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Tyson Gern
and
Mike Barinek
The course is intended for individuals looking to understand the architecture patterns necessary to take large software systems that make use of big data to production.
You will transform big data prototypes into high quality tested production software. After measuring the performance characteristics of distributed systems, you will identify trouble areas and implement scalable solutions to improve performance. Upon completion of the course you will know how to scale production data stores to perform under load, designing load tests to ensure applications meet performance requirements.
Read more
The course is intended for individuals looking to understand the architecture patterns necessary to take large software systems that make use of big data to production.
You will transform big data prototypes into high quality tested production software. After measuring the performance characteristics of distributed systems, you will identify trouble areas and implement scalable solutions to improve performance. Upon completion of the course you will know how to scale production data stores to perform under load, designing load tests to ensure applications meet performance requirements.
Read more
The course is intended for individuals looking to understand the architecture patterns necessary to take large software systems that make use of big data to production.
You will transform big data prototypes into high quality tested production software. After measuring the performance characteristics of distributed systems, you will identify trouble areas and implement scalable solutions to improve performance. Upon completion of the course you will know how to scale production data stores to perform under load, designing load tests to ensure applications meet performance requirements.
This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:
MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder
MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
Register for this course and see more details by visiting:
OpenCourser.com/course/qgl8j5/software
What's inside
Syllabus
Predictive Models
Welcome to Software Architecture Patterns for Big Data. In this first week of the course, you will learn how to write tests that allow you to iterate on predictive models.
Read more
Syllabus
Predictive Models
Welcome to Software Architecture Patterns for Big Data. In this first week of the course, you will learn how to write tests that allow you to iterate on predictive models.
Read more
Performance of Distributed Systems
In this week, you will learn how to ensure your distributed system operates as expected in production by writing performance tests.
Horizontal Distribution of Large Workloads
This week you will use queues to horizontally distribute large workloads.
Highly Available Distributed Systems
In the last week of this course, you will learn the advantages and disadvantages of high availability distributed systems.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills in scaling production data stores to perform under load, preparing learners to design load tests ensuring applications meet performance requirements
Focuses on how to take large software systems using big data to production, equipping learners with relevant practical skills
Taught by Tyson Gern and Mike Barinek, providing learners with access to experienced professionals
Examines predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems
Explicitly requires learners to come in with extensive background knowledge
Belongs to a series of other courses, which can be a sign of detail and higher comprehension
Save this course
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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 Software Architecture Patterns for Big Data with these
activities:
Review foundational data science concepts
Show steps
Refresh your understanding of key data science concepts, including data types, data structures, and data analysis techniques, to strengthen your foundation for the course.
Browse courses on
Data Science Concepts
Show steps
-
Review textbooks or online resources on data science fundamentals
-
Complete practice exercises or quizzes on data science concepts
Review basic programming concepts, such as data structures and algorithms
Show steps
Refreshes your programming fundamentals, which are essential for understanding and implementing big data solutions.
Browse courses on
Programming Concepts
Show steps
-
Read through notes or textbooks to review key concepts.
-
Solve practice problems or coding challenges to reinforce your understanding.
Form study groups or participate in online forums to discuss course topics
Show steps
Fosters collaboration, enables you to share knowledge with others, and provides diverse perspectives on course concepts.
Show steps
-
Identify or create study groups with peers who have similar interests.
-
Schedule regular meetings to discuss course material, work on assignments together, and quiz each other.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow tutorials on Apache Spark, Hadoop, or other big data technologies
Show steps
Provides hands-on experience with big data tools, enhancing your understanding of their capabilities and limitations.
Browse courses on
Big Data Technologies
Show steps
-
Identify tutorials that cover key concepts and technologies related to the course.
-
Follow the tutorials step-by-step and implement the examples provided.
-
Experiment with the technologies to gain practical insights.
Read 'Designing Data-Intensive Applications' by Martin Kleppmann
Show steps
Provides a comprehensive overview of the architectural patterns used in designing and building data-intensive applications, aligning with the course's focus on software architecture for big data.
