A recent survey of senior business executives points to some interesting facts about the state of business intelligence and knowledge management in large corporations.

www.topix.net | 7/29/10 10:18 PM

As organizations grow in scope and complexity, aggregating real-time data from numerous systems and converting that data to decision-ready information becomes increasingly challenging. When striving for Business Performance Improvement (BPI), Business Intelligence technology provides the necessary framework to gain the insight needed to lead to better decisions.

read more

soa.sys-con.com | 7/28/10 6:56 PM

GoodData is rapidly becoming a key example of the technology innovation emerging from Central Europe - and laying a bet on Europe seems to be paying off for Fidelity Growth Partners Europe , the venture and growth equity investor, which backs European entrepreneurs exclusively. It's invested $2 million in the startup, the second investment for the £100 million fund, leading an overall $6.5 million investment round. GoodData provides an on-demand business intelligence services. Other returning investors include General Catalyst, Andreessen Horowitz and Windcrest Partners. techcrunch.com | 7/28/10 4:37 PM

Quite often, the most important insights are those that issue from other people's heads, not from our companies' data marts.

www.topix.net | 7/26/10 11:20 PM

Increasingly more organizations are turning to Business Intelligence professionals to provide historical, current, and predictive views of business operations on their critical information. As a Business Intelligence consultant, your challenge is not usually the sourcing or delivery of the information, but how that information is used going forward once you have left the organization. redir.internet.com | 7/23/10 11:38 PM

