The Essential Guide to System Design in 2025

I've spent the better part of a decade writing about different ways to help engineers learn new skills and level up their careers. So if we've crossed paths before, you might already know that I have two great passions in life: 

The first is System Design. 

Put simply, System Design is the process of understanding a system’s requirements and creating an infrastructure to satisfy them.

Being a talented coder in the AI era isn’t enough. To truly excel in this industry, you need to be an engineer who can architect. This means understanding how critical pieces fit together, scale, and stay resilient under immense pressure. 

And very few disciplines reward rigorous thinking the way System Design does.

Get the software architecture right from the get go and you create the kind of quiet resilience that helped Zoom usher in a new era of remote work during the COVID-19 pandemic. Miss a detail? You risk high-profile failures like the architectural gaps at Okta that let attackers hijack admin sessions across multiple customers in 2023. There's too much on the line — for both the world and your career — to possess anything but an absolute mastery of System Design theory.

As for the second passion? That would be coffee.

I don't think this passion needs any particular explanation. But I suppose it shouldn't come as a great surprise that these two interests share a few glaring similarities.

Just as a barista prepares for the morning rush, dials in the grinder, and times each shot to perfection, a student of System Design must size up traffic patterns, calibrate resources, and orchestrate services so that every user enjoys a smooth, reliable experience. 

Throughout this guide, I'll help you wrap your head around key System Design concepts through the lens of a barista tasked with keeping their shop running smoothly and their patrons happily caffeinated. 

And don't worry: I promise this won’t turn into one of those "summer at Grandma's" stories that Googlers searching for new recipes know all too well. But I do think this analogy will be helpful in truly grasping and applying the concepts of System Design — especially today, as the complexities of modern systems reach new heights with the integration of AI.

Here's what you can expect to walk away with:

• How System Design has evolved from the early 2000s to the AI era of today.

• The ten core concepts that underpin modern software architecture.

• Essential functional and non-functional considerations for intelligent, scalable systems.

• Key architectural types and styles and when to use them.

• Real-life case studies of high-profile System Design wins, failures, and comebacks.

• Suggestions on further reading to supplement your learning.

So grab a seat (and definitely an espresso) and let's get started.

I. Understanding System Design in 2025#

Every day, I see talented engineers who have mastered algorithms and data structures writing elegant, bug-free code. That’s fantastic. But when it comes to building systems that serve millions, handle petabytes of data, or power the next generation of AI, a different skill set is required.

And in 2025, this skillset looks quite different from a decade ago.

While many of the essential patterns still remain relevant, Modern System Design sits at the crossroads of two powerful currents: mature cloud‑native practices and an explosion of AI‑native workloads. Coding skill alone no longer carries a team across that intersection — but thoughtful architecture does.

Amazon paved the way by mainstreaming service‑oriented architecture and cloud infrastructure through AWS, while Google raised the bar with MapReduce, Spanner, and Kubernetes. Together, their influence pushed the industry from slow, monolithic deployments toward modular, self‑healing services.

The next leap forward is driven by large language models (LLMs), retrieval‑augmented generation (RAGs), and autonomous agents. Intelligence is no longer bolted on at the edge — it sits in the request path, learning, reasoning, and adapting in real time. This shift adds new questions to the classic trio of latency, availability, and throughput:

• How will each component learn and adapt as data drifts?

• Where does real‑time knowledge live, and who curates it?

• What does control flow look like when an autonomous agent acts before a human prompt?

• How do we bound cost when model inference dwarfs the rest of the bill?

With this context, let’s explore the key concepts that define thoughtful and effective System Design.

II. Core Concepts#

System Design turns product ideas into reliable, scalable services.

Whether you're an engineer chasing millisecond latencies, a product manager aligning roadmaps, or an architect future‑proofing a platform, the same ten concepts surface again and again. You can think of these as the fundamental building blocks of System Design.

