Smart Scaling: Aligning Database Infrastructure with Business Growth

Unlock the full potential of your business with scalable database infrastructure. Set up systems that grow with ease alongside your customer base and market demands.

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by Matias Emiliano Alvarez Duran


Sales are booming and you’re expanding your market reach, but what happens when your infrastructure can't keep up with data demands? If you're finding that your current setup is more of a bottleneck than a baseline for growth, it's time to explore more scalable database infrastructure options.

Using our extensive experience in data engineering, this article will guide you through selecting the right strategies and technologies for a seamless expansion.

Table of contents

Is your infrastructure slowing down as your business speeds up?

Shift gears with scalable solutions that keep you on track. Explore our Data Engineering Services and accelerate your growth. 

The real impact of an underperforming database 

What happens if you don’t set up your infrastructure for scalability? Can you keep muddling through without addressing data engineering for your business? Not for long. An underperforming database can severely cripple your business's operational efficiency and economic viability.

In the short term:

When your database isn’t up to speed, the first fallout is operational: services lag or stop altogether, frustrating users and eroding trust. This operational inefficiency quickly translates into customer dissatisfaction and increased complaints, prompting your customers to look elsewhere for their needs. Issues include:

  • Slow response times: As demand increases, the first sign of trouble is often services running slower than usual, which can frustrate users and diminish their experience.

  • Frequent downtime: Overloaded systems may crash under pressure, leading to unacceptable downtime and lost productivity.

  • High customer churn: Dissatisfied customers may quickly turn to competitors if they encounter persistent performance issues.

  • Increased complaints: A rise in customer complaints often correlates with database problems, impacting your brand reputation adversely.

  • Lower user retention: If your platform can't handle increased loads, even loyal customers might begin looking elsewhere.

In the long term:

Infrastructure scalability issues ultimately put the viability of your business at risk, as inefficient data handling leads to slower decision-making, putting you a step behind more agile competitors, and turning off potential investors or partners. A few of the bigger problems you’re likely to face are:

  • Plummeting LTV (Lifetime Value): Customer attrition reduces the lifetime value of your customer base, directly affecting your bottom line.

  • Rising CAC (Customer Acquisition Cost): To fill the gap left by departing customers, spending on marketing and promotion intensifies, inflating your CAC.

  • Worsened CAC to LTV ratio: A higher CAC coupled with a lower LTV creates a vicious cycle of diminishing returns, making your business less profitable over time.

  • Reduced market competitiveness: Slow decision-making and inability to scale efficiently can leave you behind in a fast-paced market.

  • Risk to future investments: Potential partners and investors are less likely to engage with a company that faces fundamental operational challenges.

5 common database performance issues 

As your business grows, so does the strain on your database infrastructure. Many businesses find themselves grappling with performance issues that slow down operations and lead to a serious stall in business growth.

Here's a look at common factors that can bog down your database performance, complete with NaNLABS' tried-and-tested advice on how to stop these problems in their tracks.

1. Inefficient query access patterns

Poorly designed queries can overuse resources, slow down operations, and cause bottlenecks, especially as data and query complexity grow.

NaNLABS recommendation

Optimize queries and implement caching: Refine query structures for efficiency, add appropriate indexes for performance, and implement caching to speed up data retrieval for frequently accessed information.

2. Data classification and compression

Incorrect data classification and poor compression strategies can lead to inefficiencies, where critical data isn't processed timely, and storage costs skyrocket due to poorly optimized space usage.

NaNLABS recommendation

Classify data and use compression: Tailor your data processing based on the needed speed of access. Only process critical, real-time data in high-speed analytics pipelines and use data compression to balance processing speed with storage efficiency. This helps in optimizing performance and reducing costs while ensuring the right data is available at the right time.

3. Lack of proper indexing

Inadequate indexing can cause slow data retrieval times, which delays queries and impacts user experience.

NaNLABS recommendation

Comprehensive indexing strategy: Ensure critical data fields are indexed and regularly review and maintain these indexes to optimize performance.

4. Data volume and growth

Massive data growth can overwhelm your database or data pipeline if not managed properly, leading to slow response times and increased load time - as this e-learning platform quickly found out.

NaNLABS recommendation

Implement data partitioning or sharding: Manage large data volumes more effectively by dividing the database into smaller, more manageable segments, or by distributing data across multiple servers.

5. Database limitations

An outdated database can significantly hinder database performance, leading to bottlenecks that no software tuning can resolve.

NaNLABS recommendation

Upgrade or migrate to scalable cloud solutions: Enhance your server capabilities or utilize scalable cloud services to easily expand resources as needed.

To tackle these database performance issues, NaNLABS frequently recommends a mix of advanced tools and technologies. Each of these services offers some form of horizontal scalability as well as reducing the operational burden on those systems.

  • Amazon DynamoDB and DocumentDB: For their fully managed, scalable NoSQL solutions that handle large volumes of distributed data.

  • Apache Spark and AWS Glue: For big data processing capabilities that can efficiently process vast amounts of data in real-time.

  • Apache Kafka and Amazon Kinesis: For real-time data streaming capabilities that ensure timely data processing and analysis.

  • AWS Lambda: For managing data processing automatically and scaling without managing servers.

