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Kamal Sharma

Designing an application without knowing design fundamentals is like you are playing a game without knowing the rules. So You can consider design fundamentals as foundational knowledge for system design. It is as important as data structures for Coding interviews.

One thing is very important that there is nothing like wrong design, It always may be good or bad design. When interviewers ask you about system limitations or design problems then fundamental knowledge can be the key player to answer such questions because all the fundamentals are solutions for some specific problems.

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Designing an application without knowing design fundamentals is like you are playing a game without knowing the rules. So You can consider design fundamentals as foundational knowledge for system design. It is as important as data structures for Coding interviews.

One thing is very important that there is nothing like wrong design, It always may be good or bad design. When interviewers ask you about system limitations or design problems then fundamental knowledge can be the key player to answer such questions because all the fundamentals are solutions for some specific problems.

The goal of such a question is to check your problem-solving ability on a complex problem. It is quite understandable that you might not have the experience to design such big applications but proposing the best possible solutions would show your understanding of design fundamentals.

The interviewer wants to see if you consider common distributed system challenges in your design like distributing the data to multiple machines and aggregating them together later and equally distributing the load on multiple servers. It is also quite interesting to see your design approach when user throughput increases.

System design questions are quite open-ended and there is no standard answer for such questions. They want to see that you think about operational scenarios, edge cases, limitations, and assumptions.

Interviewers can decide whatever direction the discussion will go in. With that in mind, even for the same question, you may have a completely different discussion with a different interviewer.

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What's inside

Syllabus

System Design Fundamentals
Introduction
Data Partitioning
Data Redundancy and Replications
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Covers data partitioning, redundancy, and load balancing, which are essential for designing scalable and reliable systems
Explores SQL vs NoSQL databases, which is a crucial consideration when designing data storage solutions for different application needs
Includes a section on system design interviews, which directly addresses the needs of job seekers in the software engineering field
Discusses hashing and consistent hashing, which are important techniques for distributing data and load across distributed systems
Examines application deployment strategies, which are critical for ensuring smooth and efficient software releases and updates
Features a case study on designing a video streaming application like YouTube, offering practical insights into real-world system design challenges

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Reviews summary

Essential system design concepts for interviews

learners say this course provides a positive and solid foundation in system design, particularly for those preparing for technical interviews. Many found the breakdown of complex concepts like data partitioning and load balancing to be very clear and helpful. The inclusion of practical examples and an interview framework, such as the YouTube design case study, is frequently praised as a strong point that builds confidence. However, some reviewers note that certain sections, like Big Data solutions, could use more depth or hands-on exercises, suggesting it might be better suited for absolute beginners or as a theoretical introduction rather than for deep real-world architecture practice.
Well-suited for beginners or as an introduction.
"It's more of an introduction. If you already know the basics, it might not add much value. Better for absolute beginners."
"Solid overview... Good for refreshing knowledge or getting started."
Concepts are clearly broken down and explained.
"The breakdown of complex topics like data partitioning and load balancing is very clear."
"It demystified many system design concepts that felt intimidating before."
"The instructor explains concepts well."
"Cache and CDN explanations were particularly helpful."
Excellent for preparing for system design interviews.
"This course is excellent for preparing for system design interviews."
"As a software engineer preparing for interviews, this course was exactly what I needed."
"Solid overview of system design concepts... for interview prep."
"I feel much more confident discussing design trade-offs now."
Big Data section felt rushed or inadequate.
"Some parts felt a bit rushed, particularly the sections on Big Data solutions."
"The Big Data section was practically useless."
Some feel it lacks depth and hands-on exercises.
"Could use more depth or practical exercises."
"felt it was too theoretical. Not enough hands-on examples or code snippets."
"Needs significant updates and more detailed explanations."
"If you already know the basics, it might not add much value."