View
Secret Colors
on Amazon
Show steps
-
Read chapters 1-3 to understand the fundamentals of data-intensive applications.
-
Read chapters 4-6 to learn about data storage and processing patterns.
Solve coding challenges on platforms like HackerRank or LeetCode
Show steps
Enhances your coding skills, which are essential for implementing and testing big data solutions.
Browse courses on
Coding
Show steps
-
Choose coding problems related to big data concepts, such as data processing or distributed systems.
-
Attempt to solve the problems using appropriate algorithms and data structures.
-
Review your solutions and identify areas for improvement.
Build a simple data processing pipeline
Show steps
Create a small-scale data processing pipeline using tools and techniques covered in the course to gain practical experience in data manipulation and transformation.
Browse courses on
Data Processing
Show steps
-
Define the data sources and data types to be processed
-
Choose appropriate data processing tools and libraries
-
Develop code to implement the data processing pipeline
-
Test and evaluate the pipeline's performance
Design a distributed system for processing large datasets
Show steps
Allows you to apply the concepts learned in the course to a practical scenario, reinforcing your understanding of distributed system design for big data.
Browse courses on
Distributed Systems
Show steps
-
Define the requirements and constraints of the system.
-
Choose appropriate architectural patterns and technologies.
-
Design the system architecture, including data storage, processing, and communication.
Attend workshops on big data analytics or data engineering
Show steps
Offers opportunities to engage with experts and learn from industry best practices, broadening your perspective on big data applications.
Browse courses on
Big Data Analytics
Show steps
-
Research and identify workshops that align with your learning goals.
-
Attend the workshops and actively participate in discussions.
-
Follow up after the workshops to reinforce your learning.
Explore online tutorials on big data frameworks
Show steps
Deepen your understanding of big data frameworks and tools by following guided tutorials and online courses provided by reputable sources.
Show steps
-
Identify and select relevant tutorials on specific big data frameworks
-
Work through the tutorials and complete the exercises
-
Apply the learned concepts and techniques to your own projects
Design a scalable data storage solution
Show steps
Develop a design document for a scalable data storage solution that meets the requirements of a specific big data application, considering factors such as data volume, access patterns, and performance.
Browse courses on
Data Storage
Show steps
-
Analyze the data requirements and usage patterns of the application
-
Research and evaluate different data storage technologies
-
Design the data storage architecture and schema
-
Document the design and implementation details
Review foundational data science concepts
Show steps
Refresh your understanding of key data science concepts, including data types, data structures, and data analysis techniques, to strengthen your foundation for the course.
Browse courses on
Data Science Concepts
Show steps
Show steps
Show steps
- Review textbooks or online resources on data science fundamentals
- Complete practice exercises or quizzes on data science concepts
Review basic programming concepts, such as data structures and algorithms
Show steps
Refreshes your programming fundamentals, which are essential for understanding and implementing big data solutions.
Browse courses on
Programming Concepts
Show steps
Show steps
Show steps
- Read through notes or textbooks to review key concepts.
- Solve practice problems or coding challenges to reinforce your understanding.
Form study groups or participate in online forums to discuss course topics
Show steps
Fosters collaboration, enables you to share knowledge with others, and provides diverse perspectives on course concepts.
Show steps
Show steps
Show steps
- Identify or create study groups with peers who have similar interests.
- Schedule regular meetings to discuss course material, work on assignments together, and quiz each other.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow tutorials on Apache Spark, Hadoop, or other big data technologies
Show steps
Provides hands-on experience with big data tools, enhancing your understanding of their capabilities and limitations.
Browse courses on
Big Data Technologies
Show steps
Show steps
Show steps
- Identify tutorials that cover key concepts and technologies related to the course.
- Follow the tutorials step-by-step and implement the examples provided.
- Experiment with the technologies to gain practical insights.
Read 'Designing Data-Intensive Applications' by Martin Kleppmann
Show steps
Provides a comprehensive overview of the architectural patterns used in designing and building data-intensive applications, aligning with the course's focus on software architecture for big data.
View
Secret Colors
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 to understand the fundamentals of data-intensive applications.
- Read chapters 4-6 to learn about data storage and processing patterns.
