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Data Analytics and Databases on AWS

Oksana Hoeckele and Rafael Lopes

This course starts by introducing fundamental concepts in data analysis. You begin with how to assess use cases for data analysis in the cloud. After exploring some of the main data types and structures, you’ll complete the module by contrasting two data-processing approaches for analytics: extract, transform, and load (ETL) and extract, load, and transform (ELT).

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This course starts by introducing fundamental concepts in data analysis. You begin with how to assess use cases for data analysis in the cloud. After exploring some of the main data types and structures, you’ll complete the module by contrasting two data-processing approaches for analytics: extract, transform, and load (ETL) and extract, load, and transform (ELT).

You’ll then transition to start learning about the ETL pipeline, review AWS services for data analysis, and reinforce your learning through practical labs. These services include Amazon API Gateway, Amazon Relational Database Service (Amazon RDS), Amazon DynamoDB, and Amazon QuickSight. You review these services in the AWS Management Console, and evaluate how you can use each service in the ETL process. Then, you gain practical experience by working with some of these service in a preconfigured environment.

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What's inside

Learning objectives

  • Key data types and structures
  • Foundations of sql and nosql databases
  • Common sql queries
  • Etl steps for data processing
  • Aws services for the etl process
  • Hands-on skills for amazon api gateway, amazon relational database service (amazon rds), and amazon quicksight

Syllabus

Week 1: Foundations of data analysis
Welcome to the courseData-driven decisions are better decisionsETL and ELT basicsWrapping up Module 1
Week 2: ETL and database foundations
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Provides hands-on experience with Amazon API Gateway, Amazon RDS, and Amazon QuickSight, which are essential tools for building data analytics solutions on AWS
Explores ETL and ELT approaches, which are fundamental concepts for designing and implementing data pipelines in cloud environments
Covers SQL and NoSQL databases, which are core technologies for storing and managing data in modern data analytics architectures
Presented by Amazon Web Services, a leading provider of cloud computing services and a major player in the data analytics space
Requires familiarity with AWS services, so learners without prior AWS experience may need to acquire foundational knowledge beforehand

