How to choose the best AWS database for my application?

Data is the heart of any application , and how it is stored and accessed can determine the success or limitations of a project . Today, with digital transformation accelerating, choosing the database has become more important than ever, especially with the explosion in data volume and the need for data integration across different areas of the company. .

According to global consultancy IDC , global spending on public cloud services is projected to reach $805 billion in 2024 and to double by 2028 , driven by growing demand for artificial intelligence and learning , which require flexible and scalable data infrastructures. This growth reflects the increasing need for solutions that keep up with the pace of innovation and enable the efficient management of large volumes of decentralized data .

In this scenario, cloud databases, such as those offered by AWS ( Amazon Web Services , in Portuguese, Amazon Web Services), have become the ideal choice for companies looking for scalability, flexibility and performance . AWS offers a variety of database solutions, from simple storage to robust data analysis tools, meeting different needs and application types.

But, faced with so many options, common questions arise: how to choose the best AWS database for my application? What factors to consider to ensure that the choice will be the most appropriate for the project?

This article was designed to answer these questions, helping you understand the main AWS database options, their differences and which one can best align with the demands of your project . Whether for a small, medium or large application, here you will find guidance to make a safe and strategic choice.

Good reading!

Understanding the different types of databases on AWS

AWS offers a range of databases designed to meet different types of applications and requirements . Below, we present the main categories of databases available on AWS and their main characteristics, providing an overview of the options so that you know the range of choices before deciding which is ideal for your application.

Relational databases ( Amazon RDS, Amazon Aurora )

Relational databases store data in a structured way, in tables with rows and columns, and use SQL ( Structured Query Language , in Portuguese, Structured Query Language) for queries and operations.

• Amazon RDS : It is a managed relational database service that supports multiple platforms such as MySQL , PostgreSQL , Oracle , MariaDB and SQL Server . It automates complex administrative tasks like backups software updates .
  
• Amazon Aurora : is a high-performance relational database, compatible with MySQL and PostgreSQL . Developed to offer greater speed and availability, Aurora is a solution optimized by AWS that combines performance with scalability.

Non-relational databases ( Amazon DynamoDB, Amazon DocumentDB )

Non-relational databases or NoSQL ( Not Only Structured Query Language ), are designed to deal with large volumes of data that can be semi-structured or unstructured. They are highly scalable and flexible.

• Amazon DynamoDB : It is a managed NoSQL database that offers millisecond latency at any scale. It is known for its ability to automatically and quickly scale, making it ideal for scenarios that require intensive reading and writing.
  
• Amazon DocumentDB : is a managed database compatible with MongoDB , ideal for storing data in JSON document format. It makes working with semi-structured data easy and is highly scalable.

In-Memory Databases ( Amazon ElastiCache )

In-memory databases are used for storing temporary data that needs quick access. They store data in RAM, which provides extremely high read and write speed.

• Amazon ElastiCache caching service compatible with Redis and Memcached , two popular data caching solutions. It is ideal for improving the performance of applications that perform frequent queries.

Data warehouses ( Amazon Redshift )

Data warehouses are designed to store and process large volumes of historical data, enabling advanced querying and analysis.

• Amazon Redshift : is a data warehouse that supports complex SQL queries and easily integrates with BI ( Business Intelligence ) tools. Redshift scalable and enables fast analysis on large data sets.

ledger databases ( Amazon Neptune, Amazon QLDB )

These databases are specialized in scenarios where there is a need for analysis of complex networks or a reliable audit trail, which consists of a detailed record of all changes made to the database, preserving the complete history of transactions.

• Amazon Neptune : is a graph database that makes it easy to build and query complex networks of data. It supports major models and is optimized for graph queries.
  
• Amazon QLDB (Quantum Ledger Database) ledger database that offers an immutable and verifiable audit trail. This ensures transparency, traceability and trust, as each action can be reviewed and confirmed without the possibility of retroactive changes. This functionality is especially useful in applications that require regulatory compliance or where it is essential to prove the authenticity and integrity of data, such as in financial systems or supply chains.

With so many options and features, choosing the most suitable AWS database can seem complex. After all, choosing the right database not only depends on the type of data your application needs to manage, but also on performance, scalability, cost and data integration requirements .

In the next section, we'll explore the essential factors to consider when choosing an AWS database to ensure you make a your application's specific needs

Factors to consider when choosing the AWS database for your application

Choosing the ideal AWS database for your application involves a detailed analysis of several factors that directly impact performance, scalability and data integration in the project . Below, we explain the main factors that influence this decision, helping you understand how each aspect affects the functionality and data management of your application.