Below, each concept gets a plain‑English definition, a quick trade‑off note, and (where it helps) an easy analogy from the espresso bar.

Let’s discuss these concepts one by one:

1. Data storage strategies#

Data storage strategies shape how information is organized, accessed, and scaled across a system’s architecture. When designing a system, engineers must pick the right storage method based on data structure, query patterns, latency requirements, and consistency needs.

Relational databases like PostgreSQL or MySQL are often suitable for transactional systems that require strong consistency and structured relationships. In contrast, NoSQL databases like Cassandra or MongoDB may better fit applications that need high write throughput, flexible schemas, or horizontal scalability. Cloud-native applications may also leverage object storage services like Amazon S3 to efficiently manage large files or unstructured data.

Beyond the choice of database type, storage strategies must also consider how to handle growth and performance under scale. This involves techniques like indexing for faster queries, designing read-heavy or write-heavy optimizations, and using time-series databases for telemetry data.

☕ Beans stay in airtight hoppers, grounds in portafilters, milk in a cold pitcher. Use the wrong container and freshness tanks fast.

2. Database partitioning & sharding#

Data partitioning and sharding are strategies for breaking large datasets into smaller, more manageable pieces to improve performance and scalability. In partitioning, data is divided within a single database instance, often across tables or files based on logical rules such as date ranges or user IDs. This helps reduce query load and improve access speed by limiting the data each query has to scan. Partitioning usually happens at the database level and stays transparent to the application logic. There are two types of partitioning: horizontal and vertical partitioning, as explained with the help of the following visual:

On the other hand, sharding distributes data across multiple database instances or servers, with each shard containing a unique subset of the data. This is essential for systems that have outgrown the capacity of a single database. However, sharding adds complexity in routing queries, maintaining consistency, and handling joins across shards. Effective shard key design is crucial, as poor choices can lead to hotspots or uneven data distribution. Sharding enables horizontal scalability in large-scale systems, allowing the system to grow seamlessly with user demand and data volume.

☕ Split the orders between two espresso stations: Barista A handles odd-number tickets, Barista B handles even-number tickets. Drinks fly out faster, but you now have to track bean levels and shot timings across both counters.

3. Redundancy & replication#

Redundancy means duplicating critical components of a system to improve its reliability, availability, and fault tolerance. By having backups or alternate instances in place, redundancy eliminates single points of failure and ensures the system can continue functioning even when a component goes down. For example, running multiple instances of a service across different machines or zones allows the system to seamlessly redirect traffic if one instance fails. This greatly improves uptime and the overall user experience.

Replication works alongside redundancy by keeping the duplicate components synchronized. It ensures that data or system state remains consistent across redundant resources. In database systems, replication is commonly implemented using a primary-replica model, where the primary node handles all write operations, and replicas receive and apply those changes in near real-time. This setup improves read scalability, supports disaster recovery, and increases overall system resilience. Replication can also be extended across geographic regions to reduce latency and maintain availability during regional outages.

4. Load balancing#

Load balancing distributes incoming traffic across multiple servers to ensure no single server becomes a bottleneck. Load balancers enhance system responsiveness and improve overall reliability by spreading workloads evenly. They also enable applications to handle large numbers of concurrent users without performance degradation.

Whether implemented through hardware components or software-based solutions, a load balancer sits between clients and backend servers. It routes each incoming request based on defined algorithms such as round-robin, least connections, or server response time. Load balancers also run continuous health checks on servers, automatically redirecting traffic away from unresponsive or underperforming servers. This helps maintain high availability, prevents service interruptions, and supports horizontal scaling in distributed architectures.

☕ The head barista funnels each drink ticket to the espresso machine with the shortest queue — if one machine sputters, orders shift to the others, keeping lines short, coffee hot, and pressure balanced.

5. Caching#

Caching is a technique that stores frequently accessed data or computational results in a temporary, high-speed storage area called a “cache.” The main purpose of caching is to reduce the need to recompute or re-fetch data from slower, more distant sources, like a database or a remote server. 

