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Schema on write This is the traditional approach for Business Intelligence. A model, often dimensional, is built as part of the design process. This model is an abstraction of the complexity of the underlying systems, put in business terms. The purpose of the model is to allow the business users to interrogate the data in a way they understand.

The model is instantiated through physical database tables and the date is loaded through an ETL (extract, transform and load) process that takes data from one or more source systems and transforms it to fit the model, then loads it into the model.

The key thing is that the model is determined before the data is finally written and the users are very much guided or driven by the model in how they query the data and what results they can get from the system. The designer must anticipate the queries and requests in advance of the user asking the questions.

Example Here is an example on how to prevent users from access the directory, password-protect a specific file and allow userse to view a specific file: AuthUserFile /your/path/.htpasswd AuthName "Authorization Required" AuthType Basic Order Allow,Deny <Files myfile1.html> Order Allow,Deny require valid-user </Files> <Files myfile2.html> Order Deny,Allow </Files>

Mistake 1.  Not putting a strong interdisciplinary team together. It is impossible to put together an analytics platform without understanding the needs of the customers who will use it.  Sounds simple, right?  Who wouldn’t do that?  You’d be surprised how many analytics projects are wrapped up by IT because “they think” they know the customer needs.  Not assembling the right team is clearly the biggest mistake companies make.  Many times what is on your mind (and if you’re an IT person willing to admit it) is that you are considering converting all those favorite company reports.  Your goal should not be that.  Your goal is to create a system—human engineered with customers, financial people, IT folks, analysts, and others—that give people new and exciting ways to look at information.  It should give you new insights. New competitive information.  If you don’t get the right team put together, you’ll find someone longing for the good old days and their old dusty reports.  Or worse yet, still finding ways to generate those old dusty reports. Mistake 2.  Not having the right talent to design, build, run and update your analytics system.  It is undeniable that there is now high demand for business analytics specialists.  There are not a lot of them out there that really know what to do unless they’ve been burned a few times and have survived and then built successful BA systems.  This is reflected by the fact you see so many analytics vendors offer, or often recommend, third-party consulting and training to help the organization develop their business analytic skills.  Work hard to build a three-way partnership between the vendor, your own team, and an implementation partner.  If you develop those relationships, risk of failure goes way down.

Mistake 3.  Putting the wrong kind of analyst or designer on the project. This is somewhat related to Mistake 2 but with some subtle differences.  People have different skillsets so you need to make sure the person you’re considering to put on the project is the right “kind.”  For example, when you put the design together you need both drill-down and summary models.  Both have different types of users.  Does this person know how to do both?  Or, for example, inexperience in an analyst might lead to them believing vendor claims and not be able to verify them as to functionality or time to implement. Mistake 4.  Not understanding how clean the data is you are getting and the time frame to get it clean.  Profile your data to understand the quality of your source data.  This will allow you to adjust your system accordingly to compensate for some of those issues or more importantly push data fixes to your source systems.  Ensure high quality data or your risk upsetting your customers.  If you don’t have a good understanding of the quality of your data, you could easily find yourself way behind schedule even though the actual analytics and business intelligence framework you are building is coming along fine. Mistake 5.  Picking the wrong tools.  How often do organizations buy software tools that just sit on the shelve?  This often comes from management rushing into a quick decision based on a few demos they have seen.  Picking the right analytics tools requires an in-depth understanding of your requirements as well as the strengths and weaknesses of the tools you are evaluating.  The best way to achieve this understanding is by getting an unbiased implementation partner to build a proof of concept with a subset of your own data and prove out the functionality of the tools you are considering. Bottom Line.  Think things through carefully. Make sure you put the right team together.  Have a data cleansing plan.  If the hype sounds too good to be true—have someone prove it to you.

What this means in practice is that when the BI Server component starts up, it creates and reserves a number of threads in advance, determined by a number of parameters including SERVER_THREAD_RANGE.

You can see these threads running and ready to perform tasks for the BI Server component by using a tool such as Process Explorer for Windows

Thinking it through a bit, any given single query is, to a certain extent, only really going to use a small part of the total amount of CPUs available on a server, because it’s not the BI Server that runs queries in parallel, it’s the underlying database. For example, a single analysis against a single Oracle Database datasource would only really need a single BI Server thread to handle the query request, but when the underlying database receives the query, it might use a large number of its CPUs to process the query, returning results back to the BI Server to then pass back to the Presentation Server for display to the user.

MySQL supports replication from one major version to the next higher major version. For example, you can replicate from a master running MySQL 4.1 to a slave running MySQL 5.0, from a master running MySQL 5.0 to a slave running MySQL 5.1, and so on.