In my previous article I had created linked server, to access and query a database. However, it is just the basic step to try and bring two different RDBMS systems linked together. In this article I will discuss about creating a SSIS package to transfer data from MS SQL Server 2008 to MySQL 5.5 Prerequisite: ___________________________________________________ SQL Server Business Intelligence Development Studio MySQL Connector Net 5.2.7 You can download this package from http://www.mysql.com/downloads/connector/net/ Connector/Net is a fully-managed ADO.NET driver for MySQL. MySQL Connector/ODBC 5.1 (Which is already installed, in our previous article) http://www.mysql.com/downloads/connector/odbc/ Brief about SSIS ___________________________________________________ SSIS provides a graphical front end to design control flow data processing logic.  Once designed, these ‘packages’ are compiled into ‘.dtsx’ packages which can then be distributed and run on any machine with the SSIS tools installed. Packages contain two main logic flows, a ‘Control Flow’ which defines a sequence of logical operations which are processed in sequence. Each step is completed before the next starts e.g. 1.  Empty out work tables in a database 2.  Populate the work tables with data 3.  Perform calculations and update the values in the work table 4.  Update OLAP cubes with the data from the work tables 5.  Run reports against the OLAP cubes. This level of control also allows processing loops to be defined e.g. For each file in a specified folder, read the contents of the file and write it into a specified table. The second main logic flow is the ‘Data Flow’.  This allows for the processing of data at the record level. Data is read from a ‘Data Source’ and passes down a series of ‘Data Transformations’ to a ‘Data Destination’. These transformations can be as simple as changing the data type of fields e.g. varchar(4000) to varchar(2000) or decimal(18,2) to decimal(8,2), or can be more complex like data merges, joins, pivot tables, multicasts etc. Each transformation is represented by an icon in the designer and the icons are linked together to define the logic path. Creating SSIS package to transfer data from MS SQL Server 2008 to MySQL ___________________________________________________ Follow the below steps to create a SSIS package for data transfer. Go to START>MS SQL SERVER 2008>SQL SERVER BUSINESS INTELLIGENCE DEVELOPMENT STUDIO and Click on File>New>Project Under Business Intelligence Projects, select Integration Services Project and rename the Project Name. This will open a Package Designer Screen, where you can start designing the data flow for your package. Configure Connection Managers for you package ___________________________________________________ Right Click in the area where it shows the Connection Manager tab and select New ADO.Net Connection. You will see the Configuration Manager Editor window Click New Now you need to define connection manager configuration for source (i.e. MS SQL Server 2008) in our case. In the Connection Manager Editor, by default the Provider is set to .Net Providers\SqlClient Data Provider Use your test server name or IP for Server Name and under connect to database, select the database to use. I’m using ssistest. Similarly, you need to create connection manager configuration for Destination (i.e. MySQL) Right Click, under Connection Manager’s tab and Select ADO.NET Connection as mentioned above and click New on Connection Manager Editor. For Provider click on the drop down arrow to select .Net Providers\odbc data Provider. In the previous article we had created System DSN name MySQL. We will use the same here for Use user or system data source name. Enter your login information and Test Connection. It should succeed, and then click Ok. Now, on Configure ADO.NET Connection Manager screen, you can see both the source and destination are configured. It’s now time to add control flows to the package, ensure you are on Control Flow tab and Drag Data Flow Task from the Control Flow Items under ToolBox. Now click on the Data Flow tab above and Drag ADO NET Source and ADO NET Destination as shown below. You can rename the Source and Destination Names from Properties. Right click on ADO NET SOURCE i.e. MS SQL Server 2008 and click EDIT. Make the below changes as mentioned on the screen. As mentioned before I have ssistest database which has few sample tables, that I have exported from AdventureWorks database. I’m selecting one of the tables named HumanResources.Department for this example. In the above screen you can see the test server selected with database and table to transfer the data from. Similarly right click on MySQL (Destination) and click Edit. On this screen, under ADO.NET Connection Manager select MySQL.root from the drop downlist. Since we do not have any destination table to map with the source, we need to click on New. It will show you the below message, click OK Now remove the quotes and Click OK to create a table on destination as shown. It will come back to the editor, Click on the Mappings tab and check if all the columns are mapped and click OK. This will complete the data flow design of our package. Saving the SSIS Package ___________________________________________________ Once the package is created, save the copy of it to SSIS Package store or MSDB. Follow the below steps, Click on File> Save Copy of Package.dtsx As from the menu it will open a window, Select SSIS Package Store as the Package Location and Select the name of your test server. Enter /File System/Export2MySQL as the package path and Click OK. Select File > Save Copy of Package.dtsx as again from the menu and Select SQL Server as the Package Location. Select the name of your test server and fill in your authentication information. Enter Export2MySQL as the package path and Click OK. Once the copies are saved you can see them under Integration Services Stored Packages as shown below. Changes on MySQL ___________________________________________________ Before running the Package, make this change on the MySQL Server mysql> SET GLOBAL sql_mode= ‘ANSI’ Executing the Package ___________________________________________________ You can execute the package either from SSMS and BI Development Studio. I’m doing it from the BI, on the right hand side your BI screen you will find solution explorer, which has the Package.dtsx listed. Right click and select Execute Package. The moment you click execute the debugging process start and the Output is show below on the BI screen.  If the Data Flow Task is changed to Green, it means the package has succeeded, and if it is Red it means somewhere in the flow there was an error. On the Data Flow Tab, you can see the number of rows transferred in the execution. There is a progress tab on the screen, where you can check the steps taken in the flow, it also list errors and warnings if any. Query the MySQL Server to see if the data transfer was successful as show below. This was about transferring data from MS SQL Server 2008 to MySQL, you can do vice verse by changing the source and destination while designing the package on Data Flow tab. Hope this article is useful. dbperf.wordpress.com | 7/23/10 1:34 PM

The use of analytics in contact centers enables management to extract critical business intelligence that would otherwise be lost. By analyzing and categorizing recorded conversations between companies and their customers, useful information can be discovered relating to strategy, product, process, customer satisfaction/retention, and operational issues. www.ecommercetimes.com | 7/23/10 1:00 PM

