The microservices approach to applications replaces traditional monolithic application architectures with applications built from separate, loosely coupled services. Individual microservices often benefit from independent data storage tailored to particular requirements. Data services offer a modern way to store, manage, and process data, addressing the challenges developers, infrastructure teams, and operators face in deploying microservices, serverless, and other modern application architectures at scale. Cloud native applications can benefit from data services such as:
- On-demand caching to cache legacy data and increase app responsiveness
- Automated messaging and event streaming to provide reliable communication among servers, apps, and devices
- Cloud native database solutions that are fast, efficient, and simple to provision
- Data warehouse capabilities for deep analytics using massively parallel processing (MPP)
Cloud-based data services give development teams the freedom to choose the right data service for each specific workload. With traditional application architectures, data can become enmeshed with the application in a way that complicates change. Data services facilitates cloud native apps that are resilient and easy to modify. By choosing the right data services, developers can realize the speed and productivity of self-service and automated provisioning of storage resources and data management capabilities.
While the terms data services and data-as-a-service are sometimes mistakenly used interchangeably, conceptually the two are quite different. As you just learned, data services give developers the ability to address specific data management requirements to simplify development, accelerate application delivery, and increase the performance of cloud native applications. Data-as-a-Service (sometimes referred to as DaaS) operates at the other end of the data spectrum.
As organizations gather increasing amounts of data, they have difficulty leveraging that data to enable data-driven decision making. The Data-as-a-Service category encompasses various solutions to help enterprises organize and gain value from diverse data. Snowflake is one example. The term data-as-a-service is also used to describe cloud services that provide public access to data sets as a service. An example of this type of service is D&B Hoovers.
Data services may encompass a range of data storage, communication, and management tasks that contribute to application transformation. Data services benefits include:
- Modernization of legacy data. On-demand caching, efficient messaging, and distributed data accelerate and simplify high-performance, cloud native apps. Choosing the right data services reduces the total amount of custom code developers must write.
- On-demand self-service access. Developers are usually able to provision custom instances of the data services they need on demand. Automated provisioning supports rapid development, testing, and continuous delivery across clouds.
- Data management at web scale. Data services run at scale on any cloud and may incorporate features such as built-in backups, monitoring, and high availability to facilitate successful operations.
- Built-in security. Modern data services include security that’s built in rather than bolted on, including the latest capabilities for authentication and authorization, and encryption for passwords, certificates, and other secrets as well as data in flight.
Utilizing the right data services can help your team accomplish the following objectives:
- Enhance development. Self-service and automated provisioning enable developers to focus on building resilient apps across multiple clouds.
- Ensure data security. Best-of-breed data services incorporating the latest security technologies help you ensure compliance with government and regulatory requirements and protect your infrastructure, applications and data from growing cyber threats.
- Ease your cloud journey. Capture, process, and serve data at scale to better support both cloud native and traditional applications.
While there are many data services to choose from, there are several categories that are especially useful for modernizing applications:
- Relational database service. While relational databases are hardly a new idea, traditional relational databases can be difficult and time consuming to deploy and are often under the control of DBAs. Relational database services make robust database capabilities available via self-service with minimal fuss.
- In-memory caching service. Web applications can suffer from latency issues, and traditional databases are often too brittle or unreliable for use with microservices. That’s why every modern distributed architecture needs a cache. Data that is accessed frequently is cached in local memory. Caching services are designed to maintain consistency across distributed applications and to ensure reliability and availability.
- Message broker or streaming service. One of the challenges of distributed architectures is communication between application elements. Message brokers provide a common communication mechanism that can enable the transmission (streaming) of large amounts of data and that can scale with your application needs. Message brokers frequently provide additional services like translation, routing, persistence, and delivery.
While many organizations are attracted to public clouds such as AWS, Google Cloud Platform, and Azure because of the data services and other capabilities they offer, IT teams can also create data services for easy consumption within private clouds and across multi-cloud operations. Choosing a data service from a particular cloud provider can be a disadvantage for organizations that have operations in multiple public clouds, making software less portable.