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 sources cover the result set of SQL queries or stored procedures, and
the 2D table from the text or Excel files. Owing to the technical competence
or versioning, various reporting tools may only support a single data source,
such as JasperReport, Quiee, BIRT, and Crystal Report.
With years of experience, I've concluded several methods to share and discuss
Virtual Data Source for JasperReport Commercial Server
The Virtual Data Source provided in the JasperReport business edition can
solve this problem. With wizard, one virtual table can be created by joining
two ent... (more)
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)
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.
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)
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