Jinfonet Software, a provider of Java reporting solutions, on Thursday unveiled JReport 10. This new version adds rich visualization and interactive reporting to a robust, agile BI platform, providing embedded operational reporting to developers and self-service reporting to end users. JReport 10 brings Agile Business Intelligence to the next level with rich visualization features. Web 2.0 self-service reporting allows highly interactive reports to be accessible across the enterprise with superior performance and scalability.

read more

java.sys-con.com | 7/22/10 4:35 PM

From balancing your checkbook to running your giant globalcorp, open source has accounting, ERP, business intelligence, billing, human resources, investment, point of sale, spreadsheets, and time-tracking software for everyone; Cynthia Harvey assembles this collection of 50 to get you started.

www.linux.com | 7/21/10 12:36 AM

Loqu8 today announced Insight 2.5, the first business intelligence software to speed access for enterprise cloud-based applications.

www.topix.net | 7/17/10 6:54 AM

While the rest of the stock market licked its wounds following Google's lukewarm earnings report, business intelligence software developer Qlik Technologies bucked the trend and took flight in its first day of trading.

redir.internet.com | 7/16/10 11:53 PM

It's always exciting to be on the front end of a trend. Some turn out to be short-lived fads while others, such as the electronic distribution of music, disrupt entire industries. There are some who see cloud computing as a fad. Those who share that view tend to look at the Cloud as if it's an all or nothing proposition; if you can't move your entire infrastructure to the Cloud it must certainly be a passing fancy. But the Cloud is here to stay. There are many applications, like compute intensive statistical analysis, business intelligence and testing, that are incredibly well-suited for cloud computing. We're proud to have been first in the Cloud with a commercial testing solution and have been keen observers of the evolution of testing in the Cloud. What have we seen?

