From Beginner to Expert: Your Go-to Guide to AWS Scalability
Learn everything you need to know about AWS scalability, starting with definitions and benefits. Then, get into the weeds of how it works, which cloud to choose for hosting, and tips to design a scalable infrastructure.
1 min read
When do you think of scalability? Before designing the architecture or as soon as your system starts to show signs that it can’t handle the increased traffic?
In an ideal world, scalability is embedded into all areas of your architecture design. However, the reality is that many businesses think of scalability when it starts to become a problem. Either when it gets expensive to pay for workarounds and system overhead or when customers complain about latency and performance.
Whether you’re moving from a legacy system and redesigning your architecture or designing your architecture from scratch, AWS can be a great partner.
That’s why in this guide, you'll find definitions, benefits, and common mistakes about AWS scalability. You'll also get tips on how to build a scalable infrastructure and choose the right AWS services for your industry.
Need to redesign your architecture but lack the skills or time to do it in-house? Augment your team with NaNLABS. We treat your challenges as our own and offer tailored long-term solutions.
PS: This is a long guide and we don’t want to bore you out. If you’re already familiar with the basic concepts, use the table of contents to jump to the section that interests you the most.
What exactly is scalability
Scalability is your system’s ability to meet demand changes. This can happen vertically or horizontally.
Vertical scalability means replacing your current resources with larger (scaling up) or smaller ones (scaling down). For example, let’s say you have a RAM capacity of 4GB and need to increase it to 5GB. However, the next available storage tier is for 8GB. Since you need to replace your current computer with a new one, you’ll need to pay an extra 3GB that you don’t really need.
Horizontal scalability, on the other hand, is about adding (scaling out) or reducing (scaling in) resources to meet the demand. In the same example, you can add extra virtual computers to increase your RAM capacity and pay for the instances that you truly need.
Scalability vs elasticity
These concepts are closely intertwined but not necessarily dependent on one another. A system is scalable if it can increase its capacity to meet the demand. But we say a system is elastic if it can closely match that capacity with the demand changes—this includes increasing and reducing capacity. In other words, “Elasticity is the ability of the system to have enough resources to meet unplanned demand changes without wasting capacity,” says Gustavo Alberola, Software Development Advocate at NaNLABS.
Let’s illustrate this difference with an example. Imagine you’re a local ticketing platform that won the licitation to sell the Oasis reunion concert tickets. You know for sure that this event will represent a huge traffic influx to your website. Since you usually sell tickets for local artists, your current servers can’t handle this as they are.
The show producers know this. The only reason why you won over this huge deal is because you have a robust, scalable, and elastic software solution.
So, you increase the capacity after anticipating a huge spike while leaving some room for the system to adjust (increase or reduce) automatically as needed. Once the tickets are sold, you can scale down the system to avoid wasting resources. Without scalability, you risk your system crashing and upsetting your customers—you don’t want to get Oasis fans angry.
Scalability according to AWS
When Amazon first hit the market in 1994, it had one motto: Get big fast. But when it did, Amazon realized its architecture couldn’t keep up with its fast growth. The company redesigned its 90s architecture from shared databases between services to a service-oriented architecture (SOA). And then, moved into a micro-services architecture, which is the one standing today.
Compilation of Amazon’s architecture redesign from the beginning up to now. In the last image, each little dot represents a microservice.
With these changes, Amazon realized that it wasn’t only about scalability. Its system needed to be able to grow by offering a consistent experience. That’s why for Amazon, scalability is seen as reliable scalability: offering services to grow or shrink your operations easily in a sound and predictable way.
Reliability is also one of the six pillars of the AWS Well-Architected Framework. There, businesses can measure their reliability by answering questions regarding their system’s ability to recover from malfunctions and scale to meet the increasing demand over time.
Reliable scalability is your ability to scale your business in a reliable cloud that allows you to recover quickly in case something fails.
What is AWS Auto Scaling
AWS Auto Scaling is a feature that some Amazon services offer. It allows you to automatically monitor your tools and increase or reduce capacity to adapt to demand fluctuations.
Through auto-scaling, you can present your customers with high-performing tools, and protect your budget by reducing those resources once you no longer need them.
You can use Auto Scaling to increase your resources on services like:
Amazon EC2—for Instances and Spot Fleets
Amazon ECS—for Tasks
Amazon DynamoDB—for Tables, RCUs, and WCUs
Amazon Aurora—for Read Replicas
Choosing the right cloud provider: AWS vs Azure vs Google Cloud Platform (GCP)
AWS, Azure, and GCP are the three leading cloud providers, recognized for their high performance and reliability.
AWS is the biggest of them all and has a list of over 200 services that allow clients to build robust and scalable infrastructures. AWS is also great for building agile, innovative, and elastic systems.