Data type: structured, semi-structured or unstructured

The first step in choosing a database is to understand the type of data your application manages. This is one of the most important factors, as it directly influences the structure of the database and the way it is stored .

• Structured data: data organized in a rigid manner, such as tables with rows and columns. This format facilitates query and analysis, as it follows a predefined schema and is easily accessed via SQL.
  Examples : customer registrations and information (names, addresses, telephone numbers), financial records, e-commerce .

• Semi-structured data: this data has a flexible organization that allows some level of structure, but without the rigidity of tables. These are generally stored in formats such as JSON or XML, offering greater flexibility.
  Examples : user profiles, product catalogs, application
  logs
• Unstructured data: data that does not follow a schema or have a specific format, making it more complex to organize and process.
  Examples : images, videos, audio files, text documents, detailed reports.

Each type of data requires a specific approach to its storage and processing, which directly affects the choice of database .

Scalability and performance needs

Database scalability and performance are essential for applications that need to deal with large volumes of users and data . These features ensure that the system is able to grow efficiently, adapting to peaks in demand and maintaining stable performance. Understand more about the different types of scalability and how they influence database behavior:
  

• Horizontal and vertical scalability: in horizontal scalability, new servers are added to divide the load between multiple instances, while in vertical scalability the capacity of a single server is increased. Both approaches have a direct impact on infrastructure and how the database adjusts to application growth.
  
• Automatic scaling: Some AWS databases offer automatic scaling capabilities, adjusting processing power and storage according to current demand. This helps maintain performance even during request spikes.
  
• Performance for high-demand applications: applications that deal with a large number of simultaneous accesses, such as streaming platforms or financial systems, benefit from databases that support a high volume of transactions without compromising speed and performance.

These scalability and performance needs influence application configuration and impact how the database handles workload growth and demands.

Latency and response time required for your application

Latency is the time it takes the database to respond to a request. This factor is important in determining efficiency and user experience in different types of applications.

• Low latency: for applications that need fast responses, such as recommendation systems and online games, low latency is essential. The ability to store data in RAM allows you to reduce response time and guarantee immediate access to information.
  
• Tolerable high latency: For some applications, such as analyzing historical data or reporting, slightly higher latency is acceptable. These applications prioritize the ability to perform complex queries over the speed of immediate response.

The level of latency that the application can tolerate helps define the most appropriate type of database , depending on how quickly it is necessary to access and process the data.

Cost and budget available for the project

Cost is a key factor, especially in long-term projects . The pricing model for each database may vary, which influences the budget required for its implementation and maintenance.

• Usage-based pricing models: Some AWS databases use a “ pay-as-you-go ” pricing model in which the cost is calculated based on usage. This model is interesting for companies that prefer to pay for actual usage, avoiding high fixed costs.
  
• Cost for high volumes of data: Solutions that process large volumes of data, such as data warehouses and in-memory databases, may have a higher cost as they require robust infrastructure to support complex data processing and analysis.

Understanding the pricing model and available budget allows the project to be financially adjusted to the needs and scale of the application .

Compatibility and integration with other AWS tools

Compatibility with other AWS tools and ease of data integration are essential for the database to work cohesively within the application infrastructure .

• Integration with BI and machine learning : Some AWS databases that support these tools allow direct integration for data analysis and learning. This capability makes it easy to create reports and train machine learning directly on data.
  
• Support for APIs and protocols: Compatibility with specific APIs and protocols facilitates communication between systems and the use of data in different contexts. This compatibility makes the database more flexible and allows it to be used with different tools within the AWS architecture.

The ability to integrate and compatibility with additional tools allows you to create an efficient data flow, promoting data integration and optimizing overall application performance .

Each of these factors is decisive in defining the ideal AWS database for your application. Understanding the characteristics of the data, the scalability needs, the importance of latency, the available budget and the integration of data with other tools helps to configure the solution that is most aligned with the objectives and demands of the project .

Let's now explore when to choose each of these databases, analyzing the characteristics of each one and how these options influence the structure and functioning of your application?

Choosing the database for your application

Selecting the most appropriate AWS database for your application requires a deep understanding of the specific needs of each project . AWS offers a variety of database options, from structured, highly consistent solutions to flexible, scalable databases designed to handle dynamic data.

When choosing the database , it is important to consider factors such as the type of data, the need for scalability, the level of data integration between systems and the priority in terms of latency and access speed . Each type of application, from financial systems to social media platforms and real-time data analysis, requires a different approach to meet performance, security and efficiency expectations.