When an application needs specific data, it first checks the cache. If the data is found there, it can be retrieved almost instantly. If not, the data is fetched from its source, processed, and often stored in the cache for future faster access. This process significantly improves system performance by reducing latency and decreasing the load on primary data sources, resulting in faster response times and more efficient resource use.

☕ The barista brews a pot of house drip and stores it in a thermal carafe. So when someone orders the usual, the barista pours straight from the carafe (in this case, the cache) instead of starting a fresh brew, cutting wait times and easing the load on the espresso machine.

6. Content delivery network#

A content delivery network (CDN) is a globally distributed network of servers that work together to deliver web content, media, and other assets to users based on their geographic location. The primary goal of a CDN is to reduce latency and improve performance by serving content from servers that are physically closer to the user.

When a user requests content, such as a web page, image, or video, the CDN first checks if that content is cached on a nearby edge server, if it is, the content is served immediately. If not, the edge server fetches it from the origin server, stores a local copy, and then delivers it to the user. This caching mechanism reduces the need for repeated trips to a central origin server, lowering response times and decreasing the load on backend infrastructure.

CDNs also improve availability and fault tolerance by automatically rerouting requests to healthy servers and balancing traffic across multiple nodes. They play a vital role in modern System Design, especially for high-traffic applications where speed, scalability, and global reach are critical.

☕ Instead of sending every customer to one roast house, stash beans at regional cafés for accessible and sustainable service.

7. Rate limiting and throttling#

Rate limiting is a mechanism that limits how many requests a user or client can make to a service within a specific time window. This helps prevent abuse, ensure fair usage, and protect system resources from being overwhelmed during traffic spikes or malicious attacks. For example, an API might allow users to make only 100 requests per minute. Additional requests are rejected with an appropriate error response if the limit is exceeded.

Effective rate limiting improves system stability, helps maintain consistent performance, and safeguards backend services from excessive load. It is typically implemented at the API gateway or load balancer level, using algorithms like fixed window, sliding window, token bucket, or leaky bucket.

☕ The barista politely asks bulk orders to wait while the queue clears, preventing grinder overload.

8. Asynchronous processing#

Asynchronous processing allows systems to handle tasks outside the main execution flow, improving responsiveness and scalability. Instead of waiting for a task to complete, like sending an email or processing a payment, the system places the task/messages into a messaging queue. Workers then pull tasks from the queue and process them independently. This approach decouples components, smooths out traffic spikes, and allows systems to recover more gracefully from partial failures. Tools like RabbitMQ and Amazon SQS are commonly used to implement reliable message queuing with features like retry logic and dead-letter queues.

In more dynamic and event-driven architectures, publisher-subscriber (pub/sub) systems enable real-time communication between services. A producer (or publisher) emits messages to a topic, and multiple consumers (subscribers) receive those messages independently. This model is ideal for use cases like event notifications, system monitoring, and real-time analytics. Pub/sub systems like Google Cloud Pub/Sub, Redis Streams, or Apache Kafka allow for high throughput and loose coupling between services, making them a core pattern for scalable, reactive System Design.

9. CAP theorem#

The CAP theorem is a fundamental theorem within the field of System Design. It states that a distributed system can only provide two properties simultaneously: consistency, availability, and partition tolerance. The theorem formalizes the tradeoff between consistency and availability when there’s a partition. The following illustration further explains the CAP theorem:

10. PACELC theorem#

A question that the CAP theorem doesn’t answer is what choices a distributed system has when there are no network partitions. The PACELC theorem answers this question. The PACELC theorem states the following about a system that replicates data:

• if statement: A distributed system can trade off between availability and consistency if there’s a partition.

• else statement: When the system is running normally in the absence of partitions, the system can trade off between latency and consistency.

