• Post category:StudyBullet-17
  • Reading time:9 mins read

Data Architecture 101 for Data Science in AI driven 2024
Data Lake, Data Lakehouse, Data Warehouse, Data Fabric, Data Mesh, Data Architecture, Cloud Computing, Data Science

What you will learn

Fundamentals about Data Lake, Data Lakehouse, Data Warehouse and consideration when using them in Data Science Solutions

Basics about Data Fabric and Data Mesh and mapping them to Data Science use case

General Challenges in building data science solutions using infrastructure products.

Absolute fundamentals of computer science mapped to infrastructure products to understand cloud computing costs.

Jargon and buzz words free precise mapping of fundamentals to data technology products.

Course does NOT provide any step by step API based tutorials for any product or tool.


In today’s data-driven world, data architecture and data science have emerged as transformative forces, empowering organizations to harness the power of information for unparalleled insights, innovation, and competitive advantage. This comprehensive Udemy course provides a structured yet flexible learning experience, equipping you with the essential knowledge and skills to excel in these highly sought-after domains.

Unravel the Fundamentals of Data Architecture

Delve into the intricacies of data architecture, the cornerstone of effective data management and utilization. Gain a functional understanding of data tools like data lake, and data lakehouse, and methods like data fabric, and data mesh, enabling you to design and implement robust data architectures that align with organizational goals.

Cost Optimization mindset

Learn to map everything to absolute fundamentals to keep a check on infrastructure costs. Understand the value of choosing optimal solutions from the long-term perspective. Master the art of questioning the new products from a value creation perspective instead of doing a resume-driven development.

Navigate the Complexities of Hybrid Cloud Management

As organizations embrace hybrid cloud environments, managing the diverse landscapes of cloud and on-premises infrastructure becomes increasingly complex. This course equips you with the basic strategies and ideas to navigate these complexities effectively.

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Address the Challenges of Hiring and Retaining Data Science Talent

In the face of a global shortage of skilled data science professionals, attracting and retaining top talent is a critical challenge for organizations. This course delves into data science talent acquisition dynamics, providing practical strategies to identify, attract, and nurture top talent. Learn to create a data-driven culture that values continuous learning and innovation, fostering an environment where data scientists thrive and contribute to organizational success.

Overcome the Pitfalls of Outsourcing for Digital Transformation

While outsourcing can be a valuable tool for digital transformation initiatives, it also presents unique challenges. This course equips you with the knowledge and strategies to navigate these challenges effectively.

Key takeaways:

  • Master the fundamentals of data architecture necessary to build a robust solution for any use case including data science.
  • Learn the need for strategies for hybrid cloud management, optimizing network performance, implementing unified security policies, and leveraging cloud-based backup and disaster recovery solutions
  • Understand the various permutations of infrastructure tools being presented for cloud offerings and services.
  • A fundamentals-driven framework to tackle the constantly changing cloud ecosystem.

Questions Fundamentals-driven framework can answer better:

  • What will be the complexity involved in moving from a Snowflake data warehouse to a Databricks data lakehouse?
  • How will the cloud costs increase over the next 5 years if moving from an on-premise HDFSΒ to an AWSΒ data lake?
  • What to buy and what to build when considering a data platform for an enterprise?
  • How to build a data architecture for a data science solution for a complex use case like clinical data management, energy data management, or engineering data management using enterprise data services?
  • Is cloud-based data storage always cheap or does it introduce additional cost centers?
  • What is the difference between data fabric and data mesh?
  • When is the data management platform ready for prescriptive analytics?
  • Is there a way to simulate Azure Synapse or Google DataFlow using on-premise infrastructure?
  • Why is cost calculation for the cloud complex?
  • Does Kubernetes solve all problems around infrastructure management?
  • Why knowing only Python is not enough for building data science solutions?
  • What is cloud storage and why it is crucial in modern solutions?

Who should take this course:

  • Technical leaders shaping the digital transformation for domain-driven enterprise
  • Architects and solution architects seek a simpler vocabulary to communicate with nontechnical leaders.
  • Aspiring data architects seeking to establish a strong foundation in data architecture principles and practices
  • Data scientists seeking to enhance their skills and stay up-to-date with the latest advancements in architecture
  • IT professionals involved in data management, data governance, and cloud computing
  • Business professionals seeking to understand the impact of data architecture and data science on their organizations




Fundamentals to get started

From Atoms to Cloud Computing
Demystifying Databases: A precise functional guide for Decision-Makers
Demystifying Structured, Semi-Structured, and Unstructured Data in Modern Cloud
Fundamentals Quiz – 1
Navigating the Data Landscape: Understanding Data Preparation or ETL Methods
Navigating the Analytics Landscape: From Descriptive to Prescriptive Analytics
Navigating the Cloud Landscape: IaaS, PaaS, SaaS from ownership perspective
Fundamentals Quiz – 2

Data Tools Landscape : Data Warehouse, Data Lake, Data LakeHouse

Data Warehousing: Unveiling the Architecture and Fundamentals
Data Lake vs. Data Warehouse: Complementary Roles of Data Storage and Analytics
Data Lakehouses: Unified Data Management Architecture for Modern Computing
Data Products Quiz

Methods: Modern DataWarehouse, Data Fabric, Data Mesh

Modern Data Warehouses: A Practical Guide to Cost-Effective Data Management
Demystifying Data Fabric: Building a Unified Data Management Architecture
Delving into the Data Mesh: A Guide to Decentralized Data Management
Architecture Philosophy Quiz

Data Architecture considerations for Data Science

Data Science on Data Warehouses: Navigating the Challenges and Optimal Usage
Data Science on Data Lakes: Navigating the Challenges & Unlocking the Potential
Data Lakehouse: Unveiling the Challenges and Possibilities for Data Science
Data Science and Data Products Quiz
Data Fabric: Navigating Challenges of Unifying Diverse Sources for Data Science
Overcoming the Challenges of Data Mesh Implementation for Data Science
Data Science and Data Methods Quiz
Mastering the Challenges of ML Ops: Ensuring Success of Machine Learning Project
A Primer for Conquering the Challenges of Data Infrastructure for Data Science
Confidential Computing: Top Considerations for Secure Data Processing
Challenges of Real-time Analytics: Unleashing the Power of Data-driven Insights
Data Science Production Quiz

Unseen Challenges around Digital Transformation and cloud adoption

Top 10 cloud mistakes to avoid
Top 10 Hybrid Cloud considerations: Navigating the Complexities of Unified Infra
Cloud Challenges Quiz
Top 10 Hiring Challenges For Data Science Professionals
Decoding Digital Transformation: Maslow’s Hierarchy of Needs for a Success
Challenges of Outsourcing for Digital Transformation: Strategies for Success
Transformation Challenges Quiz

Applying the knowledge

Tracing information and discovering the reality behind the jargon


Closing Remarks
[Bonus Lecture] Reference Material with Links, Onboarding plan ideas and Notes