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Understanding Modern Data Architecture

Discover modern data architecture's key components, from ingestion and serving to governance and orchestration.

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2 Hours16 Videos56 Exercises
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Course Description

Introduction to modern data architectures


The world is constantly changing thanks to data, and every day the amount of data to be processed is even bigger. Thus, this course gives you an overview of modern data architectures, guiding you through the fundamental components of data solutions.

Components of modern data architectures


This is an introductory course to modern data solutions. During the first half of this course you'll learn what modern data architectures are, will review some popular solutions like Data Mesh and Data Fabric, and will explore the different elements of data architectures such as storage, data ingestion methods, processing options, and serving techniques.

Transversal Components of modern data architectures


In the second half, the course shows the transversal components that ensure the cohesion and enable the management of data architectures; explaining concepts like data governance, orchestration, observability, and even cost optimization within cloud providers. With all these elements, you'll be able to design data solutions that highly align with your organization's needs.

Designing and evaluating modern data architectures


The final part of the course integrates the knowledge you've gained, using a business case to design a data solution that satisfies the requirements of the business.
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  1. 1

    Introduction to Modern Data Architecture

    Free

    After seeing this chapter, you will be able to explain what modern data architecture is, and what are their key requirements and components. You'll also review different types of existing data architectures, ranging from Lambda to Data Mesh.

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    Modern Data Architectures
    50 xp
    What is a modern data architecture?
    50 xp
    Modern vs traditional architectures
    100 xp
    Examples of the modern data architecture
    50 xp
    Lambda architecture
    50 xp
    Lambda architecture layers
    100 xp
    Kappa architecture
    50 xp
    Kappa: What about the batch layer?
    50 xp
    Data Mesh and Data Fabric
    50 xp
    Data Mesh
    50 xp
    Data Fabric
    50 xp
    Data architecture & its use cases
    100 xp
  2. 2

    Modern Data Architecture Components

    Learn about the different components and layers of modern data architectures; from data ingestion to serving. Get familiar with the different paradigms and approaches in each layer, and some of the cloud services used to implement modern data solutions to get field experience!

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  3. 3

    Transversal Components of Data Architectures

    Dive into the transversal components that modern data architectures consist of, covering key aspects like data governance, metadata, data security, and observability. By understanding these vital elements, you'll be prepared to design robust, secure, and efficient data platforms while maintaining high standards for the data and overall service.

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  4. 4

    Putting it All Together

    Learn how to manage workflows, understand costs in cloud services, and create modern data designs. This chapter helps you work with tools like Apache Airflow and teaches you ways to save money on cloud use. By the end, you'll know how to build and judge strong data systems for businesses.

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Collaborators

Collaborator's avatar
George Boorman
Collaborator's avatar
Arne Warnke

Prerequisites

Introduction to Data WarehousingUnderstanding Data Engineering
Miller Andrés Trujillo Achury HeadshotMiller Andrés Trujillo Achury

Senior Software Engineer at Microsoft

Miller is a Senior Software Engineer at Microsoft, with a previous role at Google, that allowed him to sharpen his skills in data engineering, employing tools like Python, Java, and SQL, and cloud providers like AWS, Azure, and GCP. Miller's work approach is rooted in collaboration and constant learning. His work includes creating various data solutions for diverse clients including healthcare ones. Those solutions range from simple pipelines to libraries for data validation or platform tools for other data engineers, reflecting his commitment to practical, user-focused solutions.
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