Big Data platforms often require big infrastructure to host them. Recently, it has been convenient for many organizations to default to private cloud operations for everything related to Big Data.
The benefit is high performance and availability—as long as you are in the same building as the private cloud.
Otherwise, on-premises Big Data operations are limiting. They have a shelf life both in terms of scale and capability.
That is why it's worth considering moving your Big Data operation -- from data storage and analysis to development -- to the public cloud. Following are specific reasons why Big Data operations can be best managed in the cloud.
5 Reasons for the Public Cloud
Below are the top five reasons why all organizations with Big Data applications should strongly consider moving to the public cloud.
Integration: Cloud providers are actively building integrations into toolchains and data sources, or building frameworks for collecting data in new places. This makes it easier to expand types of Big Data platforms and collection, and makes developing functionality easier. An example is Alibaba Cloud’s DataV tool, which streamlines the processes of data collection, visualization and interpretation through a single interface.
High availability of collectors: Perhaps the top reason for moving Big Data applications to the cloud is the high availability offered to your endpoints/data collectors. Most Big Data applications are receiving data from various source types and locations. These applications and devices can be unpredictable. The public cloud can have regions throughout the world, and more options for data input give Big Data platforms new possibilities for collecting data more efficiently. Data collection can happen more efficiently by optimizing collectors by region. Solutions like Alibaba Cloud’s Smart Hardware make it simple to collect data from devices and integrate it directly into the cloud. In this way, organizations can fast-track development of new places to collect data without specific considerations around accessibility and performance. What public cloud has to offer for ingesting data will be superior to the vast majority of organizations’ private clouds. In addition to the public cloud’s default capabilities to ingest data efficiently, as more Big Data platforms embrace edge computing, the public cloud’s high availability and services like CDN will allow platforms to bring data analysis, ETL, data reduction, and other data manipulation functionality directly to users.
vNext: A Big Data platform running on a private cloud is running on infrastructure not necessarily built for what the platform is today. Rarely are organizations able to predict the requirements of storage, performance, new functionality, and the associated impact on the cloud infrastructure. The public cloud is always developing services and enhancing infrastructure ahead of the curve, and is likely already supporting large Big Data platforms. And it can change the way Big Data platforms are developed—For example, PaaS-based Hadoop allows developers of Big Data platforms more flexibility by eliminating hardware considerations altogether. Because of this, the public cloud offers evergreen services so that existing Big Data platforms can adapt and advance into more complex and rich data collection and analysis faster, without considering impacts to infrastructure.
Accessibility: Like the availability of data collection, the ability of data scientists, developers, and collectors to access public cloud services is generally better than with a private cloud, which reduces the number of issues related to accessibility and unique requirements such as a VPN, etc. It simplifies the input and output from the platform, and helps teams organize in ways that make them more effective, versus being subject to what the cloud will or will not support.
Scale: Private clouds can scale, but not on-demand. The public cloud has the ability to scale storage and compute in real time. For some applications, a lag in scaling is acceptable, and can even be predicted in advance. This is not true for most Big Data platforms. And, if scale is not automatic, it can bring the entire platform to its knees, causing a cascading complete outage, or long-lasting delay in analysis. The bottom line is that Big Data platforms need to have the flexibility to scale on-demand, without having to purchase resources in advance as an insurance policy.
There is one key element to making Big Data in the cloud successful, and that is commuting your entire Big Data platform from storage to analysis in the cloud.
All-in does not mean lock-in. All-in means that your entire Big Data platform should be cloud-native. The reason? Integration of large data storage and analysis platforms across disparate clouds will likely become the primary bottleneck for advancing the platform. So parity across infrastructure and cloud is important. This does not mean that you need to build your application for a lack of portability between clouds, but this is a good design practice in any case.
The biggest argument for not moving Big Data operations to the public cloud is based on the sheer size of data. There is no question that migration of data from existing Big Data platforms is a significant task. But it’s a one-time task, and it’s on the horizon. Migration is a significant challenge, no matter the cloud. But public cloud providers such as Alibaba Cloud have invested in partnerships with Cloudsfer, created physical transport options, and have built cloud services to make it easier.
Building cloud-native Big Data platforms, like building modern applications, is the best way to ensure that the platform can sustain in its current form and offer new capabilities in the future.
For an example of how Big Data operations can run in a public cloud, take a look at this article about Big Data on Alibaba Cloud. If you want to test Alibaba Cloud yourself, you can take advantage of $300 in free credits.