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Data Analytics and Databases on AWS
Rafael Lopes
and
Oksana Hoeckele
Rafael Lopes
and
Oksana Hoeckele
Data is everywhere. If you or your company don't know what data you have and what insights you can uncover through your data, you are at a competitive disadvantage. In this course, you'll get introduced to data analytics and the upside of data-driven decisions. You'll learn about the omnipresence of data in today's world and what it takes to start thinking and acting like a data analyst. Week 1 concludes by comparing and contrasting ETL (Extract, Transform, Load) and ELT(Extract, Load, Transform) and where data is transformed and how data warehouses retain data. Week 2 kicks off with an overview of data workflow and database foundations. The four vs (volume, velocity, variety and veracity) of data are explained along with walk-throughs of collecting, processing, and storing data. In the course's final week, you'll get briefed on some of the AWS services that can be leveraged for ETL. You'll extract data with Amazon API Gateway, process data with AWS Lambda, load data with Amazon RDS, and visualize data with Amazon QuickSight. There's the right tool for each unique data analysis task.
Register for this course and see more details by visiting:
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What's inside
Syllabus
Module 1: Foundations of Data Analysis
Welcome to the first module of the course. This module introduces fundamental concepts in data analysis. You begin the module with how to assess use cases for data analysis in the cloud. Then, you explore some of the main data types and structures, and learn how metadata can help you manage datasets. Lastly, you 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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Syllabus
Module 1: Foundations of Data Analysis
Welcome to the first module of the course. This module introduces fundamental concepts in data analysis. You begin the module with how to assess use cases for data analysis in the cloud. Then, you explore some of the main data types and structures, and learn how metadata can help you manage datasets. Lastly, you complete the module by contrasting two data-processing approaches for analytics: extract, transform, and load (ETL) and extract, load, and transform (ELT).
Read more
Module 2: ETL Pipeline and Database Foundations
In this module, you start learning about the ETL pipeline, with an emphasis on the real-world scenario. Through each step, you learn how to gather data, ensure data quality, locate the appropriate storage or database, and evaluate insights. After you examine the ETL process, you assess SQL and NoSQL databases, and interact with a hands-on activity to practice your skills.
Module 3: AWS Services for ETL
In this module, you 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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops data analysis skills, which is an essential skill in many fields
Teaches data analysis methods, which can help learners make better data-driven decisions
Taught by recognized experts in data analytics
Examines the four vs of data: volume, velocity, variety, and veracity
Contrasts ETL and ELT approaches to data management
Offers hands-on labs and interactive materials
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Activities
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Data Analytics and Databases on AWS.
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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
Data Analyst
Data Analysts extract meaningful data, identify patterns, and provide recommendations based on data and analytics. This course is an excellent foundation for those seeking to launch a career as a Data Analyst, as it introduces foundational concepts in data analysis, including data types, data structures, metadata, ETL and ELT approaches, data workflow, and database foundations. The course's emphasis on real-world scenarios and hands-on activities will help you build practical experience in data analysis.
Data Scientist
Data Scientist
Data Scientists are responsible for developing and building data analysis models, and applying those models to address real-world problems. This course may be useful for those who want to explore a career as a Data Scientist, as it introduces fundamental concepts in data analysis, including ETL and ELT approaches, data workflow, and database foundations. The course's emphasis on real-world scenarios and hands-on activities may help you build a foundation in data analysis, which is a critical skill for Data Scientists.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining an organization's database systems. This course may be useful for those who want to explore a career as a Database Administrator, as it introduces foundational concepts in data analysis, including data workflow, database foundations, and data storage. The course's emphasis on real-world scenarios and hands-on activities may help you build a foundation in data analysis and database management, which are critical skills for Database Administrators.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and implement machine learning models to solve real-world problems. This course may be useful for those who want to explore a career as a Machine Learning Engineer, as it introduces foundational concepts in data analysis, including data workflow, data storage, and data visualization. The course's emphasis on real-world scenarios and hands-on activities may help you build a foundation in data analysis and machine learning, which are critical skills for Machine Learning Engineers.