Like AWS, Azure also supports multiple regions and offers a wide range of services. Azure is mostly suited for hybrid software solutions with data hosted both on-premises and cloud instances.
GCP, on the other hand, is more data-driven and is great for big data and ML projects. It’s also fast-loading and affordable for smaller companies.
What makes AWS services better for scalability
While these three providers will do a great job at hosting your infrastructure, AWS tends to be a better solution for scalability. “Amazon has made traffic-based scaling of infrastructure extremely easy, and there's an insane amount of engineering that you would need to do [with other clouds] to even come close to the level of AWS,” says Aaron Bell, Cloud Architect at EVEREVE.
Amazon offers AWS serverless solutions, auto-scaling, and elastic load balancing. These allow you to simplify operations while supporting growth. Plus, since these are managed services, you don’t need to spend time worrying about maintenance as you scale.
Additionally, AWS is scalable thanks to the wide range of purpose-built services that can process insanely big amounts of data and requests per second. Take a look at the processing power of services like DynamoDB, Amazon SQS, Amazon EBS, and Aurora during Prime Day 2022:
AWS is also more scalable because as mentioned above, its pricing model aligns with usage. So, it’s not only scalable in terms of system capacity but also friendly with your business finances to accompany your growth. You don’t need to commit to a quote for a long period or pay for hardware.
Lastly, since AWS was created as a solution to Amazon’s scalability needs, it comes with documentation and best practices tested by them—and now other big names in the industry. For instance, you can use the AWS Well-Architected Framework to conduct an infrastructure review and identify areas of opportunity.
Want to see if your software is well-architected? Let's review it together!
Benefits of using AWS services for scalability
Whether you’re migrating your on-prem system to the cloud or designing your software architecture, here are four benefits of using AWS services to improve your system scalability.
1. Better cost efficiency
You can scale vertically or horizontally with AWS services. This means you get to choose the most cost-efficient model for your business. Also, AWS offers pay-as-you-go models, right-sizing resources, and reserved instances. This combination of features makes AWS an affordable cloud for scaling your business.
2. Faster solutions
AWS has over 200 services. Many of which are tailored to specific industry problems. For example, AWS IoT FleetWise is an AWS automotive solution that collects and stores autonomous vehicle data in a fleet for better management.
This huge offer list allows you to come up with solutions faster. This gives you a competitive advantage because the faster you get to market, the higher the chances of winning customers first.
PS: If you need help navigating this 200+ services list, our latest webinar can guide you!
3. Improved agility
Hosting your tool on the AWS cloud allows you to access a sea of interconnected systems. This means you can bring your software’s architecture to life faster. Also, by using managed services, you don’t need to worry about maintenance, security, or network. So, you can use that time to focus on developing solutions at a quick pace.
Plus, your team can innovate rapidly with AWS advanced technologies for developing, training, and deploying AI, ML, and IoT services.
4. Higher experimentation and innovation
Since you can pay as you go and scale in or out as needed, you can experiment without making a permanent financial commitment. For instance, if you ship an AI feature that doesn’t stick, you can simply kill the feature, scale back down, and experiment on something else.
How scalability works on the cloud vs on-premise
Imagine you’re a bank and recently offered new trading services that became popular among young customers. They’re now using your app thrice as much as they used to and your servers can’t support this increase in daily traffic. Your customers are experiencing outages and feel upset. This is happening because most of your operation runs on legacy systems, local servers, and mainframes due to strict security regulations.
Launching a new feature with on-premise servers can leave you with unused disk space or unhappy customers. That’s because even after planning the new traffic influx, scaling on-premise requires much more precision and previous hardware investment.
If you had a hybrid or fully cloud-based operation, especially on a cloud like AWS, you could simply launch the new functionality after setting auto-scaling parameters. If you connect your auto-scaling groups to Amazon CloudWatch to get notified of issues, you can deploy this new feature and move on with your day. Then, pay the bill at the end of the month and take the usage data to predict future demand.
Tips for designing a scalable infrastructure using AWS services
A scalable infrastructure combines multiple software components, services, and processes to handle data changes without affecting the UX, reliability, or performance. To design an infrastructure that grows with you, follow these tips:
1. Design it with expansion in mind. Choose scalable technology and write modular code that can be easily edited. For instance, use a serverless approach to reduce operational overhead in the future, like AWS Lambda. Using Amazon EC2 for managing your containerized application and setting up AWS Auto Scaling are also good practices.
2. Make it flexible and adaptable. You can do this by treating your infrastructure as code (IaC) and setting up automated CI/CD pipelines to identify bugs or errors quickly. This way, you can make changes as needed by simply tweaking the code. Use services like AWS CodePipeline or AWS CodeBuild & Code Deploy to build, test, deploy, and automate your CI/CD workflows.
