What's the first thing that comes to mind when you think cloud-native? Containerization? Microservices? They're critical ingredients of a cloud-native architecture, but not the whole thing. Cloud-native doesn’t mean just moving to the cloud – it's about taking advantage of the cloud infrastructure and services to accelerate digital transformation for delivering business value.
The concept of cloud-native began when public cloud vendors started providing easy and affordable access to the elastic instances of computing power. But, the challenge was to write applications to capitalize on the flexibility of this new cloud infrastructure for achieving business goals.
The cloud-native approach helps organizations design, build, modernize and operationalize applications and services to benefit from the cloud’s inherent benefits, including flexibility, agility, scale and cost.
Many organizations are in the midst of a massive data transformation to out-innovate their rivals for competitive advantage with a cloud-native approach. The cloud-native approach enables organizations to build modern applications by leveraging cloud computing benefits based on a flexible consumption-based pricing model or pay-as-you-go pricing model. Cloud-native applications are loosely coupled and scalable, running on a containerized and orchestrated platform.
The cloud-native approach offers the following benefits:
Cloud-native services harness microservice- and container-based principles, which can natively adopt full cloud scalability and accessibility. On the other hand, cloud-enabled and cloud-ready platforms are built for on-premises or locally hosted deployments and can run on public or private clouds. However, these do not take full advantage of the cloud.
Now that we’ve defined cloud-native, let’s look at a few foundational cloud-native architectural principles.
A microservice is an independently deployable service that serves a single and clearly scoped business purpose or capability. Microservices are like tiny Lego blocks. When assembled in the right way, you can build massive architectures. Microservices can be designed, deployed, managed and monitored from the ground up.
An application programming interface (API) provides data engineers with the flexibility to create and run workloads efficiently and allows administrators to respond to events in real time. API-driven architectures and design enable you to build applications that are modular, reusable, and extensible. That means data becomes easier to discover and reuse across your organization.
A container is an allocation of memory and CPU resources on a node with the compute role. An application service uses a container to remotely perform computations on the node. For example, a data integration service that runs on a grid can remotely run a mapping within a container on a node with the compute role.
Containers provide applications with an abstraction layer to run on, thereby abstracting away the underlying infrastructure and operating system. You can deploy containerized applications on any infrastructure, whether on on-premises data centers or public or private cloud infrastructure, while ensuring the consistency of applications, irrespective of the underlying infrastructure and runtime dependency.
For example, a containerized data integration pipeline will behave the same way whether it is deployed on a public cloud infrastructure or on-premises data center. Beyond portability and immutability, containers afford other benefits such as greater elasticity, less infrastructure maintenance and higher DevOps efficiency.
Elasticity means you can provision resources to an enterprise's processes based on the needs of that process. There are systems in place to automatically deliver or remove resources, to provide just the right amount of assets for each application.
Cloud-native security helps protect your organization and data from cyber threats, enabling you to take smart risks while maintaining a safe, compliant environment. Cloud-native security should offer various security controls, including identity and access management as well as auditing and alerting. Employees across your organization should be able to effectively collaborate with each other while administrators have sufficient control to secure and govern their tenant(s).
This is a software-as-a-service (SaaS) pricing model where cost is based on consumption, charging organizations on a per-use basis.
A multi-tenant environment hosts many organizational instances in a dedicated “private” environment using the public cloud. A multi-tenant architecture ensures that each tenant or each organization’s data is segregated from all other tenants and is only exposed to authorized users. This means there is no commingling of organizational data.
Serverless allows you to be free of managing servers, virtual machines (VMs) or containers. Serverless does not mean there are no servers involved (servers are still used for running applications). It simply means that you do not have to interact with, or control, the servers involved in the architecture. Serverless allows you to focus on the design and objectives of your application.
DataOps or data operations blends aspects of agile and DevOps to give organizations a framework for better collaboration among data scientists, data engineers, data stewards and data analysts that reduces latency in constructing large numbers of high-quality data pipelines. Like agile and DevOps, DataOps supports the notion of continuous improvement and delivery; stakeholders can determine whether the pipelines fit their needs and propose improvements.
Machine learning operations – or MLOps – is the key to successful machine learning projects. MLOps provides a critical framework for data scientists to build, train and test machine learning models. And, after models are in production, MLOps helps data engineers to test, deploy and monitor them on a continuous basis.
By adopting these cloud-native architecture principles, your organization can:
These days, there are a growing number of cloud-native solutions. For example, cloud-native data management solutions allow you to build and operationalize your data integration pipelines and application integration initiatives to take advantage of the cloud computing delivery model.
Cloud-native data management solutions enable organizations to accelerate their digital transformation journey, increase ROI and succeed in their consolidation or modernization initiatives. A cloud-native data management solution can ingest, catalog, integrate, apply data quality rules and prepare the data in a governed manner to make it available for advanced use cases in a democratized way. Let’s walk through a few of these cloud-native solutions.
Here are a few examples of organizations using cloud-native solutions to transform their business:
A cloud-native approach is essential for organizations looking to modernize existing applications and operationalize end-to-end data pipelines. It infuses intelligence and automation into cloud-native data management for driving advanced analytics and AI/ML use cases. To learn more, visit the Intelligent Data Management Cloud page.
