When does the enterprise's "big data" put into "cloud"?

In a world of big data, cloud computing plays an important role, especially in short-term jobs and applications, because much of the data in these areas is already in the cloud.

For most people, cloud computing is a big, fuzzy, and a little distant dream. Some people see big data strategies as equivalent to putting big data in the clouds, but is it really visionary or just a simple repetition of an industry meeting view?

In fact, large data and cloud computing have a lot of overlap and intersection, so it is very clear that companies are using internally deployed Hadoop, NoSQL or Enterprise Data Warehouse environment for cloud based large data strategy. It is also a reminder that cloud computing is widely considered to include other than "private deployments" or instead of a public cloud, SaaS, and multi-tenant hosting environment.

But if you limit the definition of cloud computing to a public subscription service, you will have a core problem: deciding which large data applications are better suited to the public cloud/saas environment or traditional internal deployments. In other words: the scalability, resilience, effectiveness, and cost-effectiveness and reliability of large data can be improved by the management of external service providers. Here are four use cases for large data migrations to the cloud to help you identify whether your big data is in the clouds.

Enterprise applications are hosted in the cloud for many businesses, especially small and medium-sized enterprises, if you are using a cloud-based application provided by an external service provider, most of the transaction source code in the enterprise is already in the public cloud. If you have a lot of historical data on the cloud computing platform, you may have accumulated a large amount of data.

At this point, using the data analysis value-added services provided by external service providers or their partners is more effective than relying solely on the resources within the enterprise, including customer churn analysis, marketing optimization, or offsite customer data backup and archiving services.

High-capacity external data sources require preprocessing

For example, if you're collecting social networks to monitor your customers ' emotions, you may not have servers, storage, and bandwidth within your organization to fully monitor this data, but with large data services based on the public cloud, you can simply use social media filtering services to monitor your customers ' mood changes.

An unsustainable strategic application within an enterprise

If there is already an application based large data platform within the enterprise, such as the high capacity ETL unstructured data source for dedicated Hadoop clusters, it is more feasible to solve new applications through public cloud, for example, multi-channel marketing, social media analysis, geo-spatial analysis, query archiving and elastic data. For applications that are not suitable for the current platform, an on-demand service is the best cost effective.

In fact, the public cloud is the only viable solution if companies want to deal with large data on petabytes of multiple structures as quickly as possible.

Analyze the elastic configuration of the sandbox

Cloud computing may be the only viable or affordable option if you need data to explore a shorter life cycle and order volume. You can quickly configure cloud-based storage and computing capabilities for your project, and organizations can quickly cancel these configurations when the project ends. This model can be called a "bubble market" deployment model, which can be said to be customized for cloud cover.

As long as you encounter any of these situations, the big data strategy based on cloud computing will follow, as cloud computing and the maturity of large data services, price, performance, scalability, flexibility and manageability will be improved, but the problem still exists, but the stage is different. In a few years, with more and more applications and data migrating to the public cloud, it seems impractical to use your current server to run your large data applications. So enterprises need to understand their own large data strategy, but also to find out whether the enterprise data is already in the cloud, and timely adjust the strategy.

(Responsible editor: Fumingli)