read more

soa.sys-con.com | 7/16/10 2:15 PM

This is part two in my six part series on business intelligence, with a focus on OLAP analysis. Part 1 – Intro to OLAP Identifying the differences between a data warehouse and a data mart. (this post) Introduction to MDX and the kind of SQL which a ROLAP tool must generate to answer those queries. Performance challenges with larger databases, and some ways to help performance using aggregation. Using materialized views to automate that aggregation process. Comparing the performance of OLAP with and without aggregation over multiple MySQL storage engines at various data scales. What is a data warehouse? It turns out that this question is a little more difficult to answer than it probably should be. This is because data warehousing has become an overloaded term that includes BI tools (OLAP/data mining), data extraction and transformation tools (ETL), and schema management tools. To me, the definition of data warehouse is “A relational database schema which stores historical data and metadata from an operational system or systems, in such a way as to facilitate the reporting and analysis of the data, aggregated to various levels.” This definition is a consolidation of various definitions that I have encountered. There are a few key points here. First, data warehouses rarely contain information that exists no where else in an organization. The goal of data warehousing is to collect and make a historical record of the information from another system. This might be an ERP application, the logs from a web application, data from manufacturing systems or even data from radio telescopes. This data is extracted from the source system(s) and then cleaned up and inserted into the data warehouse with ETL tools. This process is usually called “conforming” the source data into the warehouse schema. Another important aspect of the definition is aggregation. A data warehouse is usually used to summarize data over years, months, quarters, or other time dimension attributes. This aids in identifying historical trends and making predictions about future trends. Data is often aggregated in many different ways. Aggregated data may be stored in aggregated tables so that it can be accessed quickly. This is particularly important as fact tables reach into the billions of rows and hundreds of gigabytes of information is accumulated. Accessing this data outside of summarized form often takes a very long time. Is there a particular schema design which lends itself to this historical analysis? There are two main methodologies which are practiced when it comes to designing database schemata for database warehouse applications. These two methodologies approach the problem of storing data in very different ways. The first methodology was popularized by Bill Inmon, who is considered by many to be the “father” of the data warehouse, or at least the first dw “evangelist” if you will. This approach focuses on the normalization of data. Highly normalized schema are created and maintained by ETL jobs. Creating and maintaining these jobs is often one of the biggest parts of designing and running a data warehouse. A particular combination of ETL jobs which consist of one or more data transformations is usually called a “flow”. An example ETL flow might combine data from item and category information into a single dimension, while also maintaining the historical information about when each item was in each category. These types of warehouses are almost always “insert only”. Data is very likely never updated or deleted in these databases and they are expected to grow to very large sizes, usually into the terabyte range, but sometimes even into petabytes. Aggregations are the exception to this rule, as they must be updated periodically to reflect the additions of data to the source tables. The goal of this methodology is the 100% accurate description of historical data from the operational system in a normalized manner which ensures that it is able to be updated quickly. It is accepted that analysis of the data will be more complex in this form, but that this complexity is an acceptable trade off for historical accuracy. This is often described as the “top-down” approach. What is a data mart? The second approach, popularized by Ralph Kimball holds that partial de-normalization of the data is beneficial. The goal of a this approach is usually multi-dimensional (OLAP) analysis as it is very hard to create a dimensional model from a highly normalized database schema. It is particularly difficult to build such a model that scales as the volume in the warehouse increases. For this reason OLAP analysis usually is performed on a star schema which partially denormalizes the data. A star schema about a particular subject matter, such as sales, is usually referred to as a “data mart”. Maybe this is because they provide one stop shopping for all the information about the particular subject matter. That is pretty much what I imagine when I hear the phrase. Data marts tend to be updated frequently, at least once per day. As I mentioned in my previous post, a star schema consists of a central table called the fact table and additional dimension tables which contain information about the facts, such as lists of customers or products. Because of the partially denormalized nature of a star schema, the dimension tables in a data mart may be updated. In fact, there is a term for such a dimension – A “slowly changing dimension” or SCD. The fact table is usually only inserted to, but older data may be purged out of it. Sometimes the fact table will be aggregated from source data. A website which sells banner ads might roll up all the events for a particular ad to the day level, instead of storing detailed information about every impression and click for the ad. A normalized data warehouse schema might contain tables called items, categories and item_category. These three tables allow a user to determine which items belong to which categories, but this structure creates a large number of joins when many dimensions are involved. A data mart would collapse all of this information into an item dimension which would include the category information in the same row as the item information. It would be possible create two different dimensions, product and category, but performance tends to decrease as the number of dimensions increases. The difference illustrated In this mock ERD diagram you can three schemata representing sales orders. The star schema is very denormalized, having only four tables which represent the subject. The data warehouse schema, on the other hand, is very normalized and requires tens of tables to represent the same subject. The snowflake schema is a compromise between the two extremes. Typical star, snowflake and data warehouse schemata. Is one better than the other? In the game of data warehousing, a combination of these methods is of course allowed. A company might take the top-down approach where they maintain a large historical data warehouse, but they also build data marts for OLAP analysis from the warehouse data. A different approach is to build a relational warehouse from multiple data marts, or the so-called bottom-up approach to data warehousing. There is also a cousin of the star schema in which the dimensions are normalized. This type of schema is usually called a snowflake schema. The three table item/category/item_category tables in the warehouse schema example would be considered a snowflake. A dimension table (item) must be joined to additional tables (item_category,category) to find the category. These are not as popular as star schemas because they tend to not perform as well as a star schema, particularly as the volume of data in the database increases. So what is the big deal? From a OLAP performance standpoint, many databases will perform better on a star schema than on a snowflake or fully normalized schema at data warehouse volumes. This is in large part because commercial database software supports hash joins, bitmap indexes, table partitioning, parallel query execution, clustered tables and materialized views. These features make working with a star schema much easier than it may be on MySQL, but it is definitely possibly to use MySQL as long as the right tools and techniques are used. There are ways in which can add some of these features to MySQL as well, but that is a topic for a later post. In the next post I’ll talk more about Mondrian and about MDX, the multi-dimensional query language. Mondrian turns MDX into SQL, so we’ll also look at the kinds of queries which are generated by OLAP analysis. Entry posted by Justin Swanhart | No comment Add to: | | | | www.mysqlperformanceblog.com | 7/15/10 5:41 PM