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Reviews summary

Aws data analytics and database fundamentals

According to learners, this course provides a solid foundation for understanding data analytics and databases within the AWS ecosystem. Students particularly praise the hands-on labs, which they found crucial for applying concepts and gaining practical experience with services like Amazon RDS and Amazon QuickSight. The explanations of ETL and ELT processes are also highlighted as being very clear. While it offers a good breadth of coverage across relevant AWS services, some reviewers note that the depth of coverage for individual services is limited, making it an excellent starting point but potentially requiring further study for advanced topics. Overall, it's seen as a valuable introduction for professionals new to data on AWS.
Suitable pace for newcomers.
"The pace was just right for someone new to AWS data services and cloud concepts."
"Doesn't assume extensive prior knowledge, which makes it accessible for those transitioning into data roles."
"I found it accessible even without a deep tech background before starting the course."
"It's a good starting point if you're unfamiliar with cloud data tools and need a gentle introduction."
Clear explanation of data processes.
"The sections on ETL and ELT were very clear and easy to follow, providing a good conceptual understanding."
"Helped me understand the practical steps of building a data pipeline from extraction to visualization."
"Liked how they contrasted ETL and ELT approaches and discussed use cases for each."
"Gave me a practical understanding of the data processing flow within the AWS context."
Excellent starting point for AWS data.
"This course provides a great foundation for understanding how to approach data analytics on AWS."
"Perfect for beginners looking to get started with AWS data services and their basic functionalities."
"It gives you a clear overview of the basic building blocks needed for data processing in the cloud."
"Helped me grasp the fundamentals of data analytics in the cloud without being overwhelming."
Hands-on exercises are crucial.
"The hands-on labs were incredibly helpful for solidifying concepts and seeing how things work in the real AWS environment."
"I appreciated the opportunity to work directly with the AWS Management Console through the practical exercises."
"Building the ETL pipeline myself in the labs made a huge difference in my understanding."
"The practical exercises are the course's biggest strength and are essential for learning these services."
Good breadth, but limited depth.
"While it covers many services, the depth on each is quite limited; it's more of an introductory survey."
"Felt like it just scratched the surface on some topics, especially concerning optimization and advanced use cases."
"Could use more in-depth coverage, especially on specific service configurations or best practices for production."
"It's a good overview to know what services exist, but not enough for deep dives needed for implementation."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Analytics and Databases on AWS with these activities:
Review SQL Fundamentals
Reinforce your understanding of SQL syntax and database concepts before diving into AWS database services.
Browse courses on SQL
Show steps
  • Review basic SQL commands (SELECT, INSERT, UPDATE, DELETE).
  • Practice writing SQL queries on sample datasets.
  • Familiarize yourself with database normalization concepts.
Brush up on NoSQL Concepts
Solidify your understanding of NoSQL database concepts before learning about DynamoDB.
Browse courses on NoSQL
Show steps
  • Review different types of NoSQL databases (key-value, document, graph, column-family).
  • Understand the CAP theorem and its implications for NoSQL database design.
  • Explore use cases where NoSQL databases are preferred over SQL databases.
Review 'Data Science from Scratch'
Gain a deeper understanding of data science principles and Python programming for data analysis.
View Data Science from Scratch: First Principles... on Amazon
Show steps
  • Read the chapters on data visualization and statistical analysis.
  • Practice implementing data analysis techniques using Python.
  • Relate the concepts learned to the AWS services covered in the course.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow AWS QuickSight Tutorials
Enhance your skills in data visualization using Amazon QuickSight through hands-on tutorials.
Browse courses on QuickSight
Show steps
  • Explore the official AWS QuickSight documentation and tutorials.
  • Create sample dashboards and visualizations using QuickSight.
  • Experiment with different chart types and data sources.
Build a Simple ETL Pipeline on AWS
Apply your knowledge by building a basic ETL pipeline using AWS services like API Gateway, RDS, and QuickSight.
Browse courses on ETL
Show steps
  • Design a simple ETL pipeline for a specific use case.
  • Implement the pipeline using AWS services.
  • Test and refine the pipeline to ensure data accuracy and efficiency.
Document Your ETL Project
Solidify your understanding by documenting the ETL project you built, explaining the design choices and implementation details.
Browse courses on ETL
Show steps
  • Describe the architecture of your ETL pipeline.
  • Explain the purpose of each AWS service used in the pipeline.
  • Document the steps involved in transforming and loading the data.
Review 'Designing Data-Intensive Applications'
Deepen your understanding of data system design principles and best practices.
View Designing Data-Intensive Applications: The Big... on Amazon
Show steps
  • Read the chapters on data storage and data processing.
  • Understand the trade-offs involved in different data system designs.
  • Relate the concepts learned to the AWS services covered in the course.

