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Have you ever wondered how market giants like Amazon, Netflix and Airbnb manage to process millions of data in real time ? The answer lies in choosing the right database.
With digital transformation, cloud databases emerge as a more efficient and scalable solution. AWS (Amazon Web Services) , the world leader in cloud computing, comes into play to abandon local physical infrastructure and focus on developing their applications .
Founded in 2006, AWS simplified access to cloud computing, and today is a reference for businesses that need to scale operations quickly and securely. In the context of the global cloud market, which generated US$78.2 billion in the second quarter of 2024 , AWS currently leads this market with a 32% share , with services that guarantee high availability, security and automatic scalability for cloud companies. all sizes.
With so many options available, it can seem challenging to choose the right AWS service . 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.
Good reading!
Overview of Databases on AWS
AWS offers a variety of databases designed for different purposes — from SQL databases for structured transactions to NoSQL solutions for unstructured, 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 next.
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. Additionally, AWS supports global regulations like GDPR, offering essential compliance for regulated industries.
In 2024, the demand for cloud computing grew 19% , with most 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 companies can automatically increase or decrease resources to meet demand. Additionally, the pay-per-use allows companies to optimize costs by only paying for the resources they actually use.
Another difference is global replication , that is, the possibility of data being distributed across several regions, ensuring fast access and continuous availability.
Type #1: Relational Databases (SQL)
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 complex queries and ensuring information integrity and security This type of bank is ideal for financial systems, ERP and CRM , in which errors or inconsistencies are not acceptable .
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 makes it easier to use traditional databases . It is designed to automate complex tasks such as backups , updates and disaster recovery, freeing teams to focus on developing their applications.
- When to use: it is recommended for financial systems, ERP, CRM and e-commerce , which require secure transactions and always consistent data.
- Main benefits (pros): support for several engines, including MySQL , PostgreSQL , Oracle and SQL Server ; backup and quick recovery in case of failures; vertical scalability to keep up with demand peaks.
- Main challenges (cons): high cost for large-scale applications and high constant load; It is not suitable for unstructured data (such as photos and videos) or projects that require horizontal scalability.
Amazon Aurora
Amazon Aurora is an optimized version designed for superior performance and continuous availability . It is compatible with MySQL and PostgreSQL , but offers up to five times more performance at the same cost, as well as automatic replication between multiple AWS regions.
- When to use: It is ideal for digital banks, subscription platforms and online education systems, which need high speed and constant stability.
- Main benefits (pros): performance up to 5x better than traditional MySQL high availability with replication across multiple AWS regions; automatic scalability without downtime , ensuring continuity of operations.
- Main challenges (cons): complex configuration may be required for advanced applications; higher costs compared to conventional SQL databases.
Type #2: Non-relational databases (NoSQL)
The term “NoSQL” means Not Only SQL (in Portuguese, Not Only Structured Query Language), 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 does not follow a fixed schema (such as user profiles, application logs or data from IoT sensors). 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 indicated 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 games and IoT platforms. With built-in global replication, DynamoDB ensures data is always available, regardless of users' geographic location .
- When to use: Ideal for IoT, e-commerce and gaming platforms, where low latency and high global availability are essential.
- Main benefits (pros): automatic scalability and integrated global replication; minimum latency, ensuring real-time responses; serverless model (when the service does not require direct server management), scaling as necessary.
- Main challenges (cons): costs can increase quickly with large volumes of data and frequent operations; does not support complex transactions as efficiently as relational banks.
Amazon DocumentDB
Amazon DocumentDB is a solution compatible with MongoDB (a type of NoSQL database) aimed at storing and querying JSON documents . It is ideal for applications that deal with dynamic data and need flexibility in the structure of records (for example, social media platforms and content management systems).
- When to use: it is recommended for social networks, which 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.
- Main benefits (pros): compatible with MongoDB , facilitating migration and integration with existing systems; flexible scalability and backups ; ideal for applications that require semi-structured data.
Main challenges (cons): high costs compared to self-managed versions of MongoDB ; limitations in complex transactional queries.
Type #3: In-Memory Databases
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 require high performance , for example, multiplayer streaming platforms and payment systems, where every millisecond counts to guarantee a satisfactory user experience.
On AWS, Amazon ElastiCache is the leading solution for in-memory banking, with support for two popular engines, Redis and Memcached . While Redis is ideal for more complex operations like manipulating lists and queues, Memcached offers a more lightweight solution for caching . Keep watching 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 session caching game
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- multiplayer games , where thousands of players interact simultaneously and each action needs to be processed in milliseconds to ensure a fluid experience; streaming platforms and financial systems, which need real-time responses to deliver a satisfactory experience.
- Main benefits (pros): almost 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); It is not recommended for persistent storage as the data is volatile.
Type #4: Databases for storing graphs
Graph databases are designed to store and analyze complex relationships between different types of data, which also need to be analyzed for the connections between them. Its graph-based structure facilitates navigation and execution of queries involving multiple nodes and relationships, which would be difficult to do with traditional relational databases.
On AWS, Amazon Neptune is the primary solution for this type of database. It is designed for high-performance graph queries , being compatible with languages such as Gremlin and SPARQL , 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 Resource Description Framework ( RDF) graphs , offering a powerful solution for cases that require fast navigation between large interconnected data sets.
- When to use: It is ideal for social networks, knowledge platforms, where relationship analysis is essential, and streaming , which need to suggest content based on interactions and preferences of connected users.
- Gremlin and SPARQL graph languages , facilitating their implementation; high performance for browsing and querying interconnected data; automatic management with backup and recovery.
- Main challenges (cons): steep learning curve when working with graph languages; It can be complex to configure and optimize for specific queries.