Below, we explore the main usage scenarios and how each type of AWS database can meet the specific needs of your application, helping you identify the solution that best aligns with your goals.

When to opt for a relational database (SQL)?

For many applications, a relational database can be the ideal choice when the priority is to maintain an organized and consistent structure . In the AWS ecosystem, solutions such as Amazon RDS and Amazon Aurora offer reliability and robustness for applications that require high precision and integration between systems.

Below, we explore key scenarios to help you determine whether a relational database is best for your application.

• Financial and banking applications: if your application involves financial transactions, such as payment systems, accounting records or bank transactions, a relational database can guarantee the necessary integrity and accuracy. Data integration between financial and operational systems allows all areas to access centralized information, which is essential for audits and regulatory compliance.
  
• Business management systems: ERP and CRM solutions deal with large volumes of data, such as customer information, order history and inventories. The ability of relational databases to perform complex queries and generate detailed reports is essential to centralize and manage this information effectively, promoting data integration between departments and systems within the company.
  
• E-commerce online transaction platforms : in them, data reliability and accuracy are fundamental for inventory management, order processing and customer history control. With relational databases, it is possible to maintain an organized data structure, ensuring the integrity and consistency of information. This organization also facilitates data integration between platform modules, such as finance, service and logistics.
  
• Applications that require compliance and auditing: in regulated sectors, such as healthcare, finance and insurance, where it is essential to maintain accurate and auditable records, relational databases offer the necessary structure and reliability. Data integration between operational and regulatory systems ensures that information is always available for verification, meeting compliance and auditing requirements.

Applications that need tight controls and reliable integrations benefit from relational databases . However, some modern applications demand flexibility to deal with less structured data and in massive volumes, requiring an alternative that has greater adaptability.

When to opt for a non-relational database (NoSQL)?

For applications that demand flexibility in data structure and the ability to manage large volumes of dynamic information , non-relational databases present themselves as an excellent alternative. With an adaptable structure that deviates from rigid schemas, AWS options such as Amazon DynamoDB and Amazon DocumentDB offer scalability and the ability to handle varied data.

Below, we explore the main scenarios to help you identify whether a NoSQL database is best suited for your AWS application.

• Applications with variable data structure: in projects that deal with flexible and constantly changing data, such as user profiles, preferences and activity records, NoSQL databases offer the necessary adaptability. Social media applications, for example, benefit from this dynamic structure, as they store user data, posts and interactions, which can change format over time. This type of database facilitates the expansion of attributes or modification of structure, without the need to reorganize the schema, also promoting simplified data integration between different types of information.
  
• Need for rapid and flexible scalability: If your application faces peaks in usage or is experiencing rapid growth, such as online or recommender systems for products, content or services, the horizontal scalability offered by AWS NoSQL databases allows you to expand storage capacity and data processing without compromising performance. Flexibility in data management and data integration between modules (which can include user preferences, interaction history and recommendations) ensure that the application supports an increasing number of simultaneous accesses.
  
• Semi-structured and unstructured data: Many modern applications process semi-structured (like JSON) or unstructured (like images and videos) data. In these cases, NoSQL databases, which do not require rigid schemas, are ideal. This format is particularly useful for product catalogs or large-volume media storage where data is varied and requires easy updating. Data integration in this context allows you to combine data from different sources and formats in a flexible environment, accessible from different application modules.
  
• Real-time data analysis and processing: in IoT (Internet of Things) and big data , where sensors and devices constantly generate data, real-time storage and processing capacity is essential. An AWS NoSQL database meets this demand, enabling rapid analysis and management of large amounts of event and log data. This structure allows data integration between devices and analysis systems, providing a centralized and updated view of the data generated.

When considering how to choose the best AWS database for your application, NoSQL stands out for its flexibility and adaptability in scenarios where data and demand vary quickly . However, some applications require ultra-fast access and require storage that provides responses in milliseconds.

When to opt for an in-memory database?

For applications that require ultra-fast responses and frequent data access , in-memory databases offer an ideal solution. By storing data directly in RAM instead of disks, these databases minimize response time and are especially effective for applications that need low latency and high availability. As we've seen, AWS offers robust options, such as Amazon ElastiCache , with support for Redis and Memcached , which are widely used in scenarios where out-of-the-box performance is essential.

Below, we explore scenarios where an in-memory database is the best choice for your AWS application.

• Applications that require caching for quick responses: for applications that perform frequent queries on the same data, such as recommendation systems and e-commerce , using an in-memory database as a cache can speed up responses, ensuring a more efficient experience. fluid to the user. This allows you to create a cache of frequently accessed data, which reduces the load on the main database and allows for millisecond response times. Furthermore, this data integration ensures that information is always up to date and consistent.
  