A new developer survey report from open-source business intelligence vendor Jaspersoft shows that there has been minimal fallout from Oracle's acquisition of Sun Microsystems, and that Java and MySQL seem to be doing just fine in their new home .

www.topix.net | 7/15/10 3:14 AM

ANDROID SMART PHONE Senior DATA WAREHOUSING ENGINEER “We are at the intersection of all things cool in the valley” We are a Sunnyvale, CA based software applications and services development team in the heart of Silicon Valley that is combining the best of the internet, messaging, and social networking into intuitive software, called MOTOBLUR. MOTOBLUR is an innovative cloud-assisted solution to manage and integrate messaging, social networking, and other web services and will debut on the T-Mobile/Motorola CLIQ with MOTOBLUR Android smartphone. This product aggregates contacts, posts, messages, photos and more from sources including Facebook®, Twitter™, MySpace®, Gmail™, and Microsoft Exchange and many more to come. In collaboration with Google’s Android team, we are driving and developing a massively scalable ecosystem. Android is the first free, open source and fully customizable platform built for the mobile internet. Android offers a full stack: an operating system, middleware, and key mobile applications from Google and other. It also contains a rich set up API’s that allows third party developers to develop great applications. We’re financially strong and stable, in the last year generating $300M in positive cash flow; with over $7B in cash and short term investments. Be part of something “innovative & cool” which will reshape the mobile internet and be used by millions of people worldwide. Be part of the resurgent Motorola Mobile Devices business and create applications and services that will enhance the lives of everyone who uses them. Scope of Responsibilities/Expectations: Implement and maintain a high-volume, high-availability hybrid (SQL + NoSQL) data processing infrastructure that consists structured and semi-structured data. Design and implement a large distributed data warehousing and reporting solution and integrate it with 3rd-party business intelligence tools. Basic Qualifications: BS or MS in Mathematics, Statistics or Computer Science Specific Knowledge/Skills: BS or MS in Mathematics, Statistics or Computer Science Expert level Java development experience with emphasis on high performance and scalability Mastery of Hadoop, particularly HDFS, Java MapReduce, Pig, Hive, HBase Strong MySQL experience and strong knowledge of RDBMS fundamentals Working knowledge of ETL, business intelligence and data analytics, preferably Business Objects. Experience working in a Linux environment on a daily basis Hands-on Unix-oriented scripting: Shell, Awk, etc. Successful at establishing strong working relationships with cross-functional teams Strong verbal and written communication skills Working knowledge of Machine Learning methodologies or other regression methodologies like Mahout. Search and Indexing technologies like Lucene/ Solr Other NoSQL experience with “eventual consistency” platforms like Cassandra and Voldemort is a plus APPLY DIRECTLY TO JOB #93304 at: http://www.motorolacareers.com/jobsearch_frames2.cfm?w=search ABSOLUTELY NO AGENCIES OR THIRD PARTY SOLICITATION! everythingmysql.ning.com | 7/14/10 6:39 AM

Hey DBAs! Are you seeking more efficient ways of shifting through your data to aid your business operations? Two popular Business Intelligence products have community Open Source software are Pentaho and JasperSoft. And both work with MySQL.Both are easy to download and install. Both will use a JDBC connector to connect to MySQL. But how easy are the two to configure and run a simple report against a running instance of MySQL? Setting up a JDBC connection with JasperSoft or Pentaho is pretty much like using any other JDBC connection. The next step is to setup a query like SELECT name, job_title, department FROM employees, departments WHERE employees.emp_id = departments.emp_id. Either package will let you pick a variety of output templates. Then you have the BI software merge your query with the template. I honestly think an average MySQL DBA could fairly quickly generate a nice looking report from their instance and that JasperSoft would be just a little bit faster.In part two of this series, the steps will be more detailed and documented. There will also being comparing and contrasting of the two products. Both products are part of larger projects and there are many useful tools that work with the BI software that you will want to investigate. More on those in later posts.And in a short time you should be able to download a Virtual Box image with both community BI programs and a InfiniDB instance with some data sets. This way you can test all three simply. I would also consider doing a VMWare version if there is demand for it. dave-stokes.blogspot.com | 7/14/10 12:03 AM

