Software Architecture Patterns for Big Data

Software Architecture Patterns for Big Data

This course is part of Software Architecture for Big Data Specialization

Instructors: Tyson Gern

3,156 already enrolled

Included with Coursera Plus

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

3.6

(28 reviews)

Advanced level

Recommended experience

Flexible schedule

Approx. 25 hours

Learn at your own pace

Build toward a degree

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

3.6

(28 reviews)

Advanced level

Recommended experience

Flexible schedule

Approx. 25 hours

Learn at your own pace

Build toward a degree

Learn more

What you'll learn

Compare, measure, and test big data models for production use.
Write custom performance tests to measure the characteristics of a distributed system.
Use queues to horizontally distribute large workloads.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Software Architecture for Big Data Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 4 modules in this course

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

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.

What's included

3 videos8 readings1 assignment1 peer review

3 videosTotal 14 minutes

Introduction to the Course2 minutesPreview module
Introduction to Match Predictor Codebase4 minutes
Match Predictor Models7 minutes

8 readingsTotal 225 minutes

Earn Academic Credit for Your Work! 10 minutes
Course Support10 minutes
Motivation for Peer Review10 minutes
Introduction to Prediction Models30 minutes
Evaluation Metrics for Prediction Models30 minutes
Soccer Match Predictor45 minutes
Evaluating a Model60 minutes
Creating Reports & Automating Evaluation30 minutes

1 assignmentTotal 30 minutes

Predictive Models30 minutes

1 peer reviewTotal 90 minutes

Predictive Model Coding Exercise90 minutes

In this week, you will learn how to ensure your distributed system operates as expected in production by writing performance tests.

What's included

3 videos9 readings1 assignment1 peer review

3 videosTotal 21 minutes

Contact Tracing7 minutesPreview module
Direct Exchange4 minutes
Introduction to Email Verifier Codebase9 minutes

9 readingsTotal 330 minutes

Distributed Systems & Distributed Workloads45 minutes
Performance Increase with Distributed Systems15 minutes
What is a Messaging Queue?10 minutes
RabbitMQ45 minutes
Messaging Queues and Large Workloads75 minutes
Introduction to Email Verifier Codebase60 minutes
Identifying Performance Concerns10 minutes
Writing Performance Tests10 minutes
Interpreting Output of Performance Test60 minutes

1 assignmentTotal 30 minutes

Considerations for Messaging Queues30 minutes

1 peer reviewTotal 60 minutes

Performance Test Coding Exercise60 minutes

This week you will use queues to horizontally distribute large workloads.

What's included

2 videos6 readings3 assignments1 peer review

2 videosTotal 12 minutes

Performance Testing – Custom Benchmark7 minutesPreview module
Consistent Hash Exchange4 minutes

6 readingsTotal 175 minutes

Writing a Performance Test for a Business Requirement 45 minutes
Making Improvements Based on Results15 minutes
More on Testing30 minutes
Consistent Hash Ring45 minutes
Consistent Hash Exchange30 minutes
Messaging Queues and High Availability Databases10 minutes

3 assignmentsTotal 120 minutes

Improvements60 minutes
Improvements30 minutes
Consistent Hash Exchange Quiz30 minutes

1 peer reviewTotal 60 minutes

Performance Test for Business Requirement60 minutes

In the last week of this course, you will learn the advantages and disadvantages of high availability distributed systems.

What's included

1 video6 readings2 assignments

1 videoTotal 5 minutes

CAP Theorem Trade-offs5 minutesPreview module

6 readingsTotal 290 minutes

What is Availability?90 minutes
Hindrance to Availability60 minutes
High Availiability Databases60 minutes
Tradeoffs Regarding High Availability Databases60 minutes
Delay, Memory & Messaging Tradeoffs in Distributed Systems10 minutes
Replication10 minutes

2 assignmentsTotal 60 minutes

CAP Theorem 30 minutes
High Availability Databases30 minutes

Instructors

Tyson Gern

University of Colorado Boulder

3 Courses10,274 learners

Offered by

University of Colorado Boulder

Explore more from Software Development

University of Colorado Boulder
Applications of Software Architecture for Big Data
Course
University of Colorado Boulder
Software Architecture for Big Data
Specialization
University of Colorado Boulder
Fundamentals of Software Architecture for Big Data
Course
University of Colorado Boulder
Network Principles in Practice: Cloud Networking
Course

Build toward a degree

This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

3.6

28 reviews

5 stars
46.42%
4 stars
21.42%
3 stars
3.57%
2 stars
3.57%
1 star
25%

Showing 3 of 28

Reviewed on Mar 3, 2024

Really an advanced course, and i loved it. Sometimes, it need better specifications on assignments.

View more reviews

New to Software Development? Start here.

Open new doors with Coursera Plus

Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Learn more

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Explore degrees

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Learn more

Frequently asked questions

A cross-listed course is offered under two or more CU Boulder degree programs on Coursera. For example, Dynamic Programming, Greedy Algorithms is offered as both CSCA 5414 for the MS-CS and DTSA 5503 for the MS-DS.

· You may not earn credit for more than one version of a cross-listed course.

· You can identify cross-listed courses by checking your program’s student handbook.

· Your transcript will be affected. Cross-listed courses are considered equivalent when evaluating graduation requirements. However, we encourage you to take your program's versions of cross-listed courses (when available) to ensure your CU transcript reflects the substantial amount of coursework you are completing directly in your home department. Any courses you complete from another program will appear on your CU transcript with that program’s course prefix (e.g., DTSA vs. CSCA).

· Programs may have different minimum grade requirements for admission and graduation. For example, the MS-DS requires a C or better on all courses for graduation (and a 3.0 pathway GPA for admission), whereas the MS-CS requires a B or better on all breadth courses and a C or better on all elective courses for graduation (and a B or better on each pathway course for admission). All programs require students to maintain a 3.0 cumulative GPA for admission and graduation.

Yes. Cross-listed courses are considered equivalent when evaluating graduation requirements. You can identify cross-listed courses by checking your program’s student handbook.

You may upgrade and pay tuition during any open enrollment period to earn graduate-level CU Boulder credit for << this course/ courses in this specialization>>. Because << this course is / these courses are >> cross listed in both the MS in Computer Science and the MS in Data Science programs, you will need to determine which program you would like to earn the credit from before you upgrade.

MS in Data Science (MS-DS) Credit: To upgrade to the for-credit data science (DTSA) version of << this course / these courses >>, use the MS-DS enrollment form. See How It Works.

MS in Computer Science (MS-CS) Credit: To upgrade to the for-credit computer science (CSCA) version of << this course / these courses >>, use the MS-CS enrollment form. See How It Works.

If you are unsure of which program is the best fit for you, review the MS-CS and MS-DS program websites, and then contact datascience@colorado.edu or mscscoursera-info@colorado.edu if you still have questions.

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.