Solve coding challenges on platforms like HackerRank or LeetCode
Show steps
Enhances your coding skills, which are essential for implementing and testing big data solutions.
Browse courses on
Coding
Show steps
Show steps
Show steps
- Choose coding problems related to big data concepts, such as data processing or distributed systems.
- Attempt to solve the problems using appropriate algorithms and data structures.
- Review your solutions and identify areas for improvement.
Build a simple data processing pipeline
Show steps
Create a small-scale data processing pipeline using tools and techniques covered in the course to gain practical experience in data manipulation and transformation.
Browse courses on
Data Processing
Show steps
Show steps
Show steps
- Define the data sources and data types to be processed
- Choose appropriate data processing tools and libraries
- Develop code to implement the data processing pipeline
- Test and evaluate the pipeline's performance
Design a distributed system for processing large datasets
Show steps
Allows you to apply the concepts learned in the course to a practical scenario, reinforcing your understanding of distributed system design for big data.
Browse courses on
Distributed Systems
Show steps
Show steps
Show steps
- Define the requirements and constraints of the system.
- Choose appropriate architectural patterns and technologies.
- Design the system architecture, including data storage, processing, and communication.
Attend workshops on big data analytics or data engineering
Show steps
Offers opportunities to engage with experts and learn from industry best practices, broadening your perspective on big data applications.
Browse courses on
Big Data Analytics
Show steps
Show steps
Show steps
- Research and identify workshops that align with your learning goals.
- Attend the workshops and actively participate in discussions.
- Follow up after the workshops to reinforce your learning.
Explore online tutorials on big data frameworks
Show steps
Deepen your understanding of big data frameworks and tools by following guided tutorials and online courses provided by reputable sources.
Show steps
Show steps
Show steps
- Identify and select relevant tutorials on specific big data frameworks
- Work through the tutorials and complete the exercises
- Apply the learned concepts and techniques to your own projects
Design a scalable data storage solution
Show steps
Develop a design document for a scalable data storage solution that meets the requirements of a specific big data application, considering factors such as data volume, access patterns, and performance.
Browse courses on
Data Storage
Show steps
Show steps
Show steps
- Analyze the data requirements and usage patterns of the application
- Research and evaluate different data storage technologies
- Design the data storage architecture and schema
- Document the design and implementation details
Career center
Learners who complete Software Architecture Patterns for Big Data will develop knowledge and skills
that may be useful to these careers:
Project Manager
Project Manager
A Project Manager is responsible for planning and managing software development projects. This course would be beneficial to a Project Manager because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Project Manager.
Data Scientist
Data Scientist
A Data Scientist is responsible for using data to solve business problems. This course would be beneficial to a Data Scientist because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for developing and deploying machine learning models. This course would be beneficial to a Machine Learning Engineer because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Machine Learning Engineer.
Business Analyst
Business Analyst
A Business Analyst is responsible for analyzing business processes and identifying opportunities for improvement. This course would be beneficial to a Business Analyst because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Business Analyst.
Data Architect
Data Architect
A Data Architect designs, builds, and maintains the infrastructure and data systems that support an organization's data needs. This course would be beneficial to a Data Architect because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Data Architect.
Database Administrator
Database Administrator
A Database Administrator is responsible for managing and maintaining the databases for an organization. This course would be beneficial to a Database Administrator because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Database Administrator.
Network Administrator
Network Administrator
A Network Administrator is responsible for managing and maintaining the network infrastructure for an organization. This course would be beneficial to a Network Administrator because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Network Administrator.
Product Manager
Product Manager
A Product Manager is responsible for defining and managing the product roadmap. This course would be beneficial to a Product Manager because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Product Manager.
Data Engineer
Data Engineer
A Data Engineer is responsible for designing, building, and maintaining the data pipelines that move data from source systems to target systems. This course would be beneficial to a Data Engineer because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Data Engineer.
Cloud Architect
Cloud Architect
A Cloud Architect is responsible for designing and building the cloud infrastructure for an organization. This course would be beneficial to a Cloud Architect because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Cloud Architect.
