Today we are going to discuss all aspects of multi cloud data management. The majority of businesses have huge, challenging-to-manage data footprints. Disconnected data sources, uneven or nonexistent labeling and metadata standards, and local control over data access restrictions all contribute to management issues. When businesses move their data to numerous cloud providers, searching for important data feels like looking for a needle in a haystack. At that point, multi cloud data management becomes a vital solution.

As enterprises distribute apps and data across several cloud providers and geographies, managing data in the cloud becomes more challenging.

What is multi cloud data management?

A multi cloud data management architecture provides advantages, but it also comes with some disadvantages. Now let’s go over data management strategies for multi cloud systems.

Companies can gather, store, and utilize data from various cloud computing environments using a set of tools and methods known as multi cloud data management. Cloud infrastructure is something we are all familiar with. Cloud platforms are enormous, dynamic, and scalable when it comes to handling data and processing information for applications. The most sophisticated applications on the market today are built on top of them.

Understanding multi cloud architecture

The use of several cloud services from various providers to meet various purposes and criteria is known as multi cloud architecture. It lets businesses choose where their data, apps, and workloads are hosted.

By enabling businesses to distribute their workloads across many providers, a multi cloud data management solution can increase availability and redundancy as well as improve performance. Additionally, depending on their services, it enables enterprises to switch between service providers.

Instead of acquiring all of their resources from one supplier who might not be the greatest fit for their workloads, firms can mix and match storage, networking, analytics, and application platforms from several providers with multi cloud architecture.

What is multi cloud deployment?

Multiple cloud deployments of the same type (public or private), acquired from various providers, are referred to as the multi cloud. Multiple deployment types, whether public or private, have some sort of integration or orchestration between them are referred to as hybrid clouds.

Cloud costs have started to become a heavy burden for the IT sector

What is cloud agnostic database?

A cloud-agnostic database is constructed to take full advantage of open-source technologies and portable components. This type of architecture is designed to make it simple to switch cloud providers or even to use many at once. Cloud-agnostic databases allow you to choose your own route into the cloud, in contrast to cloud-native solutions, where you are often at the whim of the cloud solution provider.

How do you manage a multi cloud environment?

Although there are many different multi cloud data management approaches and solutions, the best ones all have a few things in common. The most powerful feature is probably interoperability with containers, making it simpler to migrate workloads between cloud providers than virtual machines. Therefore, support for Kubernetes and containers is essential for a strong multi cloud data management platform.

Effective multi cloud data management solutions also have automation. Automation enables IT teams to extend their operations without being overburdened while also swiftly and reliably deploying, monitoring, and securing apps. Automation makes it easier for companies to handle the complexity of multi cloud systems.

How does multi cloud data management work?

Making sure that workloads are portable is the first step in moving them across a multi cloud data management environment. You must ensure that a workload can be migrated without requiring major changes and that its cloud-native components are preserved. Since each container is a lightweight bundle of one app and its dependencies, they are easier to move across different clouds and environments, which is why many businesses are turning to them for greater workload portability.

A cloud management platform can be useful in this situation because you’ll also want to ensure that application administration is uniform across clouds. The right cloud management enables you to select the ideal platform for your application and enforce uniform policies across your cloud environments.

The final step is to know when (and why) to transfer workloads to a different public cloud. Moving workloads still requires some effort, even when using containers, and there is always a chance that performance will suffer in the new setting. This is why having a well-thought-out multi cloud strategy is crucial: Are duplicate deployments your goal in an effort to boost resilience? Or do you employ a distributed deployment strategy where each job is run in the most appropriate environment? Choose a goal, then only move your workloads toward that objective.

Multi cloud data management infrastructure types

Generally speaking, there are three different types of cloud infrastructure.

Public multi cloud data management strategy

When we think of the cloud, we typically think of public cloud infrastructure. The resources are shared among several users and are frequently made available as a monthly service. In this context, the term “shared” refers to the fact that each individual customer will have a separate instance of their services running on a shared set of hardware. Businesses can benefit from a number of benefits from a public cloud, including significant cost savings and scalability because resource allocation is simple.

