Jessica Qiu

Subscribe to Jessica Qiu: eMailAlertsEmail Alerts
Get Jessica Qiu via: homepageHomepage mobileMobile rssRSS facebookFacebook twitterTwitter linkedinLinkedIn

Top Stories by Jessica Qiu

The data computation layer in between the data persistent layer and the application layer is responsible for computing the data from data persistence layer, and returning the result to the application layer. The data computation layer of Java aims to reduce the coupling between these two layers and shift the computational workload from them. The typical computation layer is characterized with below features: Ability to compute on the data from arbitrary data persistence layers, not only databases, but also the non-database Excel, Txt, or XML files. Of all these computations, the key is the computation on the commonest structured data. Ability to perform the interactive computations among various data sources uniformly, not only including the computation among different databases, but also calculation between the databases and non-database data sources. The coupling... (more)

Data Environments Support of esProc Makes Statistical Computing More Flexible

Enterprises always have various data sources, for instance, CRM system may use SQL Server, sales reports adopt Excel, ERP applies Oracle database. When it comes to actual business analysis, enterprises usually need to conduct interactive computation, including filter, group, etc among various data environments. But data Interaction between multiple data sources are not easy to realize with some traditional statistical computing tools. In order to solve such kind of problems, esProc which adapts to various data environments comes into being. Support of various data sources is an... (more)

Database to Implement Big Data Real-Time Application

The Big Data Real-time Application is a scenario to return the computation and analysis results in real time even if there are huge amounts of data. This is an emerging demand on database applications in recent years. In the past, because there wasn't a lot of data, the computation was simple, and few parallelisms, the pressure on the database wasn't great. A high-end or middle-range database server or cluster could allocate enough resources to meet the demand. Moreover, in order to rapidly and parallel access to the current business data and the historic data, users also tended t... (more)

Will Big Data Fall into the Pitfall of Failure?

Recently, I read "Why Big Data Projects Fail" by Stephen Brobst. I can’t agree more with his opinions which exposed the problem I’ve been worried about. In this article, I am going to further discuss this topic to remind the enterprises to beware of falling into such pitfall of failure. t Let’s have a look on a positive example. As a successful enterprise in leveraging big data, how does Google make use of the big data? 1. Collect the row data, capture the contents of each website, e-mail, or Cookie, and extract the key information. 2. Create the complex syndetic index for this inf... (more)

Business Intelligence Suppliers: Are You Ready for 2013?

With the ever-changing economy, the business intelligence landscape is also transforming. According to some predictions, the worldwide spending on IT will continuously increase in 2013 and even at the next few years. The growth drivers may include data visualization, big data, speed, agility, self-service, cloud computing, etc. Therefore, the BI suppliers who have these features will be more competitive compared with their rivals. Business growth turns BI into a daily business processes. Thus demands for new generation BI software are increasing and agility one requirement for t... (more)