The first three letters of the PAC theorem are the same as the CAP theorem. The ELC is the extension here. The theorem assumes we maintain high availability by replication. When there’s a failure, the CAP theorem prevails. If there isn’t a failure, we still have to consider the tradeoff between consistency and latency of a replicated system.

☕ If the main grinder jams (partition), you face a trade-off: keep pouring slightly uneven shots with the backup grinder to stay open (availability) or pause service until the primary grinder is fixed for perfect consistency. When everything is humming (no partition), the choice shifts to tamping each shot with precision for flavor (consistency) versus speeding up pulls to shorten the line (latency).

III. Essential System Design Considerations#

Now that you have your core System Design building blocks in place, let's take it a step further.

Essential System Design considerations are core principles that guide how a system is structured and built.

These considerations ensure the system can handle growth, deliver a seamless user experience, recover from failures, and remain adaptable. Ignoring them leads to brittle systems that break under load, incur high costs, or are difficult to evolve.

Some of the core System Design considerations include:

1. Scalability

2. Reliability

3. Availability

4. Performance

5. Security and authentication

Let’s expand on each of the design considerations, starting with scalability:

1. Scalability#

Scalability refers to a system’s capacity to efficiently grow and manage increased demand while maintaining consistent performance. For example, an online learning platform must be able to handle sudden spikes in traffic during enrollment periods or live sessions without experiencing slowdowns or outages. To achieve this, systems rely on two primary approaches to scaling: horizontal scaling and vertical scaling.

• Horizontal scaling, also known as scaling out, involves adding more servers or nodes to the existing system. This approach increases the computing capacity by distributing the workload across multiple machines.

• Vertical scaling, or scaling up, means upgrading the existing server by adding more CPU, memory, or storage. This enhances the capabilities of a single machine, allowing it to handle more load independently.

Both types of scaling have pros and cons. In some scenarios, you’ll need to consider the tradeoffs and decide which type of scaling is best for your use case.

2. Reliability#

Reliability is the ability of a system to consistently perform its intended functions without failure over time. It ensures users can depend on the system to work correctly, even under adverse conditions. For instance, a cloud-based file storage service like Google Drive must reliably store and retrieve user files without data loss, corruption, or unexpected downtime.

Reliability builds user trust, reduces operational disruptions, and ensures business continuity. Without it, even well-performing systems can cause critical failures that lead to data loss, user dissatisfaction, and financial loss.

☕ The espresso machine runs an automatic purge and pressure-check between shifts, so every shot tastes the same at 6 a.m. Monday and 9 p.m. Friday.

3. Availability#

Availability measures a system’s readiness for use, specifically when it remains operational and accessible. For instance, an online banking system must be available 24/7 so customers can check balances, transfer funds, or make payments anytime.

Achieving high availability depends on redundancy through multiple instances and data replication to eliminate single points of failure. Implementing failover strategies and continuous health checks enables quick detection and replacement of unhealthy components, minimizing downtime.

4. Performance#

Performance refers to how quickly and efficiently a system responds to user requests and processes data. For example, a video streaming service must deliver smooth playback with minimal buffering, even during peak usage.

Techniques like caching reduce latency by storing frequently accessed data closer to users, while load balancing distributes traffic to optimize resource use. Employing asynchronous processing for heavy tasks ensures responsiveness, especially in user interfaces.

☕ A digital order board lights up tickets the instant they’re placed, so baristas jump on the next drink without hunting for paper chits, shaving seconds off every cup and keeping the queue moving.

5. Security and authentication#

Security in System Design involves protecting systems and data from unauthorized access, misuse, and cyber threats. This is especially critical in applications handling sensitive data. For example, an e-commerce platform must protect customer payment details, personal information, and transaction records to prevent breaches and fraud. Without strong security measures, even a well-architected system can become a liability.

Modern security strategies rely on a principle known as defense in depth, which involves layering protections at multiple levels: network, application, and data. Authentication verifies a user’s identity, and best practices include implementing multi-factor authentication (MFA) to reduce the risk of unauthorized access. Authorization ensures that users and services have access only to the resources they are permitted to use, following the principle of least privilege.