BI can help carriers find new revenue-generation opportunities as well as control unproductive expenditures such as superfluous network builds in non-profitable areas.

www.topix.net | 7/13/10 6:10 PM

This is the first of a series of posts about business intelligence tools, particularly OLAP (or online analytical processing) tools using MySQL and other free open source software. OLAP tools are a part of the larger topic of business intelligence, a topic that has not had a lot of coverage on MPB. Because of this, I am going to start out talking about these topics in general, rather than getting right to gritty details of their performance. I plan on covering the following topics: Introduction to OLAP and business intelligence. (this post) Identifying the differences between a data warehouse, and a data mart. Introduction to MDX queries and the kind of SQL which a ROLAP tool must generate to answer those queries. Performance challenges with larger databases, and some ways to help performance using aggregation. Using materialized views to automate that aggregation process. Comparing the performance of OLAP with and without aggregation over multiple MySQL storage engines at various data scales. What is BI? Chances are that you have heard the term business intelligence. Business intelligence (or BI) is a term which encompasses many different tools and methods for analyzing data, usually presenting it in a way that is easily consumed by upper management. This analysis is often used to determine how effectively the business has been at meeting certain performance goals, and to forecast how they will do in the future. To put it another way the tools are designed to provide insight about the business process, hence the name. Probably the most popular BI activity for web sites is click analysis. As far as BI is concerned, this series of posts focuses on OLAP analysis and in a lesser sense, on data warehousing. Data warehouses often provide the information upon which OLAP analysis is performed, but more on this in post #2. OLAP? What is that? OLAP is an acronym which stands for online analytical processing. OLAP analysis, which is really just another name for multidimensional analysis, consists of displaying summary aggregations of the data broken down into different groups. A typical OLAP analysis might show “sale total, by year, by sales rep, by product category”. OLAP analysis is usually used for reporting on current data, looking at historical trends and trying to make predictions about future trends. Multidimensional Analysis Multidimensional analysis is a form of statistical analysis. In multidimensional analysis samples representing a particular measure are compared or broken down into different dimensions. For example, in a sales analysis, the “sale amount” is a measure. Measures are always aggregated values. That is, total sales might be expressed as SUM(sale_amt). This is because the SUM of the individual sales will be grouped along different dimensions, such as by year or by product. I’m getting a little ahead of myself. Before we talk about measures and dimensions, we should talk about the two ways in which this information can be stored. There are two main ways to store multidimensional data for OLAP analysis OLAP servers typically come in two basic flavors. Some servers have specialized data stores which store data in a form which is highly effective for multidimensional analysis. These servers are termed MOLAP and they tend to have exceptional performance due to their specialized data store. Almost all MOLAP solutions pre-compute many (or even all) of the possible answers to multi-dimensional queries. Palo is an example of an open source version of this technology. ESSbase is an example of closed source product. MOLAP servers often feature extensive compression of data which can improve performance. Loading data into a MOLAP server usually takes a very long time because many of the answers in the cube must be calculated. The extra time spent during the load is usually called “processing” time. A relational OLAP (or ROLAP) server uses data stored in an RDBMS. These systems trade the performance of a multidimensional store for the convenience of an RDBMS. These servers almost always query over a database which is structured as a STAR or snowflake type schema. To go back to the sales analysis example above, in a STAR schema the facts about the sales would be stored in the fact table, and the list of customers and products would be stored in separate dimension tables. Some ROLAP servers support the aggregation of data into additional tables, and can use the tables automatically. These servers can approach the performance of MOLAP with the