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This article mainly shows you "how to use Hadoop to reduce the analysis cost of big data", the content is easy to understand, clear, hope to help you solve your doubts, the following let the editor lead you to study and learn "how to use Hadoop to reduce the cost of big data analysis" this article.

Big data will become a new generation of hot topic to replace cloud computing. This is the inevitable result: with the passage of time, the amount of data generated by enterprises has become larger and larger, including customer purchase preference trends, website visits and habits, customer review data, and so on; so how can such a large data set be organized into a comprehensive form? Traditional business intelligence (BI) tools (relational databases and desktop math packages) are a little inadequate when dealing with huge amounts of enterprise data. Of course, the data analysis industry also has development tools and frameworks that support data researchers and analysts to mine large data sets and can withstand the information load.

For larger companies, massive data processing is nothing new. For example, Twitter and LinkedIn are already famous users of big data. The two companies have each developed a clear set of competitive advantages, identifying trends by mining their large-scale data warehouses. So what about CIO for medium-sized enterprises? Fortunately, there are available tools at hand.

One of these tools is free, the Apache Hadoop programming framework based on Java. The framework has gained a huge market in the big data field in the past year to a year and a half. Industry experts and users around the world call Hadoop the de facto data mining standard. Looking at the performance of other existing big data products, and considering the fact that the Apache Hadoop1.0 version was released at the end of November 2011, it is surprising that Hadoop has received such recognition. Hadoop is so popular that CEO Eric Baldeschwieler, a Hortonworks company, predicts that it will process as much as half of the world's data by 2017. In the coming year, there is a good chance that Hadoop will be close to your organization in some way.

Hadoop is aimed primarily at developers. Its main framework, MapReduce, supports programmers to deal with large amounts of data in distributed computer clusters. The disadvantage is that it is a very heavy product. Moreover, Hadoop can distinguish the technical people who directly operate the data warehouse from the data consumers and data translators.

Given the budget constraints of midsize CIO, here are some suggestions to help overcome the challenge of huge amounts of data:

Don't ignore the trend. Big data will not disappear, and the ability to analyze and transform large blocks of data and the trend of analyzing data cannot be ignored. Take some time to understand the function and structure of Hadoop and other big data products. Think about the way you have data that can improve your company.

Find budget space for qualified data scientists. These people are percussion instruments for your BI symphony. There is a shortage of qualified data scientists on the market. Even at the Hadoop World Congress in November last year, training became a big topic. Use the free quota of your training budget to hire people and maintain their data analysis skills.

Understand the storage tips for a large number of datasets. Big data actually excavates huge amounts of data from multiple places and databases at an almost real-time speed without being hampered by structure. This complicates the way storage works in your infrastructure. Can cloud storage be more flexible and agile for these slave tables? Work with your data mining strategy team to give priority to understanding the type and quantity of storage requirements that take advantage of Hadoop processing power.

Get ready to use Hadoop's toolset. Understand Microsoft's presence in this area and experiment with Hadoop-Excel and Hadoop-SQL Server integration to see what kind of results you can deliver. Also learn about IBM's tools to see which one is more suitable for your existing investment in desktop and end-user software.

The competition for big data has already begun. Maybe you've fallen behind in the data mining revolution. CIO, who ignore the trend of data analysis, are actually risking their careers. However, for the CIO who have jumped into big data's field and extracted key insights, the whole world will be in their hands.

The above is all the contents of the article "how to use Hadoop to reduce big data's analysis cost". Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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