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Data Architect

Nanodegree Program

The Data Architect Nanodegree program will be to propel learners to an advanced data professional role as a Data Architect.

The Data Architect Nanodegree program will be to propel learners to an advanced data professional role as a Data Architect.

Advanced

4 months

Real-world Projects

Completion Certificate

Last Updated May 6, 2024

Skills you'll learn:
Data architecture fluency • Master data management • Big data fluency • Data quality assessment
Prerequisites:
Basic computer programming • Relational database basics • Basic SQL

Courses In This Program

Course 1 1 hour

Welcome to the Data Architect Nanodegree Program

Lesson 1

Data Architect Nanodegree Program Introduction

Lesson 2

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Course 2 4 weeks

Data Architecture Foundations

In this course, you will cover the foundations of data architecture.

Lesson 1

Welcome to Data Architecture Foundations

This is the welcome lesson of the course.

Lesson 2

What is Data Architecture

This lesson will cover basic data architecture characteristics, data governance and business requirements.

Lesson 3

Database Framework

This lesson will cover model, schema and normalization

Lesson 4

Relational Data Design

This lesson will cover conceptual, logical and physical ERD, and how to create ERDs

Lesson 5

Creating a Physical Database Schema

This lesson will cover how to create and populate a database and run CRUD command

Lesson 6 • Project

Project: Designing an HR Database

Architect a database based on the business requirements for a Human Resources Department, combining all the skills you've learned so far!

Course 3 4 weeks

Designing Data Systems

In this course, students will learn about the major components in architecting an enterprise data system by actually designing a staging area, operational data store, and data warehouse. Students will learn the importance of creating business requirements and data artifacts that define the data in a "Single Version of Truth." From ingesting data into a staging database, moving the data to the operational data store where data is transformed, to ultimately accessing the data warehouse. Students will learn how the data warehouse helps eliminate data silos and bring data from across the enterprise into one central system, which can be governed and accessible by anyone in the enterprise to understand and analyze intelligent insights to address business challenges and opportunities.

Lesson 1

Foundations of Designing Data Systems

In this lesson, we will take a 30000-foot view of Designing Data Systems. We will meet the instructor and hear about the components of the course, including the final project.

Lesson 2

Data Architecture

In this lesson, we will look at the importance of, benefits of, and artifacts of Designing Data Systems using Data Architecture. We will also introduce Snowflake, the software we will be using.

Lesson 3

Staging Data

In this lesson, we will take a look at the important step of Staging Data. We will look at the theory behind it, study the steps involved, and then actually do it in Snowflake.

Lesson 4

Operational Data Store

In this lesson, we will look at what an ODS is and how it is an important step in building a data warehouse. We will also take a look at how to manage, cleanse, and transform the data.

Lesson 5

Data Warehouse

In this lesson, we will take a look at the final step of building a data warehouse. We will also look at reporting, the primary reason for a data warehouse.

Lesson 6 • Project

Project: Design a Data Warehouse for Reporting and OLAP

In a real data warehouse, students will use actual YELP and climate datasets in order to analyze the effects the weather has on customer reviews of restaurants.

Course 4 4 weeks

Big Data Systems

There are 2 main roles in the Big Data industry: Big Data Engineer and Big Data Architect. We will focus on the Architect role. We will look at the characteristics of Big Data, its business value, and how companies are using Big Data today. We study the most popular storage and processing frameworks.....aka...Big Data ecosystem components. We will dive deep into NoSQL, how it differs from traditional relational databases, how to model, and what the tool interface looks like. Finally, we will talk about the benefits, challenges, design patterns of Data Lake technology. Your final project is based on a real-world scenario that will require you to think like an architect and build an end-to-end data lake system proposal.

Lesson 1

Introduction to Big Data Systems

In this lesson, we will take a 30000 foot view of Big Data and see why it is so important. We will meet the instructor and hear about the components of the course, including the final project.

Lesson 2

Characteristics of Big Data

In this lesson you will learn about the main characteristics of Big Data, called the 4Vs. You will also start to explore the Big Data ecosystem.

Lesson 3

Ingestion, Storage and Processing Frameworks

In this lesson, you'll take a look at several of the layers that make Big Data possible, We will also look at some of the tools that help implement those layers.

Lesson 4

NoSQL Databases

In this lesson, we will look at the differences between NoSQL and SQL. We will also see why and how NoSQL databases provide capabilities that allow Big Data to be possible.

Lesson 5

Scalable Data Lake Architecture

In this lesson, we will see what a Data Lake storage implementation of Big Data looks like. In addition to the benefits, we will see what considerations, risks, and challenges organizations face.

Lesson 6 • Project

Project - Designing an Enterprise Data Lake System

In this lesson, we will lead you through the scenario and instructions for completing the final project, which is a proposal for an actual Data Lake architecture.

Taught By The Best

Photo of Nicholas DeGiacomo

Nicholas DeGiacomo

Data Scientist

Nick has built and managed teams of scientists and engineers for political campaigns, social media, and supply chain companies. With experiences ranging from startups to Amazon, he balances speed and scale. In his free time, Nick enjoys teaching graduate statistics courses at both Columbia and Yeshiva Universities.

Photo of Shrinath Parikh

Shrinath Parikh

Senior Data Architect

Shrinath is an entrepreneur and Data Architect passionate about helping enterprise companies transform and engineer their big data analytics applications on Cloud. He has worked with AWS, Google and Microsoft cloud platforms, has over 15 certifications and an MS in Computer Science from The University Of Texas at Dallas.

Photo of Rostislav Rabotnik

Rostislav Rabotnik

Principal Data Architect

Rostislav is an Enterprise Data Architect and Data Management Leader whose expertise covers data governance, architecture, and integration practices across a diverse range of technologies. He has worked at companies of all sizes and in various industries. His musings can be found at learndataarchitecture.com

Photo of Vijaya Nelavelli

Vijaya Nelavelli

Founder & Principal Data Architect

Vijaya is the Founder and Principal Data Architect for Great View Data Corp., which provides Data Architecture consulting and implementation services. Vijaya has extensive experience with creating architecture strategy and roadmaps, establishing frameworks and best practices, and Data Management.

Photo of Shankar Korrapolu

Shankar Korrapolu

CEO at OK2

Shankar Korrapolu is the co-founder and CEO of startup OK2, a cross-platform mobile gaming engine that builds games cheaper and faster without compromising quality. For over 30 years he has offered his data processing services to organizations in investment banking, pharma, government and education sectors.

Photo of Ben Larson

Ben Larson

Data Architect / Analytics Consultant

Benjamin has over 15 years of experience working as a data professional in fields including medicine, telecomm, and finance, in roles ranging from data architect to data scientist and analytics consultant. He holds a Ph.D. in Decision Sciences, where his research was focused on rare event detection.

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