convenience of ROLAP, but there are still challenges with this approach. The biggest challenges are the amount of time that it takes to keep the tables updated and in the complexity of the many scripts or jobs which might be necessary to keep the tables in sync. Part five of my series will introduce materialized views which attempt to address these challenges in a manageable way. What makes a ROLAP so great? An OLAP server usually returns information to the user as a ‘pivot table‘ or ‘pivot report’. While you could create such a report in a spreadsheet, the ROLAP tool is designed to deal with millions or even billions of rows of data, much more than a spreadsheet can usually handle. MOLAP servers usually require that all, or almost all of the data must fit it memory. Another difference is the ease by which this analysis is constructed. You don’t necessarily have to write queries or drag and drop a report together in order to analyze multidimensional data using an OLAP tool. Data before pivoting: Data summarized in pivot form: ROLAP tools use star schema As I said before, a sale amount would be considered a measure, and it would usually be aggregated with SUM. The other information about the sale, such as the product, when it was sold and to whom it was sold would be defined in dimension tables. The fact table contains columns which are joined to the dimension tables, such as product_id and customer_id. These are often defined as foreign keys from the fact table to the dimension tables. A note about degenerate dimensions: Any values in the fact table that don’t join to dimensions are either considered degenerate dimensions or measures. In the example below the status of the order is a degenerate dimension. A degenerate dimension is stored as an ENUM in many cases. In the example below that there is no actual dimension table which includes the two different order statuses. Such a dimension would add an extra join, which is expensive. Any yes/no field and/or fields with a very low cardinality (such as gender or order status) will probably be stored in the fact table instead of in a dedicated dimension. In the “pivot data” example above, all the dimensions are degenerate: gender, region, style, date. Example star schema about sales. Often a dimension will include redundant information to make reporting easier, a process called “denormalization”. Hierarchical information may be stored in a single dimension. For example, a dimension for products may include both the category AND a sub-category. A time dimension includes year, month and quarter. You can create multiple different hierarchies from a single dimension. This allows ‘drill down’ into the dimension. By default the data would be summarized by year, but you can drill down to quarter or month level aggregation. The screenshots here in the jPivot (an OLAP cube browser) documentation can give you a better idea about the display of data. The examples break down sales by product, by category, and by region. The information is presented in such a fashion that it can be “drilled into” and “filtered on” to provide an easy to use interface to the underlying data. Graphical display of the data as pie, line or bar charts is possible. Focusing on ROLAP. This is the MySQL performance blog, and as such an in depth discussion of MOLAP technology is not particularly warranted here. Our discussion will focus on Mondrian. Mondrian is an open source ROLAP server featuring an in-memory OLAP cache. Mondrian is part of the Pentaho open source business intelligence suite. Mondrian is also used by other projects such as Wabit and Jaspersoft. If you are using open source BI then you are probably already using Mondrian. Closed source ROLAP servers include Microstrategy, Microsoft Analysis Services and Oracle BI. Mondrian speaks MDX, olap4j and XML for analysis. This means that there is a very high chance that your existing BI tools (if you have them) will work with it. MDX is a query language that looks similar to SQL but is actually very different. Olap4j is an OLAP interface for java applications. XML for analysis (XMLA) is an industry standard analytical interface originally created by Microsoft, SAS and Hyperion. Whats next? Next we’ll talk about the difference between data marts and data warehouses. The former are usually used for OLAP analysis, but they can be fundamentally related to a warehouse. Entry posted by Justin Swanhart | No comment Add to: | | | | www.mysqlperformanceblog.com | 7/12/10 8:26 PM

H3 Solutions (news, site) has announced the second major release of their SharePoint mobile application framework, Mobile Entrée, and it’s packed with business intelligence, dashboards and custom theming.

