To manage and control Business Intelligence
and Data Warehouse services, planning and change
management we have to track and understand system components
impact and dependencies.
To manage and understand Business Intelligence
and Data Warehouse services we need
track and understand data lineage and dependencies in long data
Data Warehouse database performance or data loading
migrations bring the need for change in paradigm
and conversion from external tools to database scripts,
ETL to ELT.
Structured and model driven
semantic wiki user interface allows anyone
to create and manage complicated business concepts and
definitions as familiar Wikipedia pages.
Related with IT assets and data governance.
One of the cornerstones of managing Business Intelligence and Data Warehouse ecosystems effectively is the ability to track and understand lineage, impact and dependencies in long data transformation chains. We address this need by combining semantic technologies, grammar based parsing and probabilistic rule-based inference with a powerful metadata repository and efficient aggregation and visualization techniques.
A common problem in maintaining data integration and transformation systems in corporate data warehouse environments is the necessity to migrate existing data transformation processes and workflows to new platforms. Often those migrations bring along the need for change in data transformation paradigm and conversion from ETL (Extract-Transform-Load) style to ELT (Extract-Load-Transform) style.
A rich and powerful metadata repository without a simple and easy interface for business users would not find much use. A well-structured, metamodel driven metadata repository in combination with semantic wiki user interface allows anyone to see and feel complicated metadata structures as familiar wiki pages. This is as close to Knowledge Management as it gets.
15.07.2016 -- New DDL/SQL script file scanner for ORACLE, DB2, MSSQL, NETEZZA, TERADATA, POSTGRES and others were added to the family of previously avaliable direct DB scanners.
05.01.2016 -- New ETL scanners for Informatica and IBM DataStage were added to the family of MMX database scanners as additon to previously avaliable set of ETL tools (DTS/SSIS, ODI, Pentaho, SQL Scripts and loaders, etc).
12.06.2015 -- Microsoft Business Intelligence stack, including Microsoft SSAS cubes and Microsoft SSRS reports is now supported by MMX Foundation application scanner family, that already included Oracle Reports and SAP Business Objects. Support for Tableau and QlikView will be announced soon.
03.05.2015 -- New database scanners for IBM DB2 and HP Vertica databases were added to the portfolio of MMX Foundation database scanners. This rounds up the coverage of all major analytical database platforms, previously including Microsoft SQL Server, Teradata, Oracle etc.
19.02.2015 -- SAP Business Objects Semantic Layer (SL) Universe (.UNX) scanning has been added to MMX Business Objects Scanner. BO Central Management Server (CMS), Universes (.UNV) and WebIntelligence Reports (.WID) are also supported on BOXI 3.1-4.1 platforms.
01.11.2014 -- EBA Data Point Model has been added to MMX Metadata Framework. Methodology for mapping DPM Domains and Dimensions to existing data sources was developed for a financial institution as part of the FINREP implementation, enabling an organization to avoid mapping on single Data Point level.