The Challenge of Complexity and Volatiliy
• Advances in technology have led to globalization, increase in speed, volatility and complexity of data.
• Businesses need flexible and user-friendly access to information to cope with this change.
• The standard operational ERP-applications – high volume, standardized products, stable are effective to run the business, but are to rigid to change the business.
• Flexibility and transparency have first priority to master complexity.
User-friendly access to information is a must to bring data value to the decision makers and to create a competitive advantage.
The Technology Gap
The technology gap is an important obstacle for the alignment of business and IT.
• The corporate “IT-Department” still employs the same centralized structure as in the early beginnings when called “Electronic Data Processing Dept”.
• The PC and office-tools have emancipated information handling from corporate IT and built an IT-ecosystem of its own.
• Rising power and functionality of office-tools enabled the creation of a Shadow-IT.
Excel-applications are growing in size, complexity and out of control. According to reviews, of every $ spent for IT-services 50cts disappear in this grey hole. It is impossible to suppress Shadow-IT – the solution: support it with an appropriate infrastructure.
Metasafe - to Bridge the Gap
„One does not fit all“ – an infrastructure is required which interfaces with both worlds and supports the specific requirements to bridge the gap.
• For Office-Tools: Metasafe interfaces smoothly with office-tools and provides a user-friendly access to data – substantially more data and more complex data than office tools can handle.
• For Corporate IT: Metasafe offers an advanced infrastructure for many IT-management functions like data modeling, data management, application portfolio, data dictionary, CMDB.
A flexible data platform can help to turn Shadow-IT into a valuable infrastructure. Metasafe is such a data platform (DBMS).
Metasafe - make Complex Something is characterized as being "complex" if is consists of many parts with a large number of relationships between these parts. A large number of parts (a million bolts) does not make something really complex - it is the number of relationships which matter. Applications Transparent
The new breed of applications deal with many entity-types and many relationships between entities. They must be flexible and able to react to change requests.
• A flexible data platform is an indispensable requirement for these applications.
• With Metasafe as data platform development is faster and less expensive and the applications are more flexible and user-friendly.
• Development and documentation of models
• Planning, Reporting, Control-Applications
• IT-Governance, Portfolio-Management
• IT-Development and IT-Operations
Metasafe Core – a Database-Management System
A documented data model is the foundation of serious application development
• Metasafe supports data modeling with elaborate functions for the design and documentation of the conceptual model and the derived external models (views).
• Metasafe maintains and stores models under transaction control in the database.
• Metasafe stores instance data in the same structure as the data model (Entity-Relationship) in the database.
• Metasafe supports extensive versioning for instance data.
• Metasafe is not a relational (table-only) and not a NoSQL (schema-less) DBMS.
Metasafe is a native implementation of the ER-model, also called an “executable conceptual model”. All functions (e.g. API, query-language, tools) relate to the Entity-Relationship-Model.
Metasafe - the Layer-Architecture
Metasafe is designed as a general purpose DBMS with clear layers and interfaces - in contrast to some “repositories” which mix application and data storage.
• Metsafe has flexible architecture and can be easily extended by additional functionality.
• User-developed applications access the database via the Metasafe-tool-box, the erSQL-Query-language and the API / framework.
• The framework offers a large set of functions to manage the models and instance data.
• Models can be created or changed on the fly under transaction control.
• The Metasafe core is a separate server, constructed for multi-user access.
• The Metasafe server can operate as data store for a web-application
• A relational DMBS serves as persistence storage.
The Metasafe Dictionary
A dictionary documents the meaning of the words we are using and ensures a common understanding.
• The Metasafe Dictionary documents the properties (attributes of types) of all elements and maintains a complete cross reference about the use of all the elements of the schemas, namely:
• Schemas: conceptual, external
• Types: entity-, attribute-, relationship-types;
• Instances - Catalogs, Variants to handle versioning
• Usergroups and Users to manage access rights.
An integrated central dictionary is a prerequisite to create a common point of reference and to avoid misunderstandings.
metaModeler – the Modeling Toolbox of Metasafe
Construct and document the concepual model using the elements from the dictionary and drag them to the schema.
• Create and document the external model (Views) for individual user groups and applications.