Case study: scaling a sales enablement solution

In 2021, Equinix faced challenges with their internal sales enablement tool used globally by their sales teams. The platform, essential for managing extensive digital infrastructure services, was plagued by instability and critical bugs due to its complex legacy code and outdated database system (Neo4J).

Recognizing the need for a robust solution that could scale efficiently, Equinix partnered with NaNLABS for a strategic overhaul. NaNLABS’ approach focused on migrating the platform to MongoDB, a more standardized technology that promised greater stability and scalability, leading to reduced bugs and an increase in sales team productivity - all with minimal downtime over 6 months.

Scaling your infrastructure for sustainable growth 

Scaling your database infrastructure the right way is key—not just to keep things running smoothly as your business grows, but also to dodge expensive overhauls later on. At NaNLABS, we’re all about creating scalable systems that can expand right along with your company. Here’s our take on tackling this task.

Understand the importance of scaling infrastructure the right way

Infrastructure isn’t just a one-time setup, it needs to grow and evolve as your business does. Initially, it’s tailored to meet today’s needs and anticipate the near future. But as your business expands—entering new markets, launching products, or merging with other companies—your infrastructure needs to adapt quickly.

Take note: It’s equally important not to overbuild with every possible future in mind, which can be as complex and limitless as building the internet itself! Instead, focus on creating a flexible and scalable system from the start. Good infrastructure management supports your business but also enables it to thrive through changes, saving you from future headaches.

Select your database for scalability

Selecting the appropriate database technology affects both current performance and future scalability. Whether it's SQL for complex transactions or NoSQL for scalability and flexibility, the choice should align with your data structures, access patterns, and growth projections.

  • SQL vs. NoSQL: Understand the differences and use cases for SQL and NoSQL databases. SQL databases are ideal for complex queries and transactional integrity, while NoSQL databases offer flexibility and scale more easily with large sets of distributed data.

  • Hybrid Approaches: Consider hybrid models that utilize the strengths of both SQL and NoSQL databases to meet diverse data needs effectively.

NaNLABS leverages battle-tested solutions like Amazon DynamoDB for distributed data loads, ensuring that your database can handle scaling challenges effectively.

Choose the optimal moment to tackle technical debt

One major decision you'll face is when to address technical debt. The way we see it, you have three options:

  1. Start with perfect planning: From defining query access patterns to selecting the appropriate database and architecting the data pipeline, this approach involves thorough upfront planning. While it introduces more initial overheads, it sets you up for much smoother scalability as your business grows.

  2. Begin with basics and adapt: Alternatively, you could start with the familiar technologies and processes your team already knows and then make adjustments as the need arises. This path might seem easier at first due to its simplicity and speed in deployment but often accumulates significant technical debt as patches and workarounds pile up when scaling challenges appear.

  3. Leverage expertise from the start: The third option—and where NaNLABS shines—is to incorporate data engineering services right from the beginning. Choosing NaNLABS means partnering with a team that brings years of experience in building robust, scalable infrastructures across various industries. It means proactively preparing for future needs while keeping the setup efficient, adaptable, and optimized for future success.

Take care of your architecture, and it will take care of you.

At NaNLABS, we’re big on making sure your infrastructure evolves with your business. By keeping up with emerging tech, we build resilient setups that handle today’s demands and are ready for tomorrow's opportunities. Think of smart architecture less as an IT asset and more as a business strategy that safeguards your growth and paves the way for future success.

With NaNLABS as your development sidekick, let’s scale your infrastructure right—the first time.

Is your infrastructure slowing down as your business speeds up?

Shift gears with scalable solutions that keep you on track. Explore our Data Engineering Services and accelerate your growth.

Frequently Asked Questions About Scalable Database Infrastructure

  • Which database is best for scaling?

    The best database for scaling often depends on your specific needs; however, databases like Amazon DynamoDB for NoSQL options and PostgreSQL for SQL are renowned for their scalability and robust performance under heavy loads.

  • How do I choose the right database?

    Choosing the right database involves evaluating your data structure, expected load, and specific requirements such as transaction consistency, speed, and how much your data will grow. Tools like Amazon DynamoDB, MongoDB, and Cassandra are great for different types of scalability needs.

  • How to scale a database?

    To scale a database effectively, consider implementing data partitioning, using a load balancer, upgrading to more powerful hardware, or switching to a scalable database platform like Amazon DynamoDB which supports automatic scaling.

  • What is the best way to scale your organization's infrastructure?

    The best way to scale your organization's infrastructure is to start with scalable architecture from the beginning and choose the right mix of technology—utilizing scalable databases, cloud solutions, and ensuring that your setup is flexible enough to incorporate changes as your business grows.

  • How do you make a database scalable?

    Making a database scalable can involve several strategies such as adding more indexes, optimizing queries, implementing sharding, considering NoSQL databases if high write and read speeds are needed, and using caching mechanisms to reduce load on the database.

  • What is infrastructure scaling?

    Infrastructure scaling involves expanding the capacity of your backend systems to handle increased loads smoothly. This can be done vertically by adding more resources like CPU or memory to existing servers, or horizontally by adding more servers to distribute the load.

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