Data Architecture 101 For Data Science In Ai Driven 2024

Data Lake, Data Lakehouse, Data Science, Data Warehouse, Data Fabric, Data Mesh, Data Architecture, Cloud Computing

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.

Requirements

Absolute basic understanding of comptuing expected like memory, CPU, network as black boxes.

No programming experience needed.

Description

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 ArchitectureDelve 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 mindsetLearn 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 ManagementAs 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 TalentIn 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 TransformationWhile 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 solutionsUnderstand the various permutations of infrastructure tools being presented for cloud offerings and services.A fundamentals driven framework to tackle the constantly changing cloud ecosystem.Who should take this course:Technical leaders shaping the digital transformation for domain-driven enterpriseArchitects 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 practicesData scientists seeking to enhance their skills and stay up-to-date with the latest advancements in architectureIT professionals involved in data management, data governance, and cloud computingBusiness professionals seeking to understand the impact of data architecture and data science on their organizations

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Fundamentals to get started

Lecture 2 From Atoms to Cloud Computing

Lecture 3 Demystifying Databases: A precise functional guide for Decision-Makers

Lecture 4 Demystifying Structured, Semi-Structured, and Unstructured Data in Modern Cloud

Lecture 5 Navigating the Data Landscape: Understanding Data Preparation or ETL Methods

Lecture 6 Navigating the Analytics Landscape: From Descriptive to Prescriptive Analytics

Lecture 7 Navigating the Cloud Landscape: IaaS, PaaS, SaaS from ownership perspective

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

Lecture 8 Data Warehousing: Unveiling the Architecture and Fundamentals

Lecture 9 Data Lake vs. Data Warehouse: Complementary Roles of Data Storage and Analytics

Lecture 10 Data Lakehouses: Unified Data Management Architecture for Modern Computing

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

Lecture 11 Modern Data Warehouses: A Practical Guide to Cost-Effective Data Management

Lecture 12 Demystifying Data Fabric: Building a Unified Data Management Architecture

Lecture 13 Delving into the Data Mesh: A Guide to Decentralized Data Management

Section 5: Data Architecture considerations for Data Science

Lecture 14 Data Science on Data Warehouses: Navigating the Challenges and Optimal Usage

Lecture 15 Data Science on Data Lakes: Navigating the Challenges & Unlocking the Potential

Lecture 16 Data Lakehouse: Unveiling the Challenges and Possibilities for Data Science

Lecture 17 Data Fabric: Navigating Challenges of Unifying Diverse Sources for Data Science

Lecture 18 Overcoming the Challenges of Data Mesh Implementation for Data Science

Lecture 19 Mastering the Challenges of ML Ops: Ensuring Success of Machine Learning Project

Lecture 20 A Primer for Conquering the Challenges of Data Infrastructure for Data Science

Lecture 21 Confidential Computing: Top Considerations for Secure Data Processing

Lecture 22 Challenges of Real-time Analytics: Unleashing the Power of Data-driven Insights

Section 6: Unseen Challenges around Digital Transformation and cloud adoption

Lecture 23 Top 10 cloud mistakes to avoid

Lecture 24 Top 10 Hybrid Cloud considerations: Navigating the Complexities of Unified Infra

Lecture 25 Top 10 Hiring Challenges For Data Science Professionals

Lecture 26 Decoding Digital Transformation: Maslow's Hierarchy of Needs for a Success

Lecture 27 Challenges of Outsourcing for Digital Transformation: Strategies for Success

Section 7: Applying the knowledge

Section 8: Conclusion

Lecture 28 Closing Remarks

Lecture 29 [Bonus Lecture] Reference Material

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