To further protect data, systems should use encryption in transit (TLS/SSL protocols) and encryption at rest (securing stored data with technologies like AES). Additionally, using secure API gateways, rotating credentials regularly, logging security events, and performing routine audits are essential to maintaining a secure and resilient architecture.

With a strong understanding of the key considerations, it’s now important to explore the different types of System Design that guide how systems are structured based on their scale, complexity, and purpose.

Checkpoint: Functional vs. Nonfunctional Requirements at the Café#

To better grasp the crucial distinction between the functional and nonfunctional requirements of System Design, imagine you're preparing for the most sacred (and delicious) of morning rituals: brewing the perfect cup of coffee.

Functional Requirements: Your Coffee Maker's Features#

Functional requirements outline exactly what tasks the coffee maker must perform to deliver on user expectations. Think of these as the baseline features:

• Brew coffee upon command: You press a button, and coffee reliably appears.

• Select coffee type: Espresso, drip coffee, cappuccino — options tailored precisely to user preference.

• Adjust brew strength: Whether you like your coffee mild or robust, the machine adjusts to meet your tastes.

• Dispense hot water or steam: Beyond just coffee, it meets broader needs like making tea or steaming milk.

These functional elements directly shape user interactions, defining the core capabilities that must exist for the coffee maker to fulfill its primary purpose.

Nonfunctional Requirements: #

Nonfunctional requirements, on the other hand, detail how effectively the coffee maker executes its functions. These requirements shape the overall quality and long-term satisfaction with the product. Key examples include:

• Performance (Quick brewing time): No one wants to wait too long for their coffee. The speed at which the machine brews coffee greatly influences user satisfaction.

• Reliability (Consistent temperature): The machine must reliably deliver coffee at the optimal temperature, ensuring the quality of each cup is consistent.

• Maintainability (Easy maintenance and cleaning): Regular, hassle-free upkeep keeps the machine in good shape and prevents disruptions.

• User experience (Quiet operation): An overly loud machine could disrupt the environment, making quiet operation essential, especially in shared spaces.

• Scalability and resilience (Energy efficiency and durability): Efficient energy usage and robust durability ensure the coffee machine continues performing well over time, even under heavy use.

These nonfunctional attributes don't define what the coffee maker does, but significantly influence how satisfying and usable it is, impacting user loyalty and brand reputation.

Who's thirsty?

IV. Types of System Design#

So you've got a grip on core components and essential considerations — now let's chat about the different types of System Design.

As someone who's spent years building and scaling systems at MAANG companies, truly mastering this discipline means understanding the different perspectives — and the different types — needed to build something robust, reliable, and lasting. You can break these down into two different categories:

1. Architectural styles: The fundamental blueprints that define how components are structured and interact, such as monolithic, microservices, and event-driven architectures.

2. Domain-specific System Design: This covers design approaches tailored to the unique requirements of specific domains, such as frontend System Design, generative AI System Design, etc.

1. Architectural styles#

Architectural styles are the core blueprint that dictates the entire structure, component interaction, and ultimately, your system’s scalability, maintainability, and performance. Get this right, and you lay a solid foundation; get it wrong, and you're building on quicksand.

Primarily, the architecture styles consist of:

• Monolithic architecture: Many applications begin here, in a single, unified unit where all components are tightly coupled and run within one process. A monolith can be incredibly efficient initially for startups or projects with a very clear, limited scope. It allows for rapid development and straightforward deployment (here is great resource for a more in-depth look at modern deployment strategies)

• Microservices architecture: Microservices architecture breaks a system into a collection of loosely coupled, independently deployable services, each responsible for a specific business function. This design promotes scalability, resilience, and team autonomy, but also introduces challenges like inter-service communication, data consistency, and increased operational complexity.

