OBIEE technical stuff!

Paper.li

This week’s OBIEE News is out – edition of 26th May

Stories from blog.3sixty-analytics.com, siebel-essentials.com and thatjeffsmith.com

Videos such as: WEBI to OBIEE / BI Publisher conversion tool

https://www.youtube.com/watch?v=-KoQV8pVOQI

and OBIEE Bulk Scheduler

https://www.youtube.com/watch?v=Dd_FBAEesGU

Blogs of the week

Take a look at our blogger database. Is your blog there? If not, email us at adrian.ward@addidici.com to be added on.

1. Fixing IE for Oracle BI and Hyperion Workspace Compatibility

Christian Screen says, “You may have noticed that with both the Hyperion Workspace production and the Oracle BI portal that is does not conform to Internet Explorer’s subtle rendering tantrums, that is incompatibility. This post quickly takes a look at the solution that you can use either for Oracle EPM or Oracle BI to ensure that any IE browser, including when trying mobile compatibility with the Microsoft Surface, will work for your end users. If you are interested in Fixing IE for Oracle BI and Hyperion Workspace compatibility then this is a good article for you to read.”

He then goes through the problem and the solution.

2. OBIEE 11g Concepts (IV) – What are Views? Which Views are Supported?

Pravin Khadakkar asks, ‘What are Views?’ He says, “In this blog I am covering presentation capabilities (views) of OBIEE 11g.The definitions are extracted from Oracle Standard Product documentation.”

3. Designing a Future-Proof Data Solution

Ian Abramson goes through using Design Patterns for Data Warehouse modelling techniques, tells us to consider Designing with the Future in Mind and suggests ensuring that your data warehouse is designed to support the integration of multiple data sources right from the start.

He concludes:

“Ultimately the design and development of a data warehouse requires the data architect and data modeler to look to the future. They need to anticipate data requirements and to try and define that data objects and relationships as completely as possible right from the start and you can avoid the many pitfalls of a data warehouse design by designing with the end in mind while allowing the design to evolve based on business needs.”

4. The Disadvantages of SQL Computation (I)

Raqsoft BI OLAP writes, “SQL is invented primarily to provide a method to access structured data in order to transparentise the physical storage scheme, so a lot of various types of English vocabularies and syntaxes are used in SQL to reduce the difficulty in understanding and writing it. And the relational algebra as the basic theory of SQL is a complete computation system, which can compute everything in principle. In terms of this, we certainly should use SQL to satisfy various demands for data computation.”

5. Common BI Newbie Mistakes

Lyndsay Wise names some of these mistakes as: ‘Lack of scope’ and ‘Being unwilling to take the time to evaluate internal requirements against the market’ amongst others. Her list is not an exhaustive one and she asks readers to let her know what other mistakes they’ve seen in the market.

6. What is Hadoop? – Simplified!

This article goes through:

Introduction to Hadoop
Little background of Hadoop
Framework of Hadoop processing
Where not to use Hadoop ?

and finishes by saying that in the next few articles they will explain how you can convert your simple logic to Hadoop based Map-Reduce logic. They will also take R-language specific case studies to build a solid understanding of the application of Hadoop.

7. Why are data analysts so inefficient?

This blog begins by saying, “In an attempt to produce better products, explore new markets and defeat competition, companies spend huge amounts of time and money to create data processes that empower researchers, analysts and data scientists. While these processes take many different forms, most companies still aren’t using data efficiently, and much of the time and money they spend on data is wasted. ”

Jeff Quigley then goes through:

Conveyance
Inventory
Waiting
Over-processing
Correction (errors)
OpacityHe finishes the piece by saying, “While no single framework can fully capture all the challenges associated with data efficiency, understanding these six factors will help organizations develop more productive team members and higher quality results. Over the coming weeks, we will publish a post for each of these six factors that provide detailed examples and suggestions on how to reduce their cost and impact.”

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