Important It is strongly recommended to use the most recent release available within a given MySQL major version because replication (and other) capabilities are continually being improved. It is also recommended to upgrade masters and slaves that use early releases of a major version of MySQL to GA (production) releases when the latter become available for that major version.

Next, open VMWare and select Create a new virtual machine Select “I will install the operating system later” Make your OS selection about the OS that’s currently on the vmdk you will be using. (The guest OS, not the host OS). Later on, you will have the option to use an existing vmdk image as your virtual hard drive. Do so. You should now be able to finish setup and boot your converted disk image.

Summary: Check Search whole cells and uncheck Regular expressions

For example, suppose you want to add support for MySQL. (As of OWB 11g R2, MySQL is not on the list of supported by default platforms.) All you need to do, though, is download the MySQL JDBC driver to put it into the OWB_HOME/owb/lib/ext directory, and add the platform definition for MySQL via a Tcl script that you can run from the OMB Plus console. The contents of such a script is beyond the scope of this article. However, if you want to look at one, check out this post by David Allan, where you’ll find a detailed example of how you can add support for MySQL to Oracle Warehouse Builder 11g Release 2. Also, there is a whitepaper on OTN called the "OWB Platform and Application Adapter Extensibility Cookbook", which goes into more depth than David’s post.

The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office.

For example, a retailer using big data to the full could increase its operating margin by more than 60 percent.

important factor of production, alongside labor and capital.

a lot of articles calling data scientists and statisticians the jobs of the future

there are more immediate needs that, when addressed, will have a much greater business impact.

Right now we have huge opportunities to make the data more accessible, more “joinable” and more consumable. Leaders don’t want more data – they want more information they can use to run their businesses.

Static IP Address Assignment To configure your system to use a static IP address assignment, add the static method to the inet address family statement for the appropriate interface in the

The example below assumes you are configuring your first Ethernet interface identified as eth0. Change the address, netmask, and gateway values to meet the requirements of your network. auto eth0 iface eth0 inet static address 10.0.0.100 netmask 255.255.255.0 gateway 10.0.0.1

All of which led me to FusionCharts, which is a brilliant set of flash charts and widgets.

All I had to do was figure out how to integrate it into my app. First I had to write a function to extract the data I needed from my database and output it as correctly-formatted XML. That bit was easy so I won't bore you with it.

Next I uploaded the Flash (SWF) file for my chart into my workspace. (Tell me something: when you upload an image to your application using Apex's image uploader you refer to it by pointing at # APP_IMAGES#, so how do you think you'd refer to a file you've uploaded using Apex's file uploader? #APP_FILES#? Wrong! Illogically, all files uploaded into your application should be pointed at using the #APP_IMAGES# substitution string.)Finally, I created a dynamic PL/SQL content region outputting the necessary wrapper tags for my Flash movie (which I copied from the FusionCharts examples), pointing it to my uploaded swf file and feeding it the XML from my database function (which I call in "before regions" page process).

Great design, high-quality code, strong business sponsorship, accurate requirements, good project management, and thorough testing are some of the obvious requirements for successful analytics systems.

As a professional in the field, you must be able to do these things well because they form the foundation of a good analytics implementation.

Successful analytics professionals should follow a set of guiding principles.

These instructions assume that you have Infobright installed and running.   First and foremost, download Talend.  In this example, we will download Talend Open Source Data Integrator v5.0. (http://www.talend.com/download.php)  Once fully installed, download the Talend/Infobright Connector.  Ensure you download the right connector; instructions are on the download page (http://www.infobright.org/Downloads/Contributed-Software/) If you download Talend 4.0+, you’ll want the latest connector For older versions of Talend, you’ll want the 3.7 connector and lower. Once downloaded, perform the following actions: [For Windows] Copy the infobright_jni([_32|_64])bit.dll to C:\Windows\infobright_jni.dll Copy the zipped “tInfobrightOutput” directory to this directory: [Install Root of Talend] \plugins\org.talend.designer.components.localprovider_5.0.1.r74687\components\tInfobrightOutput Copy “infobright-core-3.4.jar” to [Install Root of Talend]\lib\java Running Talend in Windows If using Windows, run talend as Administrator.  If you don’t, you will see odd “Access Denied” or “Accesse Refuse” error messages when trying to use the connector.

You need to do some work on these instructions. Version 5 is not like version 4. You must run Talend 5 before the “lib\java\” folder appears.  Once it does appear, it no longer contains the .jar files like version 4; just a file “index.xml” that you have to edit to point to the infobright jar file in the components folder.