• Event-driven architecture: Event-driven architecture relies on the production, detection, and consumption of events to drive interactions between decoupled components. It is ideal for systems that require real-time responsiveness or asynchronous workflows, such as payment processing or notification services, but it demands careful design to manage event flow, delivery guarantees, and debugging.

• Serverless architecture: In serverless architecture, developers build and deploy applications without managing server infrastructure. Cloud providers dynamically allocate resources based on usage, allowing teams to focus on business logic. This model offers automatic scaling and cost efficiency, but it may introduce limitations in execution time, vendor lock-in, and cold-start latency.

• Modular monolithic architecture: A modular monolith keeps the single deployable unit of a monolith but organizes its internal structure into well-defined, loosely coupled modules. This approach balances simplicity and maintainability, allowing teams to enforce boundaries and scale development without the full complexity of microservices. The illustration below depicts a modular monolithic architecture for an e-commerce system, where all services are structured as separate modules within a single deployment.

2. Domain-specific System Design#

While architectural styles provide the skeleton, and design levels define the anatomy, true mastery lies in understanding the specific demands of different application domains. Each area presents unique challenges and requires a specialized application of design principles. Here are some examples of domain-specific System Design areas:

• Frontend System Design: This domain focuses on the client-side, such as the user interface and everything the user sees and interacts with directly in their browser or mobile app. In today’s competitive landscape, the user experience is paramount. Our design should emphasize intuitive user experience, high performance, efficient state management, and the creation of highly reusable UI components. Accessibility and seamless cross-browser compatibility are non-negotiable.

• Backend System Design: The backend is the engine room of any modern application, including the server-side infrastructure, business logic, databases, and APIs that power the frontend. This is where the heavy lifting happens, requiring critical decisions on data modeling and storage (SQL/NoSQL), designing robust APIs (REST, GraphQL), implementing complex business logic, handling concurrent requests at scale, and ensuring ironclad authentication, authorization, and data security. A weak backend will obstruct even the most brilliant frontend.

• Generative AI System Design: This represents the cutting edge of System Design, where applications are built to integrate and utilize advanced AI models capable of generating content such as text, images, speech, or video. It requires specialized infrastructure and robust data pipelines to scale and manage these models effectively while also addressing challenges related to latency, cost, and ethical implications.

V. Real-world System Design Case Studies#

Understanding how System Design principles are applied at scale and in the wild will help you get a grip on real-world best practices and current design trends.

The following are a few representative examples that illustrate how thoughtful design translates into robust, high-impact solutions.

• How Spotify wrapped scales for 7M users

• How the ChatGPT system works behind the scenes

• Rethinking microservices with the rise of AI agents

• How 100 M+ stream the Super Bowl

• How Dropbox keeps data accessible and synchronized

• System Design lessons from Netflix outage—Tyson vs. Paul Fight

• Designing for failures: Chaos engineering for System Design

These systems showcase how leading companies solve complex challenges related to scalability, resilience, performance, and evolving user demands.

VI. What’s Next?#

If you made it this far, I'd say a congratulations (and certainly another espresso) is in order. You're well on your way to building scalable, resilient software in 2025

And if this guide gave you a fresher way to look at software architecture — from core concepts to the trade-offs that keep systems healthy — I'd wholeheartedly encourage you to take the next steps to supplement your learning:

• Skim the System Design Interview Guide for deep dives on patterns, pitfalls, and whiteboard-ready frameworks to ace any System Design Interview.

• Test your thinking with our curated System Design Interview Questions & Answers.

• Explore granular design patterns and diagrams for specific languages like React.

• Trade notes with other software professionals and chime in on some of the best System Design conversations in communities like Reddit, GitHub, and LinkedIn.

Our hands-on courses are designed to bridge the gap between theory and practice, guiding you through real-world case studies, interactive scenarios, and proven architectural patterns. You’ll learn how to design systems that are scalable, resilient, and aligned with real engineering challenges.