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Learn about the main types of databases available on AWS
Have you ever wondered how market giants like Amazon, Netflix, and Airbnb manage to process millions of data points in real time ? The answer lies in choosing the right database.
With digital transformation, cloud databases are emerging as a more efficient and scalable solution. This is where AWS (Amazon Web Services) , a world leader in cloud computing, comes in, offering a wide range of services that allow companies to abandon local physical infrastructure and focus on developing their applications .
Founded in 2006, AWS simplified access to cloud computing and is now a benchmark for businesses that need to scale operations quickly and securely. In a global cloud market context, which generated US$78.2 billion in the second quarter of 2024 , AWS currently leads this market with a 32% share , offering services that guarantee high availability, security, and automatic scalability for companies of all sizes.
With so many options available, choosing the ideal AWS service can seem like a challenge . To make this choice easier, throughout this article we will explore the main types of databases available on AWS, explaining how and when to use them , their pros and cons , and practical examples.
Enjoy your reading!
AWS offers a variety of databases designed for different purposes — from SQL databases for structured transactions to NoSQL solutions for unstructured and real-time data. One of the biggest attractions is that all options are fully managed , eliminating the need for complex maintenance.
Why do so many companies choose AWS solutions for data management? Let's explore this further below.
The importance of AWS in data management
AWS offers security, reliability, and flexibility in data management. Its databases are configured for high availability , with automatic replication and disaster recovery, eliminating the risk of downtime (unavailability of a system, service, or application), which can harm critical operations. Furthermore, AWS complies with global regulations such as GDPR, providing essential compliance for regulated industries.
In 2024, demand for cloud computing grew by 19% , with a large portion of companies directing their investments towards AI solutions and advanced analytics. Explaining its success, AWS responds to this demand with services that support large volumes of data and integration with AI applications.
Flexibility and scalability of AWS database services
AWS database solutions also offer horizontal and vertical scalability . This means that companies can automatically increase or decrease resources to meet demand. Furthermore, the pay-as-you-go allows companies to optimize costs by paying only for the resources they actually use.
Another key advantage is global replication , meaning the possibility of data being distributed across multiple regions, ensuring fast access and continuous availability.
Relational databases are linked to a standard language that manages and manipulates them, SQL ( Structured Query Language ).
These databases are widely used in systems that require accurate and consistent transactions . They organize data into interconnected tables, allowing for complex queries and ensuring data integrity and security financial, ERP, and CRM systems , where errors or inconsistencies are unacceptable .
On AWS, two services stand out in this type of database: Amazon RDS and Amazon Aurora . Check out more details below.
Amazon RDS
Amazon RDS ( Relational Database Service ) is a managed service that simplifies the use of traditional databases . It is designed to automate complex tasks such as backups , updates, and crash recovery, freeing up teams to focus on developing their applications.
- When to use it: It is suitable for financial systems, ERP, CRM, and e-commerce that require secure transactions and consistently reliable data.
- Key benefits (pros): support for various engines, including MySQL , PostgreSQL , Oracle , and SQL Server ; backup and fast recovery in case of failures; vertical scalability to handle peak demand.
- Main challenges (cons): high cost for large-scale applications and high constant load; not suitable for unstructured data (such as photos and videos) or projects that require horizontal scalability.
Amazon Aurora is an optimized version designed to offer superior performance and continuous availability . It is compatible with MySQL and PostgreSQL , but offers up to five times the performance at the same cost, plus automatic replication across multiple AWS regions.
- When to use it: It's ideal for digital banks, subscription platforms, and online learning systems that require high speed and constant stability.
- Key benefits (pros): up to 5x better performance than MySQL ; high availability with replication across multiple AWS regions; automatic scalability without downtime , ensuring business continuity.
- Main challenges (cons): complex configuration may be required for advanced applications; higher costs compared to conventional SQL databases.
The term "NoSQL" stands for Not Only SQL , indicating that these databases can support different forms of data organization, in addition to relational tables.
NoSQL databases are designed to work with large volumes of unstructured data that do not follow a fixed schema (such as user profiles, application logs, or IoT sensor data). Unlike SQL databases, which organize data into tables, NoSQL databases allow flexibility in data structure , supporting formats such as JSON documents and distributed collections.
On AWS, DynamoDB and DocumentDB are the most popular NoSQL solutions. Each is suited for specific use cases, as we will explore below.
Amazon DynamoDB
Amazon DynamoDB is optimized for high availability and low latency . It is widely used by applications that require fast operations and automatic scalability, such as e-commerce , online gaming, and IoT platforms. With built-in global replication, DynamoDB ensures that data is always available, regardless of the geographic location of users .
- e-commerce platforms , and gaming, where low latency and high global availability are essential.
- Key benefits (pros): automatic scalability and integrated global replication; minimal latency, ensuring real-time responses; serverless (when the service does not require direct server management), scaling as needed.
- Key challenges (cons): costs can increase rapidly with large volumes of data and frequent operations; it does not support complex transactions with the same efficiency as relational databases.
