BI-TAGS

One of the most challenging aspects of performance tuning is knowing where to begin. To maximize Oracle® Business Intelligence Enterprise Edition performance, you need to monitor, analyze, and tune all the Fusion Middleware / BI components. This guide describes the tools that you can use to monitor performance and the techniques for optimizing the performance of Oracle® Business Intelligence Enterprise Edition components.

In this lesson you will use PowerPivot to create and manage a measure and a Key Performance Indicator. A measure is a formula that is created specifically for use in a PivotTable (or PivotChart) that uses PowerPivot data. Measures can be based on standard aggregation functions, such as COUNT or SUM, or you can define your own formula by using DAX. For more information about measures, see Measures in PowerPivot.

Free reporting software Now enhanced for mobile intelligence Perfect solution for departments Scalable as your needs expand For Windows, Unix, Linux, Solaris, HP-UX, and AIX operating systems and any data source, including Hadoop, SAP BW, Microsoft Analysis Services, Essbase, and IBM TM1.

Simple development and maintenance of Mobile apps and dashboards Powerful Visual Data Discovery capabilities Packed with robust analytics Free online support and training Perpetual license to use forever Quick Start Guide brings you from download through your first report

In the contemporary enterprise, almost everyone will need to have data-science skills of some kind.

“When most people think of a data scientist, they think of a statistician, a guy with ‘analyst’ in his title,’” Hanrahan says. “Or, someone who works in IT and manages the data warehouses. To do these jobs, you certainly needed programming skills; you probably needed advanced statistics skills, or some combination of those skills.”

“At the most basic level, you are a data scientist if you have the analytical skills and the tools to ‘get’ data, manipulate it and make decisions with it,” he says.

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.

The greatest benefit of columnar is to avoid disk I/O.  By choosing “select *”, you run the risk of losing that benefit. 

BI tools are here to stay, and they really help make visualization of analytics easy.  When working with Infobright, always take an extra second to review the generated queries.  The extra few seconds could mean seconds or minutes in saved query times.

Queries time

InfoBright was fully 1 CPU bound during all queries.

relational database management systems (DBMSs) used as platforms for data warehouses

It is important to note that a DBMS does not in itself constitute a data warehouse — rather, a data warehouse can be deployed on a DBMS platform.

a data warehouse is simply a warehouse of data, not a specific class or type of technology

Integration BI infrastructure: All tools in the platform use the same security, metadata, administration, portal integration, object model and query engine, and should share the same look and feel. Metadata management: Tools should leverage the same metadata, and the tools should provide a robust way to search, capture, store, reuse and publish metadata objects, such as dimensions, hierarchies, measures, performance metrics and report layout objects. Development tools: The platform should provide a set of programmatic and visual tools, coupled with a software developer's kit for creating analytic applications, integrating them into a business process, and/or embedding them in another application. Collaboration: Enables users to share and discuss information and analytic content, and/or to manage hierarchies and metrics via discussion threads, chat and annotations.

Information Delivery Reporting: Provides the ability to create formatted and interactive reports, with or without parameters, with highly scalable distribution and scheduling capabilities. Dashboards: Includes the ability to publish Web-based or mobile reports with intuitive interactive displays that indicate the state of a performance metric compared with a goal or target value. Increasingly, dashboards are used to disseminate real-time data from operational applications, or in conjunction with a complex-event processing engine. Ad hoc query: Enables users to ask their own questions of the data, without relying on IT to create a report. In particular, the tools must have a robust semantic layer to enable users to navigate available data sources. Microsoft Office integration: Sometimes, Microsoft Office (particularly Excel) acts as the reporting or analytics client. In these cases, it is vital that the tool provides integration with Microsoft Office, including support for document and presentation formats, formulas, data "refreshes" and pivot tables. Advanced integration includes cell locking and write-back. Search-based BI: Applies a search index to structured and unstructured data sources and maps them into a classification structure of dimensions and measures that users can easily navigate and explore using a search interface. Mobile BI: Enables organizations to deliver analytic content to mobile devices in a publishing and/or interactive mode, and takes advantage of the mobile client's location awareness.

Analysis Online analytical processing (OLAP): Enables users to analyze data with fast query and calculation performance, enabling a style of analysis known as "slicing and dicing." Users are able to navigate multidimensional drill paths. They also have the ability to write back values to a proprietary database for planning and "what if" modeling purposes. This capability could span a variety of data architectures (such as relational or multidimensional) and storage architectures (such as disk-based or in-memory). Interactive visualization: Gives users the ability to display numerous aspects of the data more efficiently by using interactive pictures and charts, instead of rows and columns. Predictive modeling and data mining: Enables organizations to classify categorical variables, and to estimate continuous variables using mathematical algorithms. Scorecards: These take the metrics displayed in a dashboard a step further by applying them to a strategy map that aligns key performance indicators (KPIs) with a strategic objective. Prescriptive modeling, simulation and optimization: Supports decision making by enabling organizations to select the correct value of a variable based on a set of constraints for deterministic processes, and by modeling outcomes for stochastic processes.

SiSense’s survey finds that salaries for data professionals are on the rise across all geographies. The annual earnings of a data professional can range from an average of $55,000 USD for a data analyst to an average of $132,000 for VP Analytics. As many as 61% of the survey respondents reported higher earnings in 2012 compared to 2011, and only 12% reported lower earnings.

Other highlights of the survey findings include: Data professionals are highly educated. 85% of the respondents have some college degree, 39% have a Master’s degree, and 5% are Ph.D.’s. Those with doctoral degrees earn on average 65% more than those with Master’s degrees, who in turn earn 16% more than those with Bachelor’s degrees. On the job experience is even more important than education in determining salary levels. On average, professionals with ten or more years of experience earn 80% more than those with 3 years or less.

At the same time, the survey shows that those with 6 years or less make up as much as 59% of the data profession workforce.