Career center

Learners who complete Data Analytics and Databases on AWS will develop knowledge and skills that may be useful to these careers:
Data Analyst
A Data Analyst collects, processes, and performs statistical analysis of data. They often work with databases, query languages such as SQL, and data visualization tools to extract insights and trends. This course helps build a foundation in data analysis by introducing key data types and structures. It also covers ETL processes, and specifically focuses on how to use AWS services for data analysis, which are the same kinds of tools that a Data Analyst will work with every day. Those wishing to work as a Data Analyst should take this course to quickly learn the practical skills and tools necessary to quickly become proficient.
See salaries and explore the career path for Data Analyst
Database Administrator
A Database Administrator is responsible for the performance, security, and integrity of an organization's databases. They should be knowledgeable in database architecture and query languages such as SQL. This course introduces fundamental concepts in SQL and NoSQL databases and common SQL queries. A Database Administrator also needs to understand how data is extracted, transformed, and loaded, and this course provides a sound foundation in the extract, transform, and load process. It also covers AWS services for database management. A learner interested in database administration will find this course to be a good way to begin learning the necessary skills.
See salaries and explore the career path for Database Administrator
Business Intelligence Analyst
A Business Intelligence Analyst uses data to gather business insights and create reports that help guide the decision-making process in an organization. They frequently need to process data using extract, transform, and load pipelines and use data visualization tools. This course will be very helpful because it helps learners understand ETL processes and hands on skills with Amazon QuickSight, a key visualization tool for Business Intelligence Analysts. It also builds a foundation in data analysis as well as AWS services necessary for working with data and generating reports. Anyone interested in becoming a Business Intelligence Analyst will find this course beneficial.
See salaries and explore the career path for Business Intelligence Analyst
Cloud Data Engineer
A Cloud Data Engineer designs, builds, and maintains data infrastructure in the cloud. This often includes managing data pipelines, databases, and cloud-based data storage systems. This course explores the AWS services that Cloud Data Engineers use such as Amazon API Gateway, Amazon Relational Database Service, Amazon DynamoDB and Amazon QuickSight. It also provides hands-on experience in how to use these services in the extract, transform and load process. This course is a good starting point for someone looking into Cloud Data Engineering because of its focus on AWS tools and techniques.
See salaries and explore the career path for Cloud Data Engineer
ETL Developer
An ETL Developer is responsible for designing, building, and maintaining extract, transform, and load processes, which are essential for moving data from different sources to data warehouses or data lakes. This course provides a good foundation for an ETL Developer by exploring ETL steps for data processing as well as AWS services for the ETL process. It also provides an overview of different data processing approaches and provides a hands on learning experience. Anyone looking to become an ETL Developer will find this course to be beneficial.
See salaries and explore the career path for ETL Developer
Data Visualization Specialist
A Data Visualization Specialist focuses on presenting data in a clear and understandable format using charts, graphs, and dashboards. They frequently use tools like Amazon QuickSight, which is covered in this course. In addition, a Data Visualization Specialist needs to understand how to process data, which can be taught, in part, in this course. Those who are pursuing a path as a Data Visualization Specialist may find that this course will be useful to understand how data pipelines are managed.
See salaries and explore the career path for Data Visualization Specialist
Database Developer
A Database Developer is responsible for designing, developing, and maintaining databases. They write SQL queries, create database objects, and optimize performance. The course will be useful for a Database Developer because it provides a foundation in SQL and NoSQL databases, including common SQL queries. It also reviews services such as Amazon Relational Database Service which are relevant to this role. The course will help build a foundation for anyone who wants to become a Database Developer.
See salaries and explore the career path for Database Developer
Analytics Consultant
An Analytics Consultant works with clients to understand their data needs, design analytical solutions, and provide data-driven recommendations. This role requires a solid foundation in data analysis, processing, and visualization. This course introduces key data types and structures, and provides hands-on skills with Amazon QuickSight for data visualization. It also covers common SQL queries and ETL processes making it helpful for an Analytics Consultant who needs to understand the whole data pipeline. This course may be useful for those looking to become an Analytics Consultant.
See salaries and explore the career path for Analytics Consultant
Solutions Architect
A Solutions Architect designs and implements technology solutions for businesses, often working with cloud-based services. This may include setting up data analytics pipelines on AWS. This course is helpful because it provides an understanding of core services such as Amazon API Gateway and Amazon Relational Database Service used in data processing. A Solutions Architect should also understand data types and processing approaches covered in this course, and this may be useful for those looking to pursue this role.
See salaries and explore the career path for Solutions Architect
Report Developer
A Report Developer creates reports and dashboards to present data in a way that is useful for decision-making. This role requires an understanding of data analysis and data visualization tools. This course covers the ETL process, key data types, and provides hands on skill with Amazon QuickSight, which are all relevant to a Report Developer. It may be useful for a Report Developer to gain practical experience with AWS services in a pre-configured environment, as is offered in this course.
See salaries and explore the career path for Report Developer
Data Quality Analyst
A Data Quality Analyst is responsible for ensuring the accuracy, completeness, and reliability of data. They will often need to assess data processing approaches like extract, transform, and load pipelines. Data Quality Analysts may also need to work with databases and query languages. This course may be helpful for a Data Quality Analyst by introducing them to ETL concepts, database foundations, and common SQL queries. Those who wish to pursue a career in data quality may find that this course will provide some useful background.
See salaries and explore the career path for Data Quality Analyst
Cloud Support Engineer
A Cloud Support Engineer provides technical support for cloud-based services, often working with databases, data pipelines, and ETL processes, which are covered in this course. This course is helpful because it introduces AWS services for ETL and provides a foundation in data types and structures. It also provides practical experience working with these services. A Cloud Support Engineer may find they use this knowledge regularly so this course may be useful.
See salaries and explore the career path for Cloud Support Engineer
Market Research Analyst
A Market Research Analyst studies market conditions to examine potential sales of a product or service. They frequently work with data and databases to come to data driven conclusions. This course introduces how to assess use cases for data analysis in the cloud. The course also helps to build a foundation in key data types and structures, which may be helpful to a Market Research Analyst. This course may prove to be useful for a Market Research Analyst.
See salaries and explore the career path for Market Research Analyst
Financial Analyst
A Financial Analyst analyzes financial data, builds financial models, and provides insights for financial planning. Although this role is more focused on finance than data, a Financial Analyst still needs the ability to process and transform data. A Financial Analyst may find that this course helps them to understand data processing, including extract, transform and load pipelines. While not the main focus of this role, the knowledge gained from this course may be useful to a Financial Analyst.
See salaries and explore the career path for Financial Analyst
Operations Analyst
An Operations Analyst examines business processes and systems to improve efficiency and productivity. Though focused on operations, this role will often require a deeper understanding of data processing and analysis. This course may be useful for an Operations Analyst because they will get to review data processing approaches like extract, transform and load. Further, the course provides an overview of data types and structures which may be relevant for this role, so it may be helpful to an Operations Analyst.
See salaries and explore the career path for Operations Analyst