• User Session and Authentication Applications: For applications that handle real-time user sessions and authentication, an in-memory database can store temporary information such as tokens and login states, ensuring that session data is quickly accessed and updated. This is particularly useful on single sign-on (SSO) platforms and high-traffic websites where fast, seamless data integration is essential to maintaining security and the user experience.
  
• Real-time games and applications: In online and other real-time applications, the in-memory database helps quickly process dynamic information such as scores, game states, and interactions between users. Thus, this information is stored and accessed instantly, improving the player's experience. Data integration between the in-memory bank and game analysis systems also allows for real-time monitoring and insights into your performance and user behavior.
  
• Real-time analysis and application monitoring: in fraud detection or performance monitoring systems, an in-memory database allows immediate processing of large volumes of data. This way, you can keep critical metrics and events in cache , ensuring they are readily accessible for immediate analysis and response. Data integration between in-memory database and monitoring systems facilitates centralized visualization and quick decision-making based on up-to-date data.

For applications that require instant responses and frequent data access, in-memory databases offer a high-performance, low-latency solution . However, if your application requires the storage and processing of large volumes of historical data, where detailed analysis and generation of insights are more important than speed of immediate response, another approach may be more appropriate.

When to opt for a data warehouse ?

For applications that need detailed analysis and process large volumes of historical data , a data warehouse is the ideal choice. Data warehouses are designed to collect, organize, and analyze data from multiple sources, enabling advanced reporting. In the AWS ecosystem, Amazon Redshift provides a robust infrastructure for storing and querying large-scale data, easily integrating with BI and analytics tools.

Below, we explore the scenarios in which a data warehouse is best suited for your application.

• BI and data analytics applications: Companies that rely on insights to guide strategic decisions benefit from data warehouses . With its ability to process large volumes of data and run complex queries, this option allows Business Intelligence to access centralized information to create dashboards and analytical reports. Data integration between Redshift and other company data sources, such as CRM and ERP systems, provides a complete view of operations and allows for deeper analysis.
  
• Financial reporting and analysis: In financial applications such as expense tracking, revenue, investment analysis and compliance, data accuracy and the ability to generate complex reports are essential. A data warehouse allows you to centralize and organize financial data from multiple sources, making it easier to create detailed reports. Data integration between the data warehouse and the company's financial systems ensures that all reports and analyzes are based on up-to-date and consistent information.
  
• User behavior analysis in applications and e-commerce e-commerce companies and other applications that monitor user behavior, a data warehouse allows you to consolidate browsing, purchasing and interaction data in one place. This allows you to process and query this data to understand behavior patterns, identify trends and optimize the customer experience. Integrating data with marketing and customer service systems allows the team to develop strategies that are more aligned with user preferences.
  
• Storing and analyzing historical data at scale: Applications that store large volumes of historical data, such as sensor data, logs or social media activity records, need a data warehouse to process and analyze this information. They are designed to handle large data sets, enabling historical analysis and generating insights . Data integration between it and monitoring and analysis systems allows for a consolidated and organized view of data trends and history.

When your application requires robust storage and the ability to generate detailed insights from large volumes of data, a data warehouse is the ideal solution. These solutions offer the scalability and performance necessary to meet these demands, allowing your company to perform advanced analyzes and make strategic decisions based on data.

ledger database ?

For applications that need transaction tracking or complex network analysis ledger databases offer specialized structures for these scenarios. In the AWS environment, Amazon Neptune and Amazon QLDB are designed to meet these specific demands, providing efficiency in analyzing complex relationships and immutable records. Below, we discuss the main scenarios in which these types of databases are suitable.

• Financial and accounting records applications: In financial systems, such as transaction records and audits, data accuracy and integrity are critical. ledger database ensures that all transactions are immutable and can be verified with complete transparency, providing a reliable and secure history. Data integration between accounting and auditing systems facilitates centralized and secure management of these records.
  
• Supply chain management : in supply chain ledger database allows you to track each step in a reliable and immutable way. This way, it is possible to store the complete product movement history, ensuring that all parties involved have access to authentic and verifiable information.
  
• ledger database provides a clear audit trail. This feature is useful for maintaining regulatory compliance as it allows you to query historical data without the possibility of retroactive changes.

For applications that require the analysis of complex networks of data or reliable audit trails ledger databases are ideal solutions. They offer the specialized structure to meet these scenarios, ensuring the consistency and integrity of information .