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 System Design Fundamentals with these activities:
Review Data Structures and Algorithms
Reinforce your understanding of fundamental data structures and algorithms, which are essential for making informed design choices in system design.
Show steps
  • Review common data structures like arrays, linked lists, trees, and graphs.
  • Practice implementing basic algorithms for searching, sorting, and graph traversal.
  • Solve problems on platforms like LeetCode or HackerRank to solidify your understanding.
Read 'System Design Interview – An Insider's Guide'
Prepare for system design interviews by studying common system design questions and solutions.
Show steps
  • Read the book chapter by chapter, focusing on the system design questions and solutions.
  • Practice solving system design questions on your own.
  • Participate in mock system design interviews with peers or mentors.
Read 'Designing Data-Intensive Applications'
Gain a deeper understanding of the underlying principles and trade-offs involved in designing data-intensive applications.
Show steps
  • Read the book chapter by chapter, taking notes on key concepts and design patterns.
  • Reflect on how the concepts discussed in the book relate to the topics covered in the course.
  • Consider how you might apply the design patterns and techniques described in the book to real-world system design problems.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Load Balancing Algorithms
Reinforce your understanding of load balancing algorithms by implementing and testing different algorithms in a simulated environment.
Show steps
  • Implement different load balancing algorithms, such as round robin, least connections, and weighted round robin.
  • Simulate a load balancing scenario with multiple servers and clients.
  • Measure the performance of each algorithm in terms of latency, throughput, and resource utilization.
  • Analyze the results and compare the performance of different algorithms.
Create a Blog Post on Consistent Hashing
Solidify your understanding of consistent hashing by explaining the concept in a clear and concise manner in a blog post.
Show steps
  • Research consistent hashing and its applications in distributed systems.
  • Write a blog post explaining the concept of consistent hashing, its benefits, and its limitations.
  • Include diagrams and examples to illustrate the concept.
  • Publish the blog post on a platform like Medium or your personal website.
Design a Scalable URL Shortener
Apply the concepts learned in the course to design a real-world system, such as a scalable URL shortener, to solidify your understanding of system design principles.
Show steps
  • Define the requirements and constraints of the system, including the expected scale and performance characteristics.
  • Design the architecture of the system, including the data model, caching strategy, and load balancing scheme.
  • Implement a prototype of the system using a programming language and technologies of your choice.
  • Evaluate the performance and scalability of the system, and identify areas for improvement.
Create a Presentation on Data Partitioning Strategies
Deepen your understanding of data partitioning strategies by creating a presentation that explains different strategies and their trade-offs.
Show steps
  • Research different data partitioning strategies, such as horizontal partitioning, vertical partitioning, and directory-based partitioning.
  • Create a presentation that explains each strategy, its benefits, and its limitations.
  • Include diagrams and examples to illustrate the concepts.
  • Present the presentation to your peers or mentors.

