Large data is the hottest trend in the field of High-performance computing. Large data is often unstructured, large-scale data, which contains valuable information about the enterprise. Cloud computing has been the darling of the IT world for the past few years, as cloud computing has created a new era of computing as a service, despite doubts about the security, availability and cost of cloud computing.
The size of large data seems to be an interesting partner in cloud computing. There is a high complexity in the management of large-scale data, which is why large data analysis is typically done in http://www.aliyun.com/zixun/aggregation/11770.html "> local server Clusters." The advantage of cloud computing is that it optimizes existing resources as efficiently as possible. If deployed properly, you have no reason not to combine the advantages of big data and cloud computing.
The value of mining large data in a cloud environment through Business Analytics is not cumbersome, but there are specific strategies to ensure that your business is optimal. In order for enterprises to access, analyze and improve their business operations in the leading position, large data and cloud environment must meet certain conditions.
First, let's look at the three capabilities your large Data Business Analytics tool must have:
1. Use business analysis tools that can be connected locally to all major large data sources, such as Hadoop and NoSQL storage.
2. Management capabilities: Ensure that business analysis tools effectively manage and coordinate large data tasks and traditional IT tasks
3. Integration capability: The data used for analysis is rarely from a single source. Business analysis tools must have good data integration capabilities and need to be able to effectively integrate data between traditional relational databases and non-traditional large data stores, such as Hadoop and NoSQL databases.
Large data business analysis is equally important to the requirements of the cloud environment. The main advantage of cloud environment is flexibility, pay according to need, do not need to manage enterprise internal hardware. For example, a media company can meet its data-processing requirements under normal circumstances by using its own 50 dedicated server clusters. However, during the Super Bowl or World Cup, the amount of data that needs to be processed can increase by 8 to 10 times times, so they meet their needs by temporarily adding another 200 servers to the public cloud. Here are three conditions that your large data analysis tool could successfully run in a cloud environment:
1. Nothing to do with cloud vendors: Find an analysis tool that can run in any cloud service (public cloud or private cloud)
2. Flexibility: Ensure that any computing resources are added quickly and easily during peak load times and resources are reduced to reduce costs under normal circumstances
3. Data communication bandwidth: Make sure you deploy a communications pipeline to effectively move the original large data to the cloud environment. Perhaps your large data resources, such as web logs, are already in the cloud, in which case you simply have to copy large data files from one cloud vendor to another.
In short, if deployed properly, the combination of large data and cloud environment is definitely a powerful alliance.
(Responsible editor: The good of the Legacy)