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cezarovidiu

Rittman Mead Consulting - The Changing World of Business Intelligence - 0 views

www.rittmanmead.com/...world-of-business-intelligence

rittmanmead BI oracle

shared by cezarovidiu on 02 Mar 13 - No Cached
  • 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.
  • ...3 more annotations...
  • Schema on read Schema on read works on a different principle and is more common in the Big Data world. The data is not transformed in any way when it is stored, the data store acts as a big bucket. The modelling of the data only occurs when the data is read. Map/Reduce is the clearest example, the mapping is the understanding of the data structure. Hadoop is a large distributed file system, which is very good at storing large volumes of data, this is potential. It is only the mapping of this data that provides value, this is done when the data is read, not written.
  • New World Order So whereas Business Intelligence used to always be driven by the model, the ETL process to populate the model and the reporting tool to query the model, there is now an approach where the data is collected its raw form, and advanced statistical or analytical tools are used to interrogate the data. An example of one such tool is R.
  • The driver for which approach to use is often driven by what the user wants to find out. If the question is clearly formed and the sources of data that are required to answer it well understood, for example how many units of a product have we sold, then the traditional schema on write approach is best.
cezarovidiu

Star Schema Bechmark: InfoBright, InfiniDB and LucidDB - MySQL Performance Blog - 0 views

www.mysqlperformanceblog.com/...nfobright-infinidb-and-luciddb

infobright infinidb opensource mysql DW

shared by cezarovidiu on 15 Feb 13 - No Cached
  • Queries time
  • InfoBright was fully 1 CPU bound during all queries.
  • InfiniDB is otherwise was IO-bound, and processed data fully utilizing sequential reads and reading data with speed 120MB/s. I think it allowed InfiniDB to get the best time in the most queries.
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  • LucidDB on this stage is also can utilize only singe thread with results sometime better, sometime worse than InfoBright.
  • cezarovidiu on 15 Feb 13
     
    "Star Schema Bechmark: InfoBright, InfiniDB and LucidDB"
cezarovidiu

Rittman Mead Consulting » Blog Archive » Using OBIEE against Transactional Sc... - 0 views

www.rittmanmead.com/...al-schemas-part-1-introduction

rittmanmead obiee transactional

shared by cezarovidiu on 03 Apr 13 - No Cached
  • The best practice in business intelligence delivery is always to build a data warehouse.
  • Pure transactional reporting is problematic. There are, of course, the usual performance issues. Equally troublesome is the difficulty in distilling a physical model down to a format that is easy for business users to understand. Dimensional models are typically the way business users envision their business: simple, inclusive structures for each entity. The standard OLTP data model that takes two of the four walls in the conference room to display will never make sense to your average business user.
cezarovidiu

Entity Attribute Value in Magento - Magento tutorial lesson 19 - Magento - 0 views

blog.magestore.com/...ity-attribute-value-in-magento

eav magento database schema

shared by cezarovidiu on 19 Feb 14 - No Cached
  • Entity Attribute Value
  • number of attributes (properties and parameters) which can be used to describe them are potentially vast, but the number of attributes which will actually apply to a given entity are relatively modest.
  • sparse matrix
  • ...11 more annotations...
  • vertical database
  • open schema
  • more complex queries
  • Entity: The entity represents Magento data items
  • Attribute: The attributes represent data items that belong to an entity.
  • Value: The value is the simplest to understand as it is simply a value linked to an attribute
  • 1.1. What is Entity Attribute Value
  • 1.2. Entity table structure
  • At the very least, the attribute definitions table would contain the following columns: an attribute ID, attribute name, description, data type, and columns assisting input validation, e.g., maximum string length and regular expression, set of permissible values, etc.
  • The attribute or parameter:
  • The entity
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