Private multi cloud data management strategy

On the other hand, the private cloud can be located on-site or, in some cases, be vendor-provided cloud capacity that doesn’t share hardware across various clients. Although private cloud storage is frequently expensive, it also offers more dependable resource availability and scaling and various advantages for security and resilience.

Hybrid multi cloud data management strategy

Hybrid clouds can be created using both public and private resources. For mission-critical assets, the majority of configurations will comprise a fixed allocation of private cloud resources together with extra public resources that can scale as needed. The hybrid cloud, which is frequently used for highly changing workloads, is the foundation for the technique of “cloud bursting,” or quickly distributing and reassigning public cloud resources for a project as needed while retaining a stable private environment.

Don’t let your data go away in the case of an emergency

Because many current cloud users will rely on hybrid systems to manage complex enterprise workloads, it’s critical to note the contrasts here. A multi cloud setup, however, is not a hybrid cloud.

Multiple cloud infrastructures are combined in multi cloud systems. The distinction is made not so much between private and public cloud systems but between completely separate providers or vendors. With such a system, several infrastructures from suppliers such as Google, Amazon, or Microsoft can cooperate to support various computing requirements.

A public multi cloud environment is the most prevalent type, where an organization takes advantage of the open resources provided by various businesses to build a unified, reliable infrastructure. However, modern multi cloud systems can integrate hybrid, private, and public clouds.

To combine many cloud systems into a unified platform, administrators will employ tools and procedures to connect workloads and applications across different systems. Apps and procedures specifically concentrating on data are referred to as multi cloud data management.

What are the benefits of multi cloud data management?

Like with any technology, businesses use it when it offers a set of essential advantages that can help them carry out their objectives, particularly in the area of data-rich, high-performance research and computation.

The following are just a few advantages of multi cloud data management systems:

• Vendor lock: Even putting all of your eggs in one vendor’s basket has some advantages, and it can also make you and your business depend on them. You can approach such agreements with a more modular strategy and avoid being shackled to a single infrastructure or management system by employing multi cloud management.
• Security: With multi cloud administration, you can set your own rules for anything from data management to security to application monitoring and observability. Decisions about the adoption of or changes to configurations relating to security and compliance controls can therefore be made from a single location, even across different cloud vendors.
• Financial Management: Cost is a big issue with cloud computing, especially as the demand for more maintenance resources increases. Administrators can better regulate cloud resources’ size, scope, and application to lessen out-of-control spending with a strict management approach.

Challenges of using multi cloud data management solutions

Multi cloud data management is far more challenging than in an on-premises-only or simple hybrid-cloud setup, even with a poly-cloud degree of precision. Add to this the fact that moving to multiple clouds is frequently done, at least in part, haphazardly. The IT department may rely on carefully selected primary public cloud service providers. Still, individual business units may self-onboard (also known as “shadow IT”) additional public cloud service providers based on their particular needs.

Thus, despite all of its advantages—and necessity—the multi cloud method may provide additional problems with data management, such as:

Complicity

When a company utilizes a multi cloud data management solution, far too many of them believe they are purchasing a result. Actually, they are just purchasing infrastructure. In essence, even though the servers themselves might not be yours, the data and ultimate accountability for it remain yours. While managing one job and its data in a cloud environment may be straightforward, doing so while using various management tools across several cloud environments is not. Making sure that appropriate data compliance and governance guardrails are in place for each cloud environment becomes more challenging as complexity rises.

Every dark cloud has a silver lining with the container as a service (CaaS)

Cost

Although potential cost reductions are one of the main advantages of enterprise cloud computing, poor data management in multi cloud systems can soon result in skyrocketing expenses. According to Gartner, businesses frequently overspend on cloud services by up to 70% without realizing the anticipated benefits. Solutions for data archiving, backup, and recovery that weren’t made for multi cloud architectures fall under this category.

Security

The amount of potential vulnerabilities in an enterprise’s attack surface grows along with complexity, increasing the danger of data breaches like ransomware. Additionally, recovering from an attack becomes more challenging.