Amazon DocumentDB
Amazon DocumentDB MongoDB -compatible solution (a type of NoSQL database) designed to store and query JSON documents . It is ideal for applications that handle dynamic data and require flexibility in record structure (for example, social media platforms and content management systems).
- When to use it: It is suitable for social networks that need to store profiles and posts from millions of users in a flexible and scalable format; CMS ( Content Management Systems ) and platforms that work with dynamic JSON documents.
- Key benefits (pros): compatible with MongoDB , facilitating migration and integration with existing systems; flexible scalability and backups ; ideal for applications requiring semi-structured data.
Main challenges (cons): high costs compared to self-managed versions of MongoDB ; limitations in complex transactional queries.
In-memory databases are designed to store information directly in RAM , allowing for instant responses and minimal latency . They are widely used in applications that demand high performance , such as multiplayer streaming platforms , and payment systems, where every millisecond counts to ensure a satisfactory user experience.
On AWS, Amazon ElastiCache is the leading solution for in-memory databases, supporting two popular engines, Redis and Memcached . While Redis is ideal for more complex operations, such as list and queue manipulation, Memcached offers a lighter solution for caching . Keep reading for more details.
Amazon ElastiCache
Amazon ElastiCache is a fully managed in-memory database solution designed to reduce the load on primary databases and ensure real-time performance . It is useful for caching , message queues, and game
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- multiplayer games , where thousands of players interact simultaneously and every action needs to be processed in milliseconds to ensure a smooth experience; streaming and financial systems, which require real-time responses to deliver a satisfactory experience.
- Main benefits (pros): near-zero latency, storing data directly in memory; support for Redis and Memcached , widely used engines; horizontal scalability, allowing support for a large volume of simultaneous connections.
- Main challenges (cons): risk of data loss in case of failure (if not configured correctly); not recommended for persistent storage, as the data is volatile.
Graph databases are designed to store and analyze complex relationships between different types of data, which also need to be analyzed through the connections between them. Their graph-based structure facilitates navigation and the execution of queries involving multiple nodes and relationships, which would be difficult to do with traditional relational databases.
On AWS, Amazon Neptune is the leading solution for this type of database. It is designed for high-performance graph queries and is compatible with languages such as Gremlin and SPARQL , which are widely used in graph queries.
Amazon Neptune
Amazon Neptune is ideal for applications that need to analyze complex connections and relationships between data. It supports property graphs and RDF ( Resource Description Framework ) graphs, offering a powerful solution for cases requiring fast navigation between large, interconnected datasets.
- When to use it: It's ideal for social networks, knowledge platforms where relationship analysis is essential, and recommendation systems for streaming that need to suggest content based on interactions and preferences of logged-in users.
- Gremlin and SPARQL graph languages , facilitating its implementation; high performance for navigating and querying interconnected data; automatic management with backup and recovery.
- Main challenges (cons): steep learning curve when working with graph languages; can be complex to configure and optimize for specific queries.
Data warehouses are designed to store and process large volumes of historical data , enabling fast and complex analyses. Unlike operational databases, which are optimized for real-time transactional queries, data warehouses focus on analytical processing and are widely used to generate reports and strategic insights .
On AWS, Amazon Redshift is the leading service for this type of need, combining different data sources to deliver fast and deep insights. Check it out:
Amazon Redshift
Amazon Redshift is a fully managed cloud data warehouse efficiently process complex analytical queries advanced analytics in near real-time .
- When to use it: It is suitable for companies that need to consolidate large volumes of historical data for strategic reports and analyses, such as in BI ( Business Intelligence ), finance, and marketing .
- Key benefits (pros): high processing capacity for large volumes of data, with fast queries; easy integration with other AWS tools and BI platforms, such as Tableau and Power BI ; automatic scalability, adjusting resources according to demand.
- Main challenges (cons): costs can escalate rapidly in projects with large volumes of data; need for query optimization to ensure maximum performance.
Ledger is a type of immutable database, ensuring an auditable and secure trail for transactions , essential for compliance and traceability.
On AWS, Amazon QLDB is the leading solution for this type of application, allowing you to accurately verify all changes and transactions made in the database. Learn more below:
Amazon QLDB
Amazon QLDB (Quantum Ledger Database) provides a complete and immutable history of every change made to the data, with a cryptographic trail for validation. It is suitable for applications that require a reliable and permanent record blockchain infrastructure .
- When to use it: It is ideal for financial institutions and systems that need to maintain complete and immutable transaction records for auditing and regulatory compliance, ensuring data trust and security; regulatory compliance and supply chains, where it is essential to ensure a reliable and verifiable trail of all transactions.
- Key benefits (pros): guaranteed immutability, with a complete record of all changes made; does not require blockchain , facilitating implementation;
cryptographic verifiability, ensuring the integrity of the recorded data.
- Main challenges (cons): inferior performance for complex queries compared to other types of databases; costs can increase with large volumes of immutable data stored over time.
Time series databases are designed to store and analyze data that changes over time , such as sensor readings, performance metrics, and IoT data. These databases allow for fast and accurate queries of data organized by timestamps, making them ideal for continuous monitoring and predictive analytics .
On AWS, Amazon Timestream is the leading solution, ideal for handling large volumes of data that accumulate at high frequency.