Big Data Analyst
Big Data Analyst
A Big Data Analyst is responsible for analyzing large datasets to identify trends and patterns. This course would be beneficial to a Big Data Analyst because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Big Data Analyst.
Systems Administrator
Systems Administrator
A Systems Administrator is responsible for managing and maintaining the computer systems for an organization. This course would be beneficial to a Systems Administrator because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Systems Administrator.
Security Analyst
Security Analyst
A Security Analyst is responsible for protecting the organization's computer systems from security breaches. This course would be beneficial to a Security Analyst because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Security Analyst.
Software Architect
Software Architect
A Software Architect is responsible for designing and building the software architecture for an organization. This course would be beneficial to a Software Architect because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Software Architect.
Quality Assurance Analyst
Quality Assurance Analyst
A Quality Assurance Analyst is responsible for testing and ensuring the quality of software products. This course would be beneficial to a Quality Assurance Analyst because it provides a deep understanding of the software architecture patterns necessary to take large software systems that make use of big data to production. The course also covers topics such as predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems, which are all essential knowledge for a Quality Assurance Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/qgl8j5/software
Reading list
We've selected 13 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
Software Architecture Patterns for Big Data.
This comprehensive guide provides a complete overview of big data analytics, including techniques, challenges, and case studies. It valuable resource for both beginners and experienced professionals in the field.
The definitive guide to Hadoop, written by one of its original creators. It covers the latest features of Hadoop 3 and provides practical guidance on how to use Hadoop for real-world data processing tasks.
The definitive guide to Spark, written by one of its original creators. It covers the latest features of Spark 3 and provides practical guidance on how to use Spark for real-world data processing tasks.
A comprehensive guide to machine learning using Python. It covers the latest machine learning algorithms and techniques, and provides practical guidance on how to use them for real-world data analysis tasks.
A practical guide to deep learning using Python. It covers the latest deep learning algorithms and techniques, and provides practical guidance on how to use them for real-world data analysis tasks.
A hands-on guide to data science using Python. It covers the entire data science lifecycle, from data collection and cleaning to data analysis and visualization.
A classic textbook on statistical learning. It covers the latest statistical learning algorithms and techniques, and provides practical guidance on how to use them for real-world data analysis tasks.
Save
A comprehensive textbook on machine learning. It covers the latest machine learning algorithms and techniques, and provides practical guidance on how to use them for real-world data analysis tasks.
Save
A comprehensive textbook on deep learning. It covers the latest deep learning algorithms and techniques, and provides practical guidance on how to use them for real-world data analysis tasks.
A practical guide to data science for business professionals. It covers the latest data science algorithms and techniques, and provides practical guidance on how to use them for real-world business problems.
A comprehensive guide to advanced analytics with Spark. It covers the latest Spark algorithms and techniques, and provides practical guidance on how to use them for real-world big data analysis tasks.
A comprehensive guide to data management for big data. It covers the latest big data storage technologies and techniques, and provides practical guidance on how to use them for real-world big data management tasks.
A comprehensive guide to big data security. It covers the latest big data security risks and challenges, and provides practical guidance on how to mitigate them.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qgl8j5/software
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Effort
4 weeks of study, 6-8 hours/week
Level
Advanced
Via
Coursera
Institution
University of Colorado Boulder
Instructors
Tyson Gern
Mike Barinek
Language
English
Transcripts
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This course belongs to a
collection
called
Software Architecture for Big Data. It's comprised of three courses:
- Applications of Software Architecture for Big Data
- Fundamentals of Software Architecture for Big Data
- Software Architecture Patterns for Big Data (viewing)
Good to know
Develops skills in scaling production data stores to perform under load, preparing learners to design load tests ensuring applications meet performance requirements
Focuses on how to take large software systems using big data to production, equipping learners with relevant practical skills
Taught by Tyson Gern and Mike Barinek, providing learners with access to experienced professionals
Examines predictive models, performance of distributed systems, horizontal distribution of large workloads, and highly available distributed systems
Explicitly requires learners to come in with extensive background knowledge
Belongs to a series of other courses, which can be a sign of detail and higher comprehension
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