3. Choose scalable database providers. Similar to the first tip but strictly directed to databases. For instance, if you’re designing a scalable database infrastructure, you may need to rethink the idea of using an SQL database for everything. It may solve current problems fast, but it’ll become a bottleneck if you need to add other services, like analytics, later. Try Amazon S3 to store data lakes or AWS Lake Formation to build and manage them. Use Amazon Redshift as your data warehouse.
4. Adopt a polyglot database architecture. Piggybacking on the previous point, choose the right type of database for each area of your architecture. Big companies like Bank of America and Netflix follow this approach. Mix multiple AWS services like Amazon DynamoDB, Aurora, and RDS.
5. Decide when and how to reduce technical debt. Embed refactoring processes into your development to continuously get rid of legacy code. This also limits the chances of technical debt turning into financial debt or causing performance issues. You can use AWS CodeGuru to get recommendations and improve your code quality or Amazon CloudWatch to monitor your infrastructure’s performance.
6. Opt for services that support horizontal scalability. Systems that rely mostly on this type of scalability tend to be more cost-efficient, flexible, and tolerant. That’s because by distributing your application across servers, you can increase or decrease workloads as needed without putting all of your systems at risk. For instance, if one server fails, it doesn’t mean that the whole application does. Also, by relying on multiple services, you avoid creating workload bottlenecks and offer better performance. We recommend using AWS services such as AWS Lambda, Amazon DynamoDB, Amazon SQS, or Amazon Kinesis.
7. Consider sharding when modeling your data. When building scalable applications with AWS, look for services like Amazon DynamoDB or Kinesis Data Streams that can store data in partitions or shards. These are two similar ways to manage big loads of information by breaking it into smaller units before distributing it across servers. For instance, in Amazon Kinesis Data Streams, you can decide how many shards you can stream.
8. See your architecture as an intertwined system. All parts of your architecture are tied to each other, like in a chain. Whenever you evaluate your architecture, you can’t see each aspect as an isolated service, even if you follow a federated schema and microservice architecture. “You might be using Lambdas to run your service, but if Lambda itself is calling the CloudWatch Logs API, there is a direct dependence there,” says Gustavo. So make sure one service isn’t limiting the scalability of the other.
Let’s see what a scalable architecture looks like with an example.
IMDb, the biggest online directory of TV shows, movies, and celebrities, was acquired by Amazon in 1998. A couple of years ago, it went through a re-architecture to become serverless using AWS Lambda.
IMDb had a monolith with REST API with hundreds of hosts on EC2 instances. It moved into a serverless federated schema with microservices architecture.
IMDb’s serverless architecture using Lambas. Source: AWS.
Common mistakes companies make when scaling their systems with AWS
One thing is for certain when scaling applications: You’ll make mistakes. The goal is to lower the impact of those errors.
To do so, pay attention to these common mistakes people make when scaling their systems on AWS so you can avoid them.
Leaving automation out of the picture
AWS allows you to set automation for provisioning, managing resources, and monitoring your infrastructure’s performance. Not setting up automated processes to provision the right resources, assign them properly, or detect how they perform can cause you to lose money or operate on an inefficient tool.
Designing without scalability in mind
This is very common in startups because they usually need to design an MVP fast to assess viability or present a seemingly robust solution to investors. When you rush the development process, you usually sacrifice quality or scalability.
If you’re short on resources, contact AWS Business Support to get extra hands on your design.
Optimizing for storage or networking, but not performance
Choosing to optimize your application for other reasons instead of server compute capabilities, you risk high latency and a poor customer experience. This makes it even more difficult to scale because you can’t guarantee a fast-loading experience for users.
Forgetting to manage and monitor costs
AWS is affordable because you pay for what you use, but if you don’t monitor your expenses and usage correctly, you risk getting a big bill.
Also, if you have software that’s not properly designed, you may be paying for system overload as some instances may be using unnecessary resources. Setting up budgets on AWS allows you to prevent this from happening. This also means turning off resources when you stop using them.
Avoiding setting up monitors
Failing to set up Amazon CloudWatch puts you at risk of overseeing potential performance, costs, or storage issues. This could lead to outages and unplanned downtime, which leads to customer dissatisfaction and remediation costs.
Lacking to protect your platform from security breaches
Set up multi-factor authentication (MFA) as you scale, especially if you’re an early-stage startup. This protects you from having unauthorized access to their systems. The remediation and reputational costs of data breaches are high and this could cause a severe dent in your budget.
Forgetting to consider warm-up time in your plans
Like cars during winter, your auto-scaling instances need to warm up before they’re ready to handle any input. Designing scalability plans without taking into account this time may leave you with increased costs and also poor performance.
