Simplifying Data Management with Hybrid Networks

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Corporate decisions made by gut instinct are rapidly going out of fashion as business leaders can now turn to increasingly vast amounts of data to help them make data-driven decisions. And make no mistake: the amounts of data that enterprises have at their disposal really are vast. From 2020 to 2022, enterprises’ data stores will grow from 1 petabyte (PB) to an estimated 2.02PB — an annual growth rate of more than 42%, according to Statista.

Some of this data growth will come from companies choosing to retain more data, but more will come from new sources — IoT applications is one example, but there are many more. By 2025 IDC expects that a total of 462 exabytes, that’s 462 billion gigabytes, of new enterprise data will be generated every day. 

If these huge volumes of data were all stored in enterprise data centers then that alone would cause data management headaches of epic proportions. But the reality is that things are going to get much more complex.

That’s because, as we know, most enterprises also carry out significant operations, and store large volumes of data, in the cloud. And not just in one cloud, but in many. Some 93% of enterprises currently have a multi-cloud strategy, and 87% have a hybrid public/private cloud strategy, according to Flexera’s State of the Cloud 2020 report.

There are a number of reasons for this: 

  • Some apps are siloed in different clouds
  • Some enterprises want workload mobility between, say, AWS and Microsoft’s Azure to enable failover
  • Some have apps that span public and private clouds
  • Some use clouds for cloud bursting during periods of high demand. 

The upshot is that the average enterprise makes use of 2.2 public and private clouds, and this begs an awkward question: how do you manage vast amounts of data effectively when it is stored in this hybrid multi-cloud environment? 

Also read: Utilizing Private Cellular Networks for Enterprise Connectivity

The Multi-cloud Data Management Solution

One option is to attempt to manage data in each of these locations — data center, and different public clouds — separately, despite the limitations of this approach. Enterprises can use traditional data management solutions, such as those offered by enterprise storage system vendors, in their data centers, and rely on their cloud vendors for help in their clouds.

A more complex but more effective approach is to do some form of multi-cloud data management (MCDM), which includes both public clouds and private ones. But so far just 40% of enterprises have taken this approach, according to research carried out by Evaluator Group. 

The top use case for MCDM is disaster recovery and business continuity, according to Krista Macomber, Evaluator Group’s senior analyst for data protection. But this is far from the only use case for MCDM.  Other popular use cases include data governance, data protection, security, data intelligence, and helping to ensure that the right data is available to applications that need it, at the right time — especially when it needs to be temporarily moved or permanently migrated to one cloud or another. 

The key to this, and this is where data management systems can help, is the automation they provide. That’s because migrating data from applications and other sources can be highly labor intensive, and, crucially, prone to humans errors. But an MCDM system can automate this process and thereby eliminate (most) human errors.

Also read: The Ultimate Guide To Server Management Software

MCDM Vendor Offerings

So who offers MCDM solutions? The answer to that is an increasingly large group of companies. 

“Far and away, Amazon and Microsoft are the vendors that respondents most often reported having engaged with for multi-cloud data management, and that they intend to engage with for multi-cloud data management,” says Evaluator’s Krista Macomber. But a range of vendors, most notably data protection vendors, are making investments in these areas, bringing enhanced capabilities such as improved data migration and visibility to bear, she adds.

For companies that rely heavily on a container-based infrastructure, often using Kubernetes-based systems for orchestration, it makes sense to carry out MCDM at a container-aware level. That explains new standalone multi-cloud data management offerings for Kubernetes data from the likes of Diamanti with its Diamanti Ultima.

 “Many enterprises struggle with the complexity of Kubernetes and legacy storage and networking solutions that are not container-aware,” explains Tom Barton, Diamanti’s CEO. “Products like Diamanti Ultima promise enterprises an all-in-one approach for data management and portability across different clouds.” 

These products may be the exception, however. More commonly, enterprises are likely to take a mix-and-match approach when it comes to assembling a suitable hybrid cloud data management solution.  

“Multi-cloud data management implementations typically are a collection of products, often from multiple vendors,” says Macomber.  “It is advisable for IT departments to first inventory their existing technology assets, and from there make decisions regarding additional required purchases,” Macomber concludes. 

Also read: The High Cost of Counterfeit Networking Hardware

Paul Rubens
Paul Rubens
Paul Rubens is a technology journalist specializing in enterprise networking, security, storage, and virtualization. He has worked for international publications including The Financial Times, BBC, and The Economist, and is now based near Oxford, U.K. When not writing about technology Paul can usually be found playing or restoring pinball machines.

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