Data Lake, Lakehouse, Data Warehouse Fundamentals in 60 mins

Data Lake, Lakehouse, Data Warehouse Fundamentals in 60 mins, available at $19.99, has an average rating of 4.55, with 29 lectures, 10 quizzes, based on 28 reviews, and has 7961 subscribers.

You will learn about 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. This course is ideal for individuals who are Technical leaders adopting cloud in domain driven organizations or Executives seeking a big picture understanding of the cloud tranformation challenges of Data Science Adoption or Architectes and Solution Architects seeking pivots for explanining solutions to non technical audience or Infrastructure Engineers seeking a clear mapping between costs and fundamental infrastructure or Software professionals curious to explore the data landscape for career growth It is particularly useful for Technical leaders adopting cloud in domain driven organizations or Executives seeking a big picture understanding of the cloud tranformation challenges of Data Science Adoption or Architectes and Solution Architects seeking pivots for explanining solutions to non technical audience or Infrastructure Engineers seeking a clear mapping between costs and fundamental infrastructure or Software professionals curious to explore the data landscape for career growth.

Enroll now: Data Lake, Lakehouse, Data Warehouse Fundamentals in 60 mins

Summary

Title: Data Lake, Lakehouse, Data Warehouse Fundamentals in 60 mins

Price: $19.99

Average Rating: 4.55

Number of Lectures: 29

Number of Quizzes: 10

Number of Published Lectures: 29

Number of Published Quizzes: 10

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 40

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: $19.99

Quality Status: approved

Status: Live

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.

Who Should Attend

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

Target Audiences

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

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Fundamentals to get started from scratch with the Data management ecosystems

Lecture 1: From Atoms to Cloud Computing

Lecture 2: Demystifying Databases: A precise functional guide for Decision-Makers

Lecture 3: Demystifying Structured, Semi-Structured, and Unstructured Data in Modern Cloud

Lecture 4: Navigating the Data Landscape: Understanding Data Preparation or ETL Methods

Lecture 5: Navigating the Analytics Landscape: From Descriptive to Prescriptive Analytics

Lecture 6: Navigating the Cloud Landscape: IaaS, PaaS, SaaS from ownership perspective

Chapter 3: Data Tools Landscape : Data Warehouse, Data Lake, Data LakeHouse

Lecture 1: Data Warehousing: Unveiling the Architecture and Fundamentals

Lecture 2: Data Lake vs. Data Warehouse: Complementary Roles of Data Storage and Analytics

Lecture 3: Data Lakehouses: Unified Data Management Architecture for Modern Computing

Chapter 4: Methods: Modern DataWarehouse, Data Fabric, Data Mesh

Lecture 1: Modern Data Warehouses: A Practical Guide to Cost-Effective Data Management

Lecture 2: Demystifying Data Fabric: Building a Unified Data Management Architecture

Lecture 3: Delving into the Data Mesh: A Guide to Decentralized Data Management

Chapter 5: Data Architecture considerations for Data Science

Lecture 1: Data Science on Data Warehouses: Navigating the Challenges and Optimal Usage

Lecture 2: Data Science on Data Lakes: Navigating the Challenges & Unlocking the Potential

Lecture 3: Data Lakehouse: Unveiling the Challenges and Possibilities for Data Science

Lecture 4: Data Fabric: Navigating Challenges of Unifying Diverse Sources for Data Science

Lecture 5: Overcoming the Challenges of Data Mesh Implementation for Data Science

Lecture 6: Mastering the Challenges of ML Ops: Ensuring Success of Machine Learning Project

Lecture 7: A Primer for Conquering the Challenges of Data Infrastructure for Data Science

Lecture 8: Confidential Computing: Top Considerations for Secure Data Processing

Lecture 9: Challenges of Real-time Analytics: Unleashing the Power of Data-driven Insights

Chapter 6: Unseen Challenges around Digital Transformation and cloud adoption

Lecture 1: Top 10 cloud mistakes to avoid

Lecture 2: Top 10 Hybrid Cloud considerations: Navigating the Complexities of Unified Infra

Lecture 3: Top 10 Hiring Challenges For Data Science Professionals

Lecture 4: Decoding Digital Transformation: Maslows Hierarchy of Needs for a Success

Lecture 5: Challenges of Outsourcing for Digital Transformation: Strategies for Success

Chapter 7: Applying the knowledge

Chapter 8: Conclusion

Lecture 1: Closing Remarks

Lecture 2: [Bonus Lecture] Reference Material with Links, Onboarding plan ideas and Notes

Instructors

RougeNeuron Academy By Subodh Chiwate
180,000+ Enrollments | Decoding Software Careers in AI era

Rating Distribution

1 stars: 1 votes

2 stars: 1 votes

3 stars: 4 votes

4 stars: 11 votes

5 stars: 11 votes

Frequently Asked Questions

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