Using AWS solutions to scale in the automotive industry
The automotive industry has rapidly evolved in the last 30 years. It moved from using software for robotics arms in manufacturing to building cars that are essentially software with a suspension system.
AWS has become a huge partner for automotive companies and designed specific AWS automotive solutions to:
Build autonomous and connected vehicles
Simplify manufacturing
Support supply chain provisioning and management
Improve the customer’s experience through digital solutions
Enable car engineering
Leverage data for predictive maintenance
Manage electric vehicle charging stations and fleets
Services like AWS IoT FleetWise or AWS IoT Device Management allow carmakers to manage connected vehicles and develop over-the-air (OTA) cloud-native data engineering solutions. You can also use general services like Amazon Athena, Amazon Kinesis, Amazon EC2, Amazon S3, and AWS Snowball for automotive development.
Want to make your smart automotive solutions come to life? Check out our data engineering services!
How NaNLABS can help you build scalable cloud-native data engineering solutions
Designing (or redesigning) a scalable cloud-native software architecture is one of the top data engineering challenges. It requires time, money, and expertise.
We know that most engineering teams are packed with work. There’s always a fire to put out or something to patch up before it explodes. You know that if your team is constantly in urgent mode, the best thing to do is find and fix the root cause of the issue (which tends to be the architecture). But just like a circular reference in Excel, the solution (redesigning the architecture) circles back to the problem (having no time to spare).
If you see yourself reflected in this scenario, that’s when we come in.
At NaNLABS, we’re a team of data engineers and AWS experts. We focus mostly on the automotive industry, but like good engineers, we’ve done it all. Here are some examples:
How we helped HyreCar migrate to a microservices architecture
HyreCar is a two-sided car-sharing marketplace where people can rent cars and use them to offer riding services in apps like Uber or Lyft. The platform was scaling, the company was using outdated technology, and the team couldn’t take on additional work.
Our experts started working with HyreCar as an augmented team to handle the transition from a monolith architecture to a microservices-oriented one. “[The biggest challenge was] transitioning from the old architecture to the new one, while still running the business normally, at a point in which HyreCar was scaling their operations,” says Martin Aliverti, Senior Full-Stack Engineer at NaNLABS.
We also introduced new technology like AWS Amplify to make deploying test environments easy. Both things happened successfully.
How we built a charge management system (CMS) for an electric vehicle (EV) charging solutions company
This client offers EV charging stations for fleet managers with medium to heavy-duty transports. They came to us because they needed to develop a cloud-native CMS so fleet operators could schedule charging time at their stations.
To do so, we designed a reservation system going beyond the CMS. This way, fleet managers could plan charge sessions in their company’s charging locations.
Since this client heavily relies on data, we built a data lake and data pipelines for real-time, and near-real-time ingestion, as well as batch analytics. We also built electric vehicle supply equipment (EVSE) simulators and conducted load tests to make predictions about the system performance in different scenarios.
The client is now able to conduct predictive analysis based on their IoT devices' data and use the same information to get alerted in case of malfunctions in real time.
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TL;DR: What to keep in mind when scaling with AWS services
Understanding AWS scalability is crucial for businesses looking to grow without affecting software performance. It allows you to prepare your systems to handle demand changes, either by scaling vertically or horizontally.
When choosing a cloud provider, AWS stands out for its extensive set of services that support both scalability and elasticity. With over 200 services, AWS offers purpose-built tools for various industries, including automotive.
AWS is the best cloud for scalability because it was created by Amazon to support its growth. AWS is also cost-efficient, allows you to develop faster solutions, offers improved agility, and increases the opportunities for experimentation.
To design a scalable infrastructure, use AWS services that support flexible, automated, and efficient growth. Watch out for common mistakes and design for scalability from the start, set automated monitoring processes, and take instance warm-up time in your scaling plans. With proper planning and the right tools, AWS can help you build a robust infrastructure that grows with your business.
If you need more hands on deck, hire NaNLABS to inject senior expertise into your team without having to hire and onboard new team members. Get in touch.
Frequently asked questions about AWS scalability
What is AWS scalability?
AWS scalability refers to the ability of an AWS-based infrastructure to handle changing workloads efficiently. Amazon allows businesses to increase or decrease resources, such as compute power, storage, or networking, to meet the demand without over- or under-provisioning.
How can you scale out with AWS?
Scaling out or horizontal scalability is about adding more instances or resources to your system to distribute the workload. It’s like buying extra computers to go side-by-side and handle your operations. You can scale out with AWS by using services that support this practice. Some include:
Amazon EC2 Auto Scaling
Elastic Load Balancing (ELB)
Amazon RDS
Aurora Read Replicas
AWS Lambda
Amazon S3
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