Much time can be wasted testing complex OLAP Cubes only to find they weren't created correctly. This articles draws on real-world experience to show how to unit test Cubes to ensure they were built the right way.
Many different data mining, query model, processing model, and data collection techniques are available. Which one do you use to mine your data, and which one can you use in combination with your existing software and infrastructure? Examine different data mining and analytics techniques and solutions, and learn how to build them using existing software and installations. Explore the different data mining tools that are available, and learn how to determine whether the size and complexity of your information might result in processing and storage complexities, and what to do.
In this article by John Heaton, author of Business Intelligence Cookbook: A Project Lifecycle Approach Using Oracle Technology, we will review some of the key architecture and design issues that should be addressed by the project. Specifically, we will cover: * Choosing your database type * Defining your database layout * Selecting the Third Normal Form or a Dimensional model
The art of data mining is a wide field, and mentioning the term to two different developers gives you two very different ideas about it. In this article, you learn what data mining is, its importance, different ways to accomplish data mining (or to create web-based data mining tools) and develop an understanding of XML structure to parse XML and other data in PHP technology.
Using a patterns approach could be the difference between successfully implementing an enterprise data warehouse (EDW) time and being yet another project failure statistic. It provides steps for defining a set of solution patterns that provide a balance between the need to collect and integrate data for the EDW and the need of the business to have immediate, tactical solutions.
This article discusses how IT organizations face challenges when data volume explodes and how grid technology has proven to be an effective solution to handle this data volume and make the data processing faster. This article also looks at the data warehousing products of some of the vendors that have implemented the grid technology in their databases and ETL tools.
Over the past few years, OLAP has become an increasingly popular approach to handling multidimensional data for warehousing and business intelligence. OLAP servers and applications are commonplace and many storage schemes, query mechanisms, and access strategies have been developed to meet the business demand for complex analytical querying. The JDBC API has served as a proxy mechanism for accessing and manipulating dimensional data on the Java platform, but using JDBC can compromise the benefits of a dimensional scheme. Often this is remedied by combining JDBC with a proprietary extension, but that means being locked into a specific implementation.
This article, which is intended for Java developers familiar with OLAP, introduces the Java OLAP (JOLAP) API. It describes its core components and related packages and offers an example of a JOLAP query and retrieval operation. It also briefly discusses the similarities between JOLAP and JDBC, as well as the relationship between JOLAP and mdXML, the XMLA query language for multidimensional data.
Relational and dimensional modeling are often used separately, but they can be successfully incorporated into a single design when needed. Doing so starts with a normalized relational model and then adds dimensional constructs, primarily at the physical level. The result is a single model that can provide the strengths of its parent models fairly well: it represents entities and relationships with the precision of the traditional relational model, and it processes dimensionally filtered, fact-aggregated queries with speed approaching that of the traditional dimensional model. Real-world experience was the motivation for this analysis: on three separate data warehousing projects where I worked as programmer, architect, and manager, respectively, I found a consistent pattern of data/database behavior that lent itself far more to a hybrid combination of dimensional and relational modeling than to either one alone. This article discusses the hybrid design and provides a fully functional reference implementation.