Read full story... www.cmswire.com | 7/12/10 5:16 PM

The London launch of Oracle’s (news, site) 11g Business Intelligence Enterprise Edition this week is the culmination of three years work to pull together all its business intelligence tools and build on the technologies it acquired with the Siebel and Hyperion acquisitions.

Read full story... www.cmswire.com | 7/9/10 5:24 PM

The Philippines lags behind other Asian countries and regions in the popularization of digital TV, according to the latest figures released Thursday by Informa Telecoms and Media, a leading provider of business intelligence and strategic services to the global telecoms and media markets. The Philippines ranked second from the bottom of the list in terms of digital TV penetration, with just 5 percent in 2009, up from 1 percent in 2005. By 2015, the country's digital TV penetration will ... english.people.com.cn | 7/8/10 9:26 AM

Database giant updates its BI platform with a variety of new services and integration into Oracle's growing array of software products.

redir.internet.com | 7/8/10 12:13 AM

Editor's note: The following analysis is written by Robert J. Moore, the CEO and co-founder of RJMetrics, an on-demand database analytics and business intelligence startup. Robert blogs at The Metric System and can be followed on Twitter at @RJMetrics. Location-based social networks Foursquare and Gowalla are accumulating users (and headlines) with impressive momentum. While both companies have been vocal about reaching major milestones, we wanted to take a closer look at the data behind these accomplishments.  Based on our estimates, Foursquare is not only bigger in terms of both users and venues, but it also is growing at a faster clip than Gowalla. For the past four weeks, we've been monitoring the Foursquare and Gowalla APIs to track growth rates, as well as to sample users and venues. This data was loaded into an RJMetrics Dashboard , which provided the results found here with just a few clicks. We will keep these estimates up-to-date with fresh data and you can view them any time at our Startup Data page. Here are a few highlights from our findings: techcrunch.com | 7/7/10 9:50 PM

I recently wrote an article for The Enterprise Mobility Foundation, The Next Step in Business Process Optimization: Mobility. In this article I describe the enormous amount of work that has gone into designing and automating business processes and ERPs like SAP. The conclusion of the article is that as sophisticated as ERPs are today, there remains significant feature gaps, especially in extending business processes out to the mobile workforce. In this article, I want to highlight some areas where there is still much work to be done in field services. In field services, a number of SAP mobility partners like Clicksoftware and Syclo have comprehensive field services and enterprise asset management solutions with mobile clients for use on smartphones and ruggedized handhelds. However, there are still feature gaps in the integration of real time data, M2M integration, geotags, business intelligence, augmented reality and multi-media support.

read more

soa.sys-con.com | 7/7/10 5:11 PM

EMC broadens its IT software stack with the purchase of a developer of massively parallel processing database software for business intelligence and analytics.

redir.internet.com | 7/7/10 12:26 PM

Enterprise software giant EMC has acquired data warehousing company Greenplum Software . Financial terms of the deal were not disclosed but the acquisition is an all-cash transaction. Greenplum, which has raised $61 million in funding, develops database software for business intelligence and data warehousing applications. Greenplum has a number of high profile investors, including Sun Microsystems and SAP Ventures. The company's client base includes Skype, Equifax, T-Mobile and Fox Interactive Media Greenplum will become the foundation for a new data computing product division within EMC's Information Infrastructure business. techcrunch.com | 7/6/10 9:36 PM

Microstrategy is releasing a new business intelligence app for the iPhone.

www.macworld.com | 7/6/10 6:15 PM

PC World - Microstrategy on Tuesday is announcing a next-generation mobile BI (business intelligence) application for Apple's iPhone and iPad, joining the growing pack of enterprise vendors that are embracing the red-hot platform. us.rd.yahoo.com | 7/6/10 2:30 PM

Event Date: July 8, 2010 1:00 pm GMT-8

Register Now