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Analytics and Databases on AWS.
Cover image
Designing Data-Intensive Applications
Save
Provides a comprehensive overview of the principles behind building scalable and reliable data systems. It covers topics such as data storage, data processing, and distributed systems. While not specific to AWS, it provides valuable context for understanding the design considerations behind AWS data services. This book is commonly used as a reference by industry professionals.
Designing Data-Intensive Applications: The Big...
Paperback
$$
Designing Data-Intensive Applications: The Big...
Kindle Edition
$$
Cover image
Data Science from Scratch
Save
Provides a solid foundation in data science principles using Python. It covers essential concepts like data manipulation, visualization, and basic statistical analysis. While not AWS-specific, it provides valuable background knowledge for understanding data analysis techniques used in the cloud. It is more valuable as additional reading to build a strong foundation.
Data Science from Scratch: First Principles with...
Paperback
$$
Data Science from Scratch: First Principles with...
Kindle Edition
$$
Data Science from Scratch: First Principles with...
Paperback
$$

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Effort
2 to 4 hours per week for 3 weeks
Level
Introductory
Via
edX
Institution
Amazon Web Services
Instructors
Oksana Hoeckele
Rafael Lopes
Session
November 4, 2024 - November 30, 2026
Language
English
Transcript
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Provides hands-on experience with Amazon API Gateway, Amazon RDS, and Amazon QuickSight, which are essential tools for building data analytics solutions on AWS
Explores ETL and ELT approaches, which are fundamental concepts for designing and implementing data pipelines in cloud environments
Covers SQL and NoSQL databases, which are core technologies for storing and managing data in modern data analytics architectures
Presented by Amazon Web Services, a leading provider of cloud computing services and a major player in the data analytics space
Requires familiarity with AWS services, so learners without prior AWS experience may need to acquire foundational knowledge beforehand
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