Traditional SQL databases have been joined by a more flexible family of databases known as NoSQL. These databases promise scalability, speed, and adaptability in handling vast amounts of structured and unstructured data. This article explores the various NoSQL offerings by AWS, their features, and how to discern which one aligns best with your unique project requirements.
What is NoSQL?
NoSQL stands for “Not Only SQL”, implying that while SQL can be a part of the system, it’s not the only option for data storage. NoSQL databases are an increasingly popular option for web applications that handle big data or real-time processes. They can store unstructured, semi-structured, or structured data, providing flexibility, scalability, and speed.
NoSQL databases are designed to overcome SQL’s limitations – they support auto-sharding, making it easy to distribute data across multiple servers. They also support replication, ensuring data availability in case of failure. NoSQL databases handle large volumes of data at high speed, making them a good fit for real-time applications.
There are multiple types of NoSQL databases, including document databases, key-value stores, columnar databases, and graph databases. Each type is designed to solve specific problems, so it’s useful to know which is the right one for your requirements.
AWS Native NoSQL Options
Amazon Web Services (AWS) offers a variety of NoSQL databases to cater to different data management needs.
Amazon DynamoDB
Amazon DynamoDB is a fully managed service that provides fast and predictable performance with seamless scalability for NoSQL databases. It’s suitable for an application that needs to support key-value and document data structures. DynamoDB offers multi-master and multi-region replication, ensuring high availability and data durability.
DynamoDB supports ACID transactions, which makes it a reliable choice for applications with complex business logic requirements. It also provides built-in security, in-memory caching, backup and restore, and real-time event notifications, allowing it to support many use cases.
Amazon DocumentDB
Amazon DocumentDB is a fully managed service that enables document databases to support MongoDB workloads. It’s designed for high performance, availability, and scalability. DocumentDB can handle large amounts of document data, making it suitable for content management, catalogs, and user profiles.
DocumentDB also supports MongoDB application code, drivers, and tools, allowing you to migrate your MongoDB applications to DocumentDB without code changes. It offers automatic scaling, replication, and backup and restore across multiple Availability Zones, ensuring durability and availability.
Amazon Keyspaces
Amazon Keyspaces is a highly available, scalable, and fully managed Apache Cassandra-compatible database service. It’s useful for applications that require wide column and key-value data models. Keyspaces supports the Cassandra Query Language (CQL), making it easy for developers to manage data.
Keyspaces automatically scales to support your application traffic, providing consistent, single-digit millisecond response times. It offers replication across multiple AWS Regions, ensuring high availability and durability. Keyspaces also supports SSL for client connections, providing secure data transmission.
Amazon Neptune
Amazon Neptune is a fast and reliable graph database service. It’s fully managed and is designed to handle connected data sets and gives you the power to query data with milliseconds latency. Neptune supports common graph models like W3C’s RDF and Property Graph, and their query languages SPARQL and Apache TinkerPop Gremlin.
Neptune is highly available and durable, replicating six copies of your data across three AWS Availability Zones. It offers continuous backup to Amazon S3, point-in-time recovery, and data replication across Regions. Neptune is suitable for knowledge graphs, fraud detection, social networking, and recommendation engines.
Amazon Timestream
Amazon Timestream is a fully managed, fast, scalable time-series database service. It’s designed to handle the scale and complexity of time-series data efficiently. Timestream automates rollups, tiering, retention, and compression of data, so you can focus on building the applications.
Timestream’s query engine lets you access and analyze recent and historical data together. It reduces the cost and effort of managing data lifecycle. Timestream is suitable for IoT applications, DevOps, application monitoring, and real-time analytics.
Amazon Quantum Ledger Database
Amazon Quantum Ledger Database (QLDB) is a fully managed service for ledger databases. It provides an immutable, transparent, and verifiable transaction log. QLDB uses an append-only journal, where each transaction is uniquely numbered, timestamped, and immutable.
QLDB supports PartiQL, a SQL-compatible query language. This makes it easy for developers to interact with the ledger. QLDB is useful for applications that require a complete and cryptographically verifiable history of all application data changes, such as supply chain, finance, and manufacturing.
Choosing the Right AWS NoSQL Database
When selecting an AWS NoSQL database, you should cover the following steps.
Understand Your Data and Requirements
To choose the right NoSQL database, you must understand your data and the requirements of your application. For instance, if you need to store and query graph data, Amazon Neptune would be the best choice. If you have time-series data, Amazon Timestream would be the most suitable. Understanding your data will guide you towards the most appropriate database service.
Consider Performance and Scalability
Another crucial factor is the performance and scalability of the database. If your application needs to support high-speed reads and writes, you should choose a database that provides high performance, like Amazon DynamoDB. If your application needs to scale to handle large amounts of data, you should opt for a scalable database like Amazon Keyspaces.
Analyze Cost Factors
Each AWS NoSQL database has its own pricing model, and the cost can vary based on factors like data storage, read/write capacity, data transfer, and backup storage. You should analyze these factors and choose a database that fits your budget.
Assess Integration and Compatibility
You should assess the integration and compatibility of the database with your existing system and tools. For instance, if your application is built with MongoDB, you can easily migrate to Amazon DocumentDB because it supports MongoDB workloads. Similarly, if your application uses the Apache Cassandra API, you can transition to Amazon Keyspaces without any code changes.
Conclusion
NoSQL databases offer a flexible, scalable, and high-performance solution for managing data in various contexts. AWS provides a variety of NoSQL databases, each designed to cater to specific data and application requirements. By understanding your data, performance and scalability needs, cost factors, and integration and compatibility requirements, you can select the most suitable AWS NoSQL database for your application.
Author Bio: Gilad David Maayan
Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Imperva, Samsung NEXT, NetApp and Ixia, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership. Today he heads Agile SEO, the leading marketing agency in the technology industry.