Amazon Timestream
Amazon Timestream is designed to efficiently store and query time series data. It organizes data by time and allows businesses to perform real-time analytics, with a system that automatically compresses and archives data, reducing costs.
- When to use it: It is suitable for monitoring IoT sensors in a factory, where it is necessary to track metrics such as temperature, humidity, and vibrations in real time to prevent failures and ensure operational efficiency; monitoring infrastructure and analytical applications, where data needs to be analyzed in real time and organized by period.
- Key benefits (pros): optimized for large volumes of temporal data, with fast queries; automatic and hierarchical storage to reduce costs associated with historical data; native integration with AWS IoT Core and Amazon CloudWatch , facilitating real-time data analysis.
- Main challenges (cons): may require complex configuration for more elaborate queries; costs may increase if data is not properly archived or optimized.
- What type of data will be stored?
That's the starting point . If you work with structured data, which fits well into tables and requires high consistency, relational SQL databases, such as Amazon RDS or Amazon Aurora, are perfect for this scenario. Unstructured or semi-structured data, on the other hand, benefit better from NoSQL databases, such as Amazon DynamoDB or Amazon DocumentDB . - What kind of speed does the application need?
In applications that require fast responses, real-time performance is essential. In-memory databases, such as Amazon ElastiCache , store information directly in RAM, ensuring minimal latency. - What will the data volume be and how will it grow?
Consider the volume and the need for scalability . Projects that accumulate large volumes of historical data require solutions that facilitate advanced analytics and strategic reporting. Amazon Redshift , for example, supports complex analytics with direct integration to BI tools. On the other hand, if the project involves data that changes over time, Amazon Timestream is an efficient choice for organizing and querying temporal data. - How does the need for complete traceability and transparency work?
Applications that require traceability and security need an immutable trail , such as in financial systems and supply chains. Amazon QLDB offers exactly that: an auditable trail of all operations, with cryptographic security, without the need for a complex blockchain . - What is the demand for complex connections and relationships?
Complex connections and relationships require graph databases . If your application needs to analyze dynamic relationships, Amazon Neptune is the ideal solution. Its ability to navigate and query interconnected data in graphs ensures high performance and flexibility.
- What is the expected investment in these solutions?
Costs and maintenance should always be considered . All AWS databases are managed, eliminating the need for manual maintenance. However, costs can vary depending on the type of service and data volume. Services like Amazon DynamoDB , which follow the serverless , scale automatically without intervention, but it's important to monitor their usage to avoid surprises.
Integrating and choosing the right databases can seem challenging, especially with so many options and variables. This is where a strategic approach makes all the difference.
At Skyone, we help companies navigate this complexity and leverage the full potential of AWS with tailored solutions . Our focus is on simplifying data management, offering ongoing support and an implementation that meets the specific needs of each client.
With expertise in cloud and automation , we ensure that every organization has access to the best technologies without wasting time or increasing operational complexity. Whether scaling a digital business or integrating corporate systems, we are ready to guide your company through every step of the digital transformation .
The decision about which database to use on AWS is not just technical, but also strategic . As we have seen, each service addresses specific needs: SQL and NoSQL for different types of data; in-memory databases for instant responses; data warehouses for in-depth analysis; and other formats geared towards more specific applications, such as graph and time-series databases.
More than just knowing the options, it's essential to understand what makes sense for your project now and in the future . The most robust solution isn't always the best choice. The idea is to align technology and needs to ensure agile, efficient operations that are ready to scale.
| Databases | AWS service | When to use | Benefits | Challenges |
| Relational (SQL) | Amazon RDS, Amazon Aurora | Financial systems, ERPs, CRMs | High consistency and transaction support | High costs in large volumes |
| Non-relational (NoSQL) | Amazon DynamoDB, Amazon DocumentDB | IoT, e-commerce , social networks | Automatic scalability and low latency | Costs increase with intensive use |
| In memory | Amazon ElastiCache | Games, streaming , financial systems | Minimum latency, horizontal scalability, and integration with Redis and Memcached. | Risk of data loss without proper configuration |
| Graph storage | Amazon Neptune | Social platforms, recommendation systems | Fast navigation through interconnected data | Steep learning curve |
| Data warehouses | Amazon Redshift | Business intelligence, financial reporting, and marketing. | Integration with BI and high processing capacity | The need for optimization for high performance |
| Ledger (immutable records) | Amazon QLDB | Regulatory compliance and auditing | Cryptographic immutability and verifiability | Limited performance for complex queries |
| Time Series | Amazon Timestream | Sensor monitoring, IoT | Optimized for temporal data and integration with AWS IoT | Complex configuration for advanced queries |
Thinking long-term, the true value of AWS lies in its ability to evolve alongside business demands. The landscape changes, projects grow, and solutions need to keep pace. Therefore, choosing the right database today can be the difference between stagnation and progress tomorrow .
Instead of choosing an option based on fame or trendiness, the focus should be on how technology can solve real problems and bring efficiency to each stage of the operation . It is this alignment between need and solution that allows companies to exceed expectations and prepare for what lies ahead.
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