ER/Studio Data Architect

Data Modeling for Multi-Platform Environments

ER/Studio Data Architect

  • Create effective data models to build a business-driven data architecture
  • Document and enhance existing databases to reduce redundancy
  • Implement naming standards to improve data consistency and quality
  • Effectively share and communicate models across the enterprise
  • Map data sources and trace origins to enhance data lineage

פרולוג'יק היא המפיצה הרישמית של מוצרי IDERA בישראל.
כמו כן פרולוג'יק מספקת חבילת מוצר מושלמת הכוללת אינטגרציה, ליווי והדרכה.

  • Create effective data models to build a business-driven data architecture
  • Document and enhance existing databases to reduce redundancy
  • Implement naming standards to improve data consistency and quality
  • Effectively share and communicate models across the enterprise
  • Map data sources and trace origins to enhance data lineage

Build a Business-driven Data Architecture

Data architects need to ensure that everyone in the organization understands what the data is and can explain it in business terms. Data Architect provides an easy-to-use visual interface for data modeling professionals to document, understand, and publish information about data models and databases so they can be better harnessed to support business objectives.

Reduce Redundancy

Import and reverse-engineer content from multiple data sources and platforms into logical and physical data models, and integrate the elements into reusable constructs with an enterprise data dictionary. Document existing relational and NoSQL databases to allow reuse of common data elements and structures, for better support of business objectives.

Improve Data Consistency and Quality

Assign a naming standards template to your model, submodel, entities, and attributes. Those naming standards will be applied automatically between the logical and physical models, simplifying the data modeling process and ensuring consistency between models. Track model changes associated with agile workflows.

Share and Communicate Across the Enterprise

The multi-level design layers in ER/Studio Data Architect allow for the accurate visualization of data, which promotes communication between business and technical users. Manage model version control and share data assets in the repository. Create and track tasks and view changes to data models aligned to agile workflows.

Enhance Data Lineage

Universal mappings provide links between instances of the same concept across models and databases to enhance traceability even further, and data lineage shows the connections between databases, models, metadata, and data sources for traceability. Organizations can obtain a clear understanding of where their data originated, where it is used, and what the data actually means.

ER/Studio Data Architect is available in two editions: The standard ER/Studio Data Architect edition is the feature-rich tool with extensive data modeling capabilities across multiple platforms, along with import bridges for other common modeling tools. The ER/Studio Data Architect Professional edition also includes the model repository for version control and agile change management.

Advanced Graphics and Layout

Automatically create highly readable, highly navigable diagrams with one or a combination of layouts

Automated and Custom Transformation

Streamlines the derivation of one or more physical designs from a logical one and checks for normalization and compliance with the target database

Extensible Automation Interface

Automate tedious, routine tasks such as coloring tables, enforcing and applying naming standards, globally update storage parameters and integrate with desktop applications

Multiple Presentation Formats

Publish models and reports in a variety of formats including HTML, RTF, XML Schema, PNG, JPEG and DTD Output

Visual Data Lineage

Visually document source/target mapping and sourcing rules for data movement across systems

Dimensional Modeling

Leverage complex star and snowflake schema designs and support importing rich dimensional metadata from BI and data warehouse platforms

Data Classification

Categorize and label objects according to the level of security and privacy

Permission Management

Enable user, role and group permissions at logical and physical level

Concurrent Model and Object Access

Allows real-time collaboration between modelers working on data models down to the model object level

Version Management

Manages the individual histories of models and model objects to ensure incremental comparison between, and rollback to, desired diagrams

Component Sharing and Reuse

Predefined Enterprise Data Dictionary eliminates data redundancy and enforces data element standards

Security Center Groups

Streamline security administration with local or LDAP groups improving productivity and reducing errors

Agile Change Management

Assign and track tasks associated with data models to align changes to user stories and development workflows.

Forward and Reverse Engineering

Generate source code from database designs. Construct graphical models from existing database or schema. Apply design changes with formulated alter code

Universal Mappings

Map between and within conceptual, logical and physical model objects to trace objects upstream or downstream

Data Dictionary Standardization

Define and enforce standard data elements, naming standards and reference values

Advanced Compare and Merge

Enable advanced bi-directional comparisons and merges of models and database structures

Business Data Objects

Represent master data and transactional concepts with multiple entities and relationships, such as products, customers, and vendors

Submodel Management

Allow creation of multi-leveled submodels, merge submodel properties across existing models and synchronize submodel hierarchies

Naming Standards

Assign a naming standards template to models, submodels, entities and attributes for automatic application between logical and physical models

Metadata Integration

Import and export metadata from BI Platforms, UML and data modeling solutions, XML Schemas and CWM (Common Warehouse Metamodel) to create a metadata hub

Automatic Migration of Foreign Keys

Maintain foreign keys to ensure referential integrity in database designs

”Where Used” Analysis

Display mappings between logical entities and attributes to their implementation across physical designs

Model Completion Validation

Automate model reviews and enforce standards by validating for missing object definitions, unused domains, identical indexes and circular relationships

    • Windows Server 2008, 2008R2, 2012, 2012R2

    Native connections:

    • DB2 (LUW and z/OS)
    • Oracle
    • Azure SQL Database
    • SQL Server
    • SQL Server in Azure VMs
    • Sybase
    • MongoDB
    • Hadoop Hive
    • ODBC Connections

    Supported Platforms:

    • Firebird® 1.5, 2.x
    • Greenplum 4.2
    • Hitachi HiRDB
    • Hadoop Hive 0.12, 0.13
    • IBM® DB2® 5.x – 10.x for LUW and z/OS® & iSeries V4R5 and V5R2
    • IBM Informix® OnLine and SE
    • Informix 9.x dynamic server
    • InterBase® 4, 2007, 2009
    • InterBase XE, XE3
    • Microsoft® