IBM Cloud® Database:
A fully managed data platform for modern applications
With IBM Cloud® Database-as-a-Service (DBaaS) services, specialists deliver and maintain ready-to-use, highly available, database instances, allowing developers and IT staff to focus on value added tasks.
IBM Cloud supports an extensive portfolio of relational and non-relational (No-SQL) databases and integrations to support building or migrating a wide range of application types across all industries. Relational databases structure data in a tabular format with a fixed schema, while non-relational databases use flexible schemas, organizing data differently depending on the database type. Irrespective of the type of non-relational database, they all aim to solve for the flexibility and scalability issues inherent in relational models which are not ideal for unstructured data formats, like text, video, and images.
Supports structured, unstructured, SQL, NoSQL, IoT, blockchain and more, with full integration across IBM Cloud services in compute, observability, identity and access management.
IBM Cloud® Databases allow developers and IT teams to focus on building applications while IBM manages infrastructure operations, database software updates, backups, and scaling to meet changing workload demands.
IBM Cloud Databases provide built-in security capabilities across Classic and Gen 2. Gen 2 strengthens network isolation with private-only access and software-defined networking, giving organizations greater control over connectivity and access.
Data-in-motion encryption through Transport Layer Security (TLS) and at-rest encryption for data on disk and backups throughout integration with IBM® Key Protect Services.
Products
Use cases
Databases power real-time, user-centric applications such as mobile apps, web portals and personalized digital services. They support functions like user profiles, recommendation engines, session management and content delivery across industries such as retail, media, travel and financial services.
Managed databases serve as reliable, scalable data sources for training and operationalizing AI models. They store features, labels and inference outputs, enabling use cases such as predictive maintenance in manufacturing, fraud detection in banking, demand forecasting in retail and intelligent automation across many sectors.
Analytical applications dissect and analyze data to inform and drive business decision making across all data-driven industries. These applications are vital for tasks like pricing and packaging in e-commerce, planning and resource management in manufacturing, customer management and business intelligence in analytics, and IoT operations in logistics, among others.
Operational applications leverage managed databases to handle daily businesses operations by capturing and storing real-time data on operational events, enabling effective decision-making. This approach is applicable across various industries, including logistics, transportation, banking and e-commerce.