Career center

Learners who complete System Design Fundamentals will develop knowledge and skills that may be useful to these careers:
Site Reliability Engineer
A site reliability engineer (SRE) focuses on ensuring the reliability, scalability, and performance of systems in production. This course gives the SRE a deeper understanding of system design principles. It also provides understanding of data partitioning, redundancy, caching, and load balancing. The course's content about thinking about operational scenarios, limitations, and assumptions is helpful. The course also covers application deployment strategies, which is directly applicable to challenges that an sre faces on the job.
Solutions Architect
A solutions architect is responsible for designing and implementing IT systems that meet an organization's specific needs. This course, with its focus on system design fundamentals such as data partitioning, redundancy, caching, and load balancing, helps build a strong foundation. A solutions architect often faces challenges related to data distribution, system scalability, and handling increased user throughput. The course's emphasis on problem-solving in complex system designs helps one prepare to propose effective solutions. The course also explains big data solutions, application deployment strategies, and SQL versus NoSQL databases. This is directly applicable to the challenges a solutions architect will face on the job.
Systems Engineer
A systems engineer focuses on designing, integrating, and managing complex systems over their life cycles. The course's focus on system design fundamentals is highly relevant to the role of a systems engineer. A systems engineer must be able to consider operational scenarios, edge cases, limitations, and assumptions. The course's coverage of data partitioning, redundancy, and replication helps a systems engineer design robust systems. Furthermore, the discussion of load balancing and application deployment strategies is directly applicable.
Data Engineer
A data engineer builds and maintains the infrastructure required for data storage, processing, and analysis. Data engineers need to understand data partitioning, redundancy, and big data solutions, all covered in this course. Data engineers need to understand the trade-offs between SQL and NoSQL databases, a topic explored in the course. The emphasis on system limitations and design problems is relevant to the challenges of building robust data pipelines and infrastructure. The course also covers hashing.
Cloud Architect
A cloud architect designs and oversees a company's cloud computing strategy, including cloud adoption plans, cloud application design, and cloud management. A cloud architect needs to understand load balancing, data partitioning, and caching strategies, all covered in this course. Cloud architecture requires careful consideration of data redundancy and replication, another key area highlighted in the course. As someone working with the design of distributed systems, the course's exploration of common distributed system challenges will be helpful. The course also explains application deployment strategies, which is directly applicable to the challenges a cloud architect will face on the job.
Principal Engineer
A principal engineer is a high-level technical expert responsible for setting the technical direction for a team or organization. A principal engineer requires a deep understanding of system design principles. The course gives principal engineers a deeper understanding of data partitioning, redundancy, caching, and load balancing. This will inform strategic technology choices; the course's discussion of system design trade-offs is also valuable. This course will also cover big data solutions. This will increase this professional's knowledge.
Data Architect
A data architect designs and manages the data infrastructure for an organization, ensuring data is stored, processed, and accessible efficiently. This course helps build knowledge of data partitioning, redundancy, and big data solutions. A data architect needs to understand the trade-offs between SQL and NoSQL databases, a topic covered in this course. As with system design, a data architect needs to understand how to design and architect in a way that anticipates increased user throughput. This course may be of assistance. The course's emphasis on system limitations and design problems is relevant.
Performance Engineer
A performance engineer identifies and resolves performance bottlenecks in software systems. This course is relevant to a performance engineer due to its focus on system design fundamentals. A performance engineer needs to understand load balancing, caching, and data partitioning, all of which are covered in the course. These concepts are crucial for optimizing application performance and scalability. The course's discussion of hashing and consistent hashing is also relevant for improving data access speeds.
Technical Lead
A technical lead guides a team of engineers, making key technical decisions and ensuring projects align with technical standards. This course provides a foundation for making informed decisions about system architecture and design. As a technical lead, you will benefit from the course's coverage of data partitioning, redundancy, caching, and load balancing. These concepts are essential for leading discussions and making strategic choices about the system's technical direction. The course also covers application deployment strategies, essential to the technical lead.
Machine Learning Engineer
A machine learning engineer develops, deploys, and maintains machine learning models and systems. This course offers help in several ways. A machine learning engineer needs to understand data partitioning, redundancy, and caching, all covered in the course. The course's discussion of big data solutions is also valuable, as machine learning often involves processing large datasets. The course's coverage of application deployment strategies is also relevant, as it helps machine learning engineers deploy models effectively.
Backend Engineer
A backend engineer is responsible for server-side logic and databases that power applications. This course may be useful for a backend engineer through its focus on system design fundamentals. The backend engineer will benefit from coverage of data partitioning, redundancy, SQL versus NoSQL databases, and caching strategies. These topics are crucial for building scalable and reliable backend systems. The course's insights into load balancing and big data solutions are also relevant for handling high traffic and large datasets.
Database Administrator
A database administrator manages and maintains databases, ensuring data integrity, security, and availability. A database administrator benefits from the discussion of data partitioning and replication that this course provides. It also covers the trade-offs between SQL and NoSQL databases, which is a key consideration for database administrators. The course's emphasis on system limitations and design problems is relevant to the challenges a database administrator faces when trying to maintain a large database for the long term. The course also covers hashing, which will be of use.
DevOps Engineer
A DevOps engineer works to automate and streamline the software development lifecycle, focusing on collaboration between development and operations teams. The course's coverage of application deployment strategies is directly relevant to a DevOps engineer's responsibilities. Understanding load balancing, data partitioning, and caching, as covered in the course, helps in optimizing application performance and scalability. As well, this course will introduce you to distributed systems challenges. This may be useful for this career role.
Software Architect
A software architect makes high-level design choices and dictates technical standards, including coding standards, tools, and platforms. The course's emphasis on system design fundamentals builds a foundation for a software architect. This may be useful in understanding how to design applications, partition data, handle redundancy, and implement caching strategies. The course also covers load balancing, a crucial consideration for software architecture. It also covers SQL and NoSQL databases, which will inform decisions about data storage solutions. As such, this course may be useful.
Full Stack Engineer
A full stack engineer works on both the front-end and back-end of applications. While full stack engineers are not always expected to be system design experts, the skills attained in a course such as this will still prove useful. This course focuses on system design fundamentals. As such, it may be of use in data partitioning, data redundancy, caching, and load balancing. This knowledge helps the full stack engineer better see how their work connects to and relies on the backend and databases.

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 System Design Fundamentals.
Provides a comprehensive overview of the concepts and technologies used in building data-intensive applications. It covers topics such as data storage, data modeling, data consistency, and distributed systems. It is highly recommended as a reference text for this course, as it provides a deeper understanding of the underlying principles behind system design decisions. This book is commonly used by industry professionals.
Provides a practical guide to preparing for system design interviews. It covers common system design questions and provides step-by-step solutions. It valuable resource for students who are preparing for technical interviews. This book is commonly used by students preparing for technical interviews.

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