Type #5: Data warehouses
Data warehouses ( in Portuguese, data warehouses) are designed to store and process large volumes of historical data , allowing fast and complex analyses. Unlike operational databases, which are optimized for real-time transactional queries, data warehouses are focused 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, deep insights. Check it out:
Amazon Redshift
Amazon Redshift is a fully managed cloud data warehouse efficiently process complex analytical queries It allows companies to integrate data from multiple sources, and perform advanced analytics in near real-time .
- When to use: it is recommended for companies that need to consolidate large volumes of historical data for reporting and strategic analysis, such as in BI ( Business Intelligence ), finance and marketing .
- Main 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 quickly in projects with a large volume of data; need for query optimization to ensure maximum performance.
Type #6: Ledger Databases
Ledger is a type of immutable database, ensuring an auditable and secure trail for transactions , essential for compliance and tracking.
On AWS, Amazon QLDB is the main solution for this type of application, allowing you to accurately verify all changes and transactions carried out in the database. Find out more below:
Amazon QLDB
Amazon QLDB (Quantum Ledger Database) provides a complete, immutable history of every change made to your 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 is ideal for financial institutions and systems, which need to maintain complete and immutable records of transactions 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.
- Main benefits (pros): guaranteed immutability, with a complete trail of all changes made; does not require blockchain , facilitating implementation;
cryptographic verifiability, ensuring the integrity of recorded data.
- Main challenges (cons): lower performance for complex queries compared to other types of databases; Costs can increase with large volumes of immutable data stored over time.
Type #7: Time Series Databases
Time Series databases are designed to store and analyze data that changes over time , such as sensor readings, performance metrics, and IoT data. These banks allow quick and accurate queries of data organized by timestamps, making them ideal for continuous monitoring and predictive analysis .
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. It organizes data by time and allows companies to perform analysis in real time, with a system that automatically compresses and archives data, reducing costs.
- When to use: it is recommended for monitoring IoT sensors in a factory, where it is necessary to monitor metrics such as temperature, humidity and vibrations in real time to avoid failures and ensure operational efficiency; infrastructure monitoring and analytical applications, where data needs to be analyzed in real time and organized by period.
- Main benefits (pros): optimized for large volumes of temporal data, with fast queries; automatic and hierarchical storage to reduce costs 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 can increase if data is not archived or optimized correctly.
How to choose the ideal AWS database?
Choosing the right database may seem like a challenge, but it doesn't have to be complicated. The key is to understand the nature of your project and align that with the options available on AWS. Below, we bring some essential factors for you to reflect on before choosing the ideal service.
- What type of data will be stored?
That's the starting point . If you work with structured data that fits well into tables and requires high consistency, SQL relational databases such as Amazon RDS or Amazon Aurora are perfect for this scenario. Unstructured or semi-structured data benefits better from NoSQL databases, such as Amazon DynamoDB or Amazon DocumentDB . - What kind of speed does the application need?
In applications that need fast responses, real-time performance is essential. In-memory databases, like Amazon ElastiCache , store information directly in RAM, ensuring minimal latency. - What will be the volume of data and how will it grow?
Consider volume and the need for scalability . Projects that accumulate large volumes of historical data need solutions that facilitate advanced analytics and strategic reporting. Amazon Redshift , for example, supports complex analytics with direct integration into BI tools. On the other hand, if your project involves data that changes over time, Amazon Timestream is an efficient choice for organizing and querying temporal data. - What does the need for tracking and full transparency look like?
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 complex blockchain . - What is the demand for complex connections and relationships?
Complex connections and relationships require graph banks . If your application needs to analyze dynamic relationships, Amazon Neptune is the ideal solution. Its ability to navigate and query interconnected data in graphs guarantees high performance and flexibility.
- What is the investment expectation in these solutions?
Costs and maintenance must always be considered . All AWS databases are managed, eliminating the need for manual maintenance. However, costs may vary depending on the type of service and volume of data. Services like Amazon DynamoDB , which follow the serverless , scale automatically without intervention, but it is important to monitor their usage to avoid surprises.
Integrating and choosing the right databases can seem challenging, especially when faced 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 to simplify data management, offering continuous support and implementation that follows the specific needs of each client.
With expertise in cloud and automation , we ensure that each organization has access to the best technologies without wasting time or increasing operational complexity. Whether it's scaling a digital business or integrating corporate systems, we're ready to guide your company through each stage of digital transformation .
Conclusion
The decision about which database to use on AWS is not only technical, but also strategic . As we have seen, each service responds to specific needs: SQL and NoSQL for different types of data; memory banks for instant responses; data warehouses for in-depth analysis and other formats aimed at more specific applications, such as graph banks and time series.
More than knowing the options, it is essential to understand what makes sense for your project now and in the future . The most robust solution will not always be the best choice. The idea is to align technology and needs to ensure agile, efficient and ready-to-scale operations.
Below, we present an easy summary of each type of database discussed and their characteristics:
| 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 correct configuration |
| Graph storage | Amazon Neptune | Social platforms, recommendation systems | Fast navigation across interconnected data | Steep learning curve |
| Data warehouses | Amazon Redshift | BI, financial reporting and marketing | Integration with BI and high processing capacity | Need for optimization for high performance |
| Ledger (immutable records) | Amazon QLDB | Regulatory compliance and auditing | Immutability and cryptographic 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 in the long term, the true value of AWS lies in the possibility of evolving along with business demands. The scenario changes, projects grow and solutions need to keep up with them. Therefore, choosing the right database today can be the difference between stagnation or advancement tomorrow .
Instead of choosing an option based on fame or fad, the focus should be on how technology can solve real problems and bring efficiency to each stage of the operation . It is this harmony between need and solution that allows companies to go beyond what was expected, and prepare for whatever lies ahead.
