Part 3: Cloudy with a Chance of Data? Here’s How to Develop a Multi-Cloud Data Fabric Through DataOps
Increasingly, more enterprises are relying on multiple clouds to run their unique portfolio of application workloads. For instance, one company might run its production applications in Oracle Cloud while maintaining its dev/test environments for those same applications in Amazon Web Services (AWS). Another business might leverage cloud backup in Azure but use Google Cloud Platform (GCP) for its AI/ML services.
The major providers, including Amazon, Google, and Microsoft, offer an increasingly broad portfolio of differentiated services. Taking full advantage of multiple providers means companies need to get a better handle on the data that fuels cloud use cases. Teams need the ability to migrate fast, secure data, provision it to various consumers, and protect it for compliance purposes across multiple clouds.
In order to overcome the challenges of managing data in a multi-cloud environment, industry leaders are looking to DataOps as a platform-based approach to grapple with the increasing tension of having to innovate quickly while complying with data privacy regulations. Leading advisory firm 451 Research found that 86 percent of companies plan to increase investment in DataOps strategies and technologies this next year, and 92 percent expect it to have a positive impact on their organization’s success.
Building a Multi-Cloud Data Fabric
Multi-cloud presents an environment fraught with a high level of data friction that stands in the way of fast, efficient data movement. There are more teams in play, more governance policies that need to be in place across multiple geographic regions, different tooling for applications running on diverse and distributed environments as well as an assortment of manual workflows. These are a handful reasons why DataOps is all the more important as a way to cut through friction when it comes to successfully moving and managing data across diverse cloud environments.
Central to DataOps is the alignment of people, process, and technology that enables the rapid, automated, and secure management of data. The goal is simple: to eliminate any friction throughout the data lifecycle. Here are the key defining characteristics of a DataOps platform that will empower teams to reap the benefits of multi-cloud:
- Cross-cloud data movement. A DataOps platform allows teams to move data from one cloud to another at speed while keeping data across clouds synchronized.
- Fast data environment provisioning within clouds. The ability to quickly provision data environments can accelerate integration testing of distributed, multi-cloud applications as well as be a source of validation for the workloads that are migrated.
- API-driven automation. Automation will eliminate manual processes and facilitate the integration the integration into DevOps toolchains.
- Governance across clouds. Visibility into data environments across clouds from a single point is extremely important when it comes to understanding where your sensitive data resides. Teams can also determine and specify who has access to what data in the cloud.
- Protect sensitive data. The platform should provide the ability to consistently mask data across clouds in non-production environments.
- Works across heterogeneous environments and data sources. Teams should be empowered to work with data from a diverse set of data sources and securely deliver data to every stakeholder at the speed and scale required to enable rapid development of applications.
In a multi-cloud world, addressing the data layer is more important than ever. Data can no longer exist within isolated silos distributed across multiple clouds/locations, and that’s exactly what DataOps is devoted to. Adopting a DataOps platform can help enterprise teams achieve a state of connectedness in which (1) data moves fluidly and securely from anywhere, to anywhere; (2) data is governed in a consistent, holistic manner; (3) and businesses can see and control data through a single platform. Companies are only able to innovate when they can unlock the data they need, and DataOps promises to drive faster innovation by providing access to quality data to those who need it in a self-service environment while maintaining security and privacy controls.