How can we combine big data with cloud computing?

How do you find treasures in the ocean of big data? For enterprises that have massive data information to process, the first obstacle to be overcome before data analysis is the inability to locate relevant and meaningful information.

How do you find treasures in the ocean of big data? For enterprises with large amounts of information to process, the first obstacle to be overcome before data analysis is the inability to locate relevant and meaningful information. This is also a problem with hgst. hgst (Hitachi global storage technology company) is a major manufacturer of computer hardware and they urgently need to track the data collected on production facilities. According to David Hinz, hgst's cloud computing and High Performance Solutions Director, finding the required data to evaluate the production process is a long or impossible task.

"We cannot determine where it is, who owns it, and how to get it," Hinz said. "For hgst, we want to improve performance so that it can get data in a timely manner, and enables the team to quickly find it. Then, we can go back and analyze and find out relevant insights to help us improve manufacturing processes, improve customer response, and promote products ." By centralizing and streamlining data on the cloud, you can retrieve the desired information in minutes instead of weeks. It turns out that this company is wise to set up a hive data warehouse on the cloud.

How can we combine big data with cloud computing?

Most of today's data is unstructured, and most of this type of data is generated by users. Ben Butler, Senior Manager of AWS big data and high availability, commented: "It is easier to generate data and the cost is lower. However, it puts pressure on the rest of the life cycle: collection, storage, analysis, and computing, so that you can understand the meaning of the rapidly growing data ."

Today, it is not surprising that large enterprises with Pb-level data and streaming media have always been included. The explosive growth of available information means that there will be an increasing gap between data that can be collected and analyzed at a high speed. "Big data, the amount of data you have, the types, and the growth rate all require new tools," Butler said. "on the cloud, we combine different computing, network, and storage tools, you can solve these problems."

Unlock the secrets of big data

The elasticity and On-Demand configuration provided by cloud computing provides core strength for enterprise organizations to experiment with and try new methods to solve big data. Enterprises can try and manipulate their data in different iterative ways based on the supplied infrastructure. Infrastructure no longer limits what to use to process data. With the same flexibility, enterprises will not overhead even with high variable loads.

Scalability can also reduce the waiting time for data processing as needed, so that less competition for resources will be made. Access to unlimited resources allows enterprises to make better decisions when using AWS solutions. For example, EMR or other solutions are embedded in the kinesis background. near real-time analysis can be performed within several seconds by passing visualized results to the Bi system for display.

Big data processing is a challenge. Without strong hardware to support such data, it is almost impossible to perform meaningful analysis on it. Fortunately, the cloud computing explosion and the ability to use processing elastically mean that small enterprise organizations can now complete tasks that can only be completed by large enterprises. Cloud computing has made big data easier to manage and can perform more and faster analysis on the data. As big data continues to grow, larger cloud computing will be there for processing.

How can we combine big data with cloud computing?