In the next section, we will discuss how to choose an database based on application size and growth, covering the importance of considering data volume and future needs when selecting the most suitable solution for your application development.

Defining the database based on application size and growth

As your application evolves, you need to ask yourself whether the database you choose today will be able to keep up with your company's growth . Thinking about growth and future needs is essential to defining a solution that supports expansion without compromising performance. With a variety of scalable options, AWS offers databases capable of serving everything from early-stage projects to applications with large volumes of data.

Each application has its own pace and complexity , but how to determine which option will best accompany this growth? In this section, we explore how different AWS databases adapt to ongoing application development, considering scalability and efficient data integration between systems as a key factor for long-term success.

Factors to consider in application growth

When choosing an AWS database, here are the key factors that will help ensure the solution supports application development over time:

• Current data volume and growth projections: The starting point is to understand the current size of the database and estimates of its growth. For applications that handle a lower initial volume of data, relational databases like Amazon RDS can meet your needs with vertical scalability (increasing server capacity). On the other hand, for applications that need to grow quickly and constantly, such as social networks or streaming , solutions that offer horizontal scalability, such as Amazon DynamoDB , allow capacity to be adjusted by adding more servers, serving larger volumes of data and users without losing performance.
  
• Automatic scalability: applications that face peaks in demand, such as e-commerce stores during large sales events, benefit from databases that automatically adjust their capacity. Amazon Aurora and Amazon DynamoDB have automatic scaling features that guarantee constant performance without the need for manual interventions. This is essential for applications that need continuous and uninterrupted data integration between modules, even during periods of high demand.
  
• Need for real-time vs. real-time data storage and processing Historical data: Some applications prioritize storing historical data for later analysis, while others need real-time data. In cases of large-scale historical storage, such as activity logs or sensor data, Amazon Redshift may be the best option as it offers massive storage and tools for complex analysis. For applications that require instant responses, such as gaming platforms and real-time monitoring systems, Amazon ElastiCache enables in-memory storage for fast access and minimal latency.
  
• Complexity in data integration between systems and applications: as the application grows, data integration between different systems becomes increasingly complex. For applications that rely on fluid, centralized integration, such as ERPs, a relational database like Amazon RDS offers a structured environment that facilitates data synchronization between systems. Applications that deal with dynamic data, such as in big data , can benefit from the flexibility of Amazon DynamoDB , which allows you to manage different types of data and easily integrate them with analysis tools.

Considering the current size and projected growth of the application is essential to define the most suitable AWS database. A choice that supports scalability over time will allow your application to grow without compromising performance and data consistency.

In the next section, we'll examine how Skyone can help companies with this process by providing expert support to choose and implement the AWS database solution that best aligns with your application's needs.

How does Skyone help companies choose and implement the ideal AWS database

At Skyone , we understand that choosing the right database can transform an application's ability to scale and generate value. Therefore, our work goes beyond a simple recommendation: we offer strategic consultancy that identifies the AWS database solution most aligned with the specific needs and growth potential of each of our clients .

We follow every stage of this journey , from initial analysis to implementation and ongoing support, ensuring that the chosen AWS database easily integrates with other existing systems and provides performance, security and flexibility .

We serve our customers in a personalized way , with solutions that adapt to the reality of each business. With our experience and commitment, we position ourselves as the ideal partner for companies looking for a scalable and efficient data infrastructure on AWS, offering tailored solutions and dedicated support .

Our main focus is to simplify complex technologies, increasing autonomy and productivity with modern solutions that keep pace with market growth and innovation.

Want to ensure your company is equipped with the best AWS database solution? Request a conversation with one of our experts!

Conclusion

As we have seen throughout this article, a well-chosen database is one of the pillars for the success and sustainable growth of an application . With the right AWS database solution, organizations can reach new levels of efficiency and prepare for the challenges of an ever-changing market, with benefits such as:

• Scalability and flexibility: an infrastructure that follows business growth and adapts to new demands, ensuring that the application is always ready to meet the increase in users and data.
  
• Simplified data integration: the possibility of integrating data between systems, promoting a centralized view that supports informed and strategic decision-making.
  
• Optimized performance: with low latency and high performance, it is possible to offer more agile and satisfying experiences to users in real time.

In short, choosing the right database can transform the way a company operates and connects with its data , providing the solid foundation needed to confidently advance digital transformation. However, it is a decision that requires careful analysis and alignment with the needs of each application.

Did you like this content and want to learn about how to protect and secure your cloud application environment? Check out our article on zero trust and information security.