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Data Architecture Core Basics for Cloud Beginners in 60 mins

Last updated on April 18, 2024 9:18 am


What you’ll 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 fundamentals course provides a structured yet flexible learning experience, equipping you with the essential knowledge and skills to excel in these highly sought-after domains.

The course takes a breadth-first approach, introducing learners to the evolving landscape. It does not contain any deep dives with specific APIs! Data architecture has no silver bullets, so please don’t expect one from the course as well.

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.

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 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 HDFS to an AWS data lake?

  • What to buy and what to build when considering a data platform for an enterprise?

  • 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?

  • 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 more straightforward 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

Who this course is for:

  • Technical leaders adopting cloud in domain driven organizations
  • Executives seeking a big picture understanding of the cloud tranformation challenges of Data Science Adoption
  • Architectes and Solution Architects seeking pivots for explanining solutions to non technical audience
  • Infrastructure Engineers seeking a clear mapping between costs and fundamental infrastructure
  • Software professionals curious to explore the data landscape for career growth


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