Multi cloud database examples

A standalone database created, deployed, and used in a cloud environment is referred to as a cloud database. A cloud database combines the flexibility of cloud computing with all the features of a traditional database.

The UK will probe Amazon, Microsoft, and Google regarding their cloud market share

Some of the most popular multi cloud data management solutions are listed below.

Microsoft Azure

One of the most used and widely used cloud systems is the Microsoft Azure cloud database. It provides computer, networking, databases, analytics, AI, and IoT services.

Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service are just a few of the options available on Microsoft’s public cloud computing platform (SaaS).

Microsoft Azure provides a variety of software options that let customers build huge ecosystems on the same foundation, making any problems simple to fix.

The drawback of Azure is that it requires specialized management and upkeep, including server monitoring and patching.

Oracle

Oracle provides its users with enterprise-scale cloud database technology. The database solution automates database management using machine learning, providing excellent performance, dependability, and security.

The Oracle cloud database covers hyper-scale Big Data and Streaming applications, such as OLTP, data warehousing, Spark, text search, image analytics, and data catalog.

Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and Data as a Service is the various options provided (DaaS).

Lack of integration with other cloud systems is a drawback.

Google Cloud

The same hardware and architecture as other Google products are used by the Google Cloud Platform (GCP), which provides a number of services. A wide range of hosted services for cloud computing, storage, networking, big data, machine learning, Internet of Things, cloud management, etc., are included in GCP’s offer.

IaaS, PaaS, and serverless computing environments are offered by GCP.

The database storage solution for NoSQL non-relational storage, Cloud Datastore, is one of the components of the Google Cloud Platform.

Google’s native Cloud Bigtable database and Cloud SQL for MySQL completely relational storage are further Google Cloud solutions.

The drawbacks include the absence of managed services and exorbitant rates, which include a significant support fee.

Is Google cloud a multi cloud data management service?

You may boost business agility and profit from multi cloud by using Google Cloud to build new apps rapidly and update current ones. They provide a uniform platform, data analysis, and service-centric perspective across your environments for your deployments, regardless of where they are located.

So the answer is yes.

OpenStack

OpenStack is a multi cloud data management service that is open-source, extremely flexible, scalable and has a straightforward architecture and straightforward deployment. The database is created by the community and released every six months. It is based on development milestones.

Managing many database instances is possible, and both relational and non-relational database engines are supported.

The drawback of OpenStack is that advanced engineering knowledge is necessary to navigate its voluminous configuration options and address any potential problems.

Rackspace

Cloud databases from Rackspace can be scaled, completely managed, or hosted. They are known for their excellent performance and SANs built on the OpenStack platform.

With frequent backups of all cloud databases, Rackspace provides simple access to your cloud database through the Cloud Control Panel, CLI, or API.

In the event of a disaster or hardware failure, data protection is ensured through redundant storage and synchronous data replication.

The disadvantage is that there are fewer data centers than the rivals.

Conclusion

The conundrum is that while multi cloud data management is, in many ways, the best strategy for enterprise cloud computing, how can businesses get past the difficulties it poses for data management?

Consolidate data management first. The key to lowering the complexity of multi cloud setups has end-to-end visibility into and control of your complete data estate—from your on-premises resources to all of your public cloud service providers—through a single pane of glass. The majority of data management tools available today, however, are not really designed to function in multi cloud situations. Instead, use web-scale technologies to enable more cost-effective, efficient, and secure data management from the edge to the core to the cloud by using cloud-optimized, at-scale data management.

Automate data management, next. In today’s multi cloud environments, it’s already challenging for IT teams to keep up with data management requirements. As businesses depend on more cloud services, ransomware attacks and other threats to data integrity multiply, and data privacy laws tighten, the challenge will only get more difficult. Technology powered by AI that can fully autonomously self-provision, self-optimize, and self-heal data management services for the enormous volumes of data in the multiple cloud environments businesses are shifting to

In the same way that it is an understatement to say that enterprise cloud computing has entered the mainstream, it is an understatement to suggest that multi cloud implementation is what enterprise cloud computing will look like in the future. Use unified, independent multi cloud data management to overcome the difficulties.