Software Architect
Software Architect
Software Architects design and develop software systems that meet the needs of an organization. This course may be useful for those who want to explore a career as a Software Architect, as it introduces foundational concepts in data analysis, including data workflow, database foundations, and data storage. The course's emphasis on real-world scenarios and hands-on activities may help you build a foundation in data analysis and software design, which are critical skills for Software Architects.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. This course may be useful for those who want to explore a career as a Data Engineer, as it introduces foundational concepts in data analysis, including data workflow, data storage, and data visualization. The course's emphasis on real-world scenarios and hands-on activities may help you build a foundation in data analysis and data engineering, which are critical skills for Data Engineers.
Business Analyst
Business Analyst
Business Analysts identify and analyze business needs, and develop solutions to improve business processes. This course may be useful for those who want to explore a career as a Business Analyst, as it introduces foundational concepts in data analysis, including data workflow, data storage, and data visualization. The course's emphasis on real-world scenarios and hands-on activities may help you build a foundation in data analysis and business process improvement, which are critical skills for Business Analysts.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course may be useful for those who want to explore a career as a Software Engineer, as it introduces foundational concepts in data analysis, including data workflow, data storage, and data visualization. The course's emphasis on real-world scenarios and hands-on activities may help you build a foundation in data analysis and software development, which are critical skills for Software Engineers.
Cloud Architect
Cloud Architect
Cloud Architects design and deploy cloud-based solutions to meet the needs of an organization. This course may be useful for those who want to explore a career as a Cloud Architect, as it introduces foundational concepts in data analysis, including data workflow, data storage, and data visualization. The course's emphasis on real-world scenarios and hands-on activities may help you build a foundation in data analysis and cloud computing, which are critical skills for Cloud Architects.
For more career information including salaries, visit:
OpenCourser.com/course/97h4k2/data
Reading list
We've selected ten 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.
This in-depth guide to Apache Spark covers its core concepts, programming model, and advanced applications. It explores Spark's capabilities for data processing, machine learning, and stream processing, providing a comprehensive understanding of this powerful big data analytics framework.
Offers a comprehensive introduction to data analytics, covering various techniques, tools, and best practices. It's a valuable resource for beginners and those seeking a refresher in data analytics concepts.
This comprehensive guide to Hadoop offers insights into its architecture, ecosystem, and applications. It covers Hadoop Distributed File System (HDFS), MapReduce, and other Hadoop components, providing a technical deep dive into the world of big data processing.
Provides a comprehensive overview of database systems, with a focus on theoretical foundations.
Delves into data analysis using Python, covering data manipulation, visualization, statistical modeling, and machine learning. It's a practical guide for individuals seeking to enhance their data analysis skills in Python.
Introduces the fundamental concepts and techniques of data warehousing. It covers data modeling, data integration, and data analysis, providing a practical understanding of how data warehouses support decision-making and business intelligence.
Save
Provides a comprehensive overview of cloud computing concepts, technologies, and architectures. It covers cloud service models, deployment models, and cloud security, offering a foundational understanding of the cloud computing paradigm and its implications.
Provides a comprehensive overview of NoSQL databases, including their advantages and disadvantages.
Provides an overview of data warehouse concepts and design principles.
Save
Provides a comprehensive overview of data visualization techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/97h4k2/data
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Effort
2-4 hours per week
Level
Introductory
Via
Coursera
Institution
Amazon Web Services
Instructors
Rafael Lopes
Oksana Hoeckele
Language
English
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This course belongs to a
collection
called
AWS Cloud Technology Consultant. It's comprised of eight courses:
- Introduction to Information Technology and AWS Cloud
- Developing Applications in Python on AWS
- Capstone: Following the AWS Well Architected Framework
- Data Analytics and Databases on AWS (viewing)
- Providing Technical Support for AWS Workloads
- DevOps on AWS and Project Management
- Automation in the AWS Cloud
- Skills for Working as an AWS Cloud Consultant
Good to know
Develops data analysis skills, which is an essential skill in many fields
Teaches data analysis methods, which can help learners make better data-driven decisions
Taught by recognized experts in data analytics
Examines the four vs of data: volume, velocity, variety, and veracity
Contrasts ETL and ELT approaches to data management
Offers hands-on labs and interactive materials
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