Import / Export models (XML)
• Generate a documented model description: Use Metasafe to publish the model for general use and do not leave it pinned to the wall of specialists.
• Create graphics with automatic layouting instead of painting pictures
• Cooperative creation of models in multi-user mode with access rights
A fully documented model as a common language for users, designers and specialists avoids misunderstandings and errors. Metasafe is the right tool for the job.
Internal Model – the Structure of Instance Data
The internal model describes the structure and access mechanism of a data store.
• In the relational world, the information (conceptual) model is different from the structure of the instance data. The meaning of data (conceptual) and the storage of data (internal) are separated by a structure gap. (impedance mismatch).
• Metasafe applies the identical ER-structure for the conceptual model and for the external data.
• This omits the “impedance mismatch” (structural gap) between the model and the instance data (as e.g. in the relational model).
• This leads to the much simpler and more powerful Metasafe erSQL query language which empowers the users to access data without needing a data specialist.
Test the Data Model with Test Data
The data model is an important artifact of application development. It must be tested with testdata before being put in use.
• In current practice data models are never tested. Developers move quickly to their relational data base and forget about the model. The model, which describes the meaning of the data remains pinned to the wall and become obsolete.
• With Metasafe you can easily test the data model to make sure that the model meets the requirements
• Simply load test data using the Metasafe toolset
• Browse and inspect the data with metaEditor
• Create erSQL-Queries as defined by Use-Cases
Quick Start with Metasafe versatile Tool Set
In the current practice data modeling, data storage and access tools are separate disciplines and virtually do not know of each other.
• Metasafe integrates them and provides model-driven tools for the access of the instance data. These ‘model-driven’ tools offer a set of generic functions for of the shelf usage.
• Model-based tool-set for generic services
• Desktop version based on Eclipse
• Web based version with Eclipse RAP
• Multi-window, multi-user with access rights
• Multi-function framework wit Java API
• Interfaces to various tools (Excel, BIRT)
• Import/Export in various formats
• Generation of Documentation with XML / XSLFO
metaEditor – Multi-Function Tool for Instance Data
metaEdtior offers an integrated and extensible set of services under a common hood.
• Configurable Eclipse-views with extension points
• Search entity and browse relationships
• Handle access to other data stores (eg relational)
• Attribute view and editor
• Configurable layout and access rules
• Export to XML, XLS, graphics
• erSQL-Query with query-editor and runtime
• Access and manage external attachments
Generic Model-Driven Search, Browse and Edit
Metasafe provides basic housekeeping functions in a model-driven manner. They are always up-to-date, also after changes of the model – without the need for code-generation.
• Configure @ Model instead of programming
• Search entities with wild-cards and versioning
• Browse along relationships in the network
• Display GUI controlled by configurable widgets
• Multi-Update with transaction control
• Update-rights defined in the (external) model
• Update controlled by validation rules in Java-Script
• Multi-window drag-drop to create relationships
Access Data easily using the erSQL Query-Editor
The access language is a core function of a DBMS. The relational world has (r-)SQL to access instance data as a tool for specialists.
• Metasafe in contrast offers the model driven access language erSQL for users and specialists.
• ER-Model driven query-definition and execution
• Navigation of relationships replaces complex “Joins”
• Point and click in the data model to create a query
• Graphical documentation of the query-structure
• Execution returns typed result-set and metadata
• Export result as XML, XLS, data-stream, search-tree
• Run the query embedded in a Java program
Get High-Quality-Reports without Writing Programs
Reports are an substantial part of an application and consume a substantial share of the development and maintenance budget. Conventional programming of reports is expensive and reports are number one change problems.
• BIRT empowers non-IT people to create their own reports - if the data are transparent and users know SQL
Solve this problem with BIRT and erSQL combined!
• The erSQL-query-editor embedded in BIRT – data access is transparent with the model and erSQL-query
• Reduce cost – provide adhoc reporting wit BIRT
Put Information @ the Fingertips of the User
Direct access to information is the basis for an alignment of Business and IT.
• Understand and document the language of the user.
• Create and published a data model and maintain it
• Acquire test data and store them in the repository
• Create erSQL test-queries for Use-Cases
• Generate reports with queries and BIRT
• Implement the application
• Monitor the use and install proper support
• Enpower users to access the data on their own