lakes, data warehouses, and lakehouse architectures to support large-scale analytics and AI.

Meet your professor Open the classroom

AI Super Mentor

Dr. Filippo Leone

Advanced data engineering, data modeling, data governance, expertise in cloud data platforms (e.g., Snowflake, Databricks), leadership in data platform teams.

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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Big Data Architecture and Data Lake Management

Academic focus

Practical focus

Internships in technology companies or data consulting firms

Roles as data architects or data governance specialists

Consultancy in advanced big data architecture and data lake management

Support roles in academic research projects on big data architecture

Chief Data Officer (CDO) for large corporations or data consulting firms

Data Architect for large-scale data platforms

Data Lake Engineer for analytics platforms

Researcher in Big Data Architecture and Data Lake Management

Cultivating an interdisciplinary approach, integrating computer science, database management, and data analytics

Developing strategic thinking for big data solutions and data lake management

Enhancing problem-solving through the analysis of complex data management challenges

Critical thinking for a comprehensive and nuanced understanding of Big Data Architecture and Data Lake Management

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Nina Napitupulu

Dr. Filippo Leone

Find the program that expands your universe.