Big Data Tools

The following small series summarizes 10 best data mining tools for everyone, which can help you analyze big data from various angles and make correct business decisions through data.

The big data field has boomed in recent years. As a segment of big data, education big data has great potential to promote educational change. Big data technology is rapidly evolving, which opens up new possibilities for big data applications.

"Editor's note" This article is reproduced in the public number "Fu Zhihua", the author has been Tencent social networking business Group data center director and Tencent Data Association president.     Before Tencent, he worked in the market consulting, Analysys International, China Internet Association, and served as vice president of Dcci Internet Data Center. The internet industry in the accumulation of large data and applications to Baidu, Tencent and Alibaba is the most noteworthy. Baidu, Tencent and Alibaba in the application of large data although there is a common place, but due to their respective data sources and ...

As John Ginder, a big data director at Ford, and other business executives have described, the industry lacks simple tools to solve big data problems. "We are moving towards the great end of the big data. John Ginder said in an interview with the media, "although Ford has experts to develop tools and specific problems for some special applications, but in the face of the future of all the data will be excavated, we do not have enough staff and tools." Data experts, not computer scientists, should review the data and find out ...

Innovative report from the dean of top research institutes when all our actions and our lives can be "data", the company that holds the data is like owning a rich gold mountain. 　　As Victor Maire-Schoenberg says, the big data age is "the future that has happened," and in this already-occurring future, there is no bystander. December 19, 2013, by the boutique media "digital Business Times" sponsored by the "Great data creation of the era of Subversion" 2013 China Summit Forum in Beijing to make a grand Holiday Crowne Plaza Hotel ...

Now Apache Hadoop has become the driving force behind the development of the big data industry. Techniques such as hive and pig are often mentioned, but they all have functions and why they need strange names (such as Oozie,zookeeper, Flume). Hadoop has brought in cheap processing of large data (large data volumes are usually 10-100GB or more, with a variety of data types, including structured, unstructured, etc.) capabilities. But what's the difference? Today's enterprise Data Warehouse ...

The Data Platform Business Unit first used not the MaxCompute (original ODPS) currently in use, but Hadoop. The original Hadoop cluster was named Cloud Ladder 1. At that time, Alibaba was also developing its own computing platform, which was the original ODPS, and named it. Cloud Ladder 2.

We have all heard the following predictions: By 2020, the amount of data stored electronically in the world will reach 35ZB, which is 40 times times the world's reserves in 2009. At the end of 2010, according to IDC, global data volumes have reached 1.2 million PB, or 1.2ZB. If you burn the data on a DVD, you can stack the DVDs from the Earth to the moon and back (about 240,000 miles one way). For those who are apt to worry about the sky, such a large number may be unknown, indicating the coming of the end of the world. To ...

Now Apache Hadoop has become the driving force behind the development of the big data industry. Techniques such as hive and pig are often mentioned, but they all have functions and why they need strange names (such as Oozie,zookeeper, Flume). Hadoop has brought in cheap processing of large data (large data volumes are usually 10-100GB or more, with a variety of data types, including structured, unstructured, etc.) capabilities. But what's the difference? Today's enterprise data warehouses and relational databases are good at dealing with ...

Now Apache Hadoop has become the driving force behind the development of the big data industry. 　　Techniques such as hive and pig are often mentioned, but they all have functions and why they need strange names (such as Oozie,zookeeper, Flume). Hadoop has brought in cheap processing of large data (large data volumes are usually 10-100GB or more, with a variety of data types, including structured, unstructured, etc.) capabilities. 　　But what's the difference? Enterprise Data Warehouse and relational number today ...