Sql Query Challenges

The development of the economy has led to the emergence of software and computing power service infrastructure, commonly known as cloud services or cloud computing.

Large data will challenge the enterprise's storage architecture and data center infrastructure, and will trigger the ripple effect of cloud computing, data Warehouse, data mining, business intelligence and so on. In 2011, companies will use more TB (1TB=1000GB) Datasets for business intelligence and Business Analytics, and by 2020 global data usage is expected to rise 44 times-fold to 35.2ZB (1zb=10 billion TB). The challenges of large data for the vast number of data information, how the complex application of these data into the current data warehousing, business intelligence and data analysis technology ...

Recently, in the Sybase IQ 15.4 Media activities, CSDN United several technical media jointly interviewed Sybase China technical director Lu Dongming. Lu Dongming shared views on the impact of large data on traditional database vendors, the comparison of column-and row-style databases, and other hot topics. Sybase China technical director Ludoming Ludomin first introduced SAP's 5 major database products: Sybase re-use Server enterprise abbreviation ASE (line database) Sy ...

The development of any new technology will undergo a process from the public to the final universal application. Large data technology as a new data processing technology, after nearly a decade of development, has just begun to be applied in various industries. But from the media and public view, the big data technology always has the mysterious color, appears to have the magical power which digs the wealth and forecasts the future. Widely circulated large data applications include the target supermarket based on the girl's shopping history to determine whether pregnancy, credit card companies based on the user in different time and space shopping behavior to predict the customer's next purchase behavior, and so on. Large Data Technology ...

The use of large data has been far less than the ability to collect large data, the main reason is that the current enterprise data mainly dispersed in different systems or organizations, the key to the big data strategy is to be able to more in-depth, richer mining all the data system of valuable information, so more accurate prediction of customer behavior, find business value, 　　However, it is difficult to move this data to a separate data store, and security and regulatory issues are not guaranteed, Oracle Big Data SQL launched to solve the current challenges. The following is a translation:

In January 2014, Aliyun opened up its ODPS service to open beta. In April 2014, all contestants of the Alibaba big data contest will commission and test the algorithm on the ODPS platform. In the same month, ODPS will also open more advanced functions into the open beta. InfoQ Chinese Station recently conducted an interview with Xu Changliang, the technical leader of the ODPS platform, and exchanged such topics as the vision, technology implementation and implementation difficulties of ODPS. InfoQ: Let's talk about the current situation of ODPS. What can this product do? Xu Changliang: ODPS is officially in 2011 ...

Storing them is a good choice when you need to work with a lot of data. An incredible discovery or future prediction will not come from unused data. Big data is a complex monster. Writing complex MapReduce programs in the Java programming language takes a lot of time, good resources and expertise, which is what most businesses don't have. 　　This is why building a database with tools such as Hive on Hadoop can be a powerful solution. Peter J Jamack is a ...

Storing them is a good choice when you need to work with a lot of data. An incredible discovery or future prediction will not come from unused data. Big data is a complex monster. In Java? The programming language writes the complex MapReduce program to be time-consuming, the good resources and the specialized knowledge, this is the most enterprise does not have. 　　This is why building a database with tools such as Hive on Hadoop can be a powerful solution. If a company does not have the resources to build a complex ...

The big figures, which emerged in 2011 and soar in 2012, may change many aspects of data management in a dramatic way. Large data systems have brought about changes in the management and manipulation of computer data, continuous extraction, transformation and loading functions, operational business intelligence, dynamic large data, and cloud-based data warehouses. However, with large data entering the 2013, there is no system technology more active than the NoSQL database and Hadoop framework, it seems that these two products have more room for development. According to the marketanalysis ....

Big data has grown rapidly in all walks of life, and many organizations have been forced to look for new and creative ways to manage and control such a large amount of data, not only to manage and control data, but to analyze and tap the value to facilitate business development. Looking at big data, there have been a lot of disruptive technologies in the past few years, such as Hadoop, Mongdb, Spark, Impala, etc., and understanding these cutting-edge technologies will also help you better grasp the trend of large data development. It is true that in order to understand something, one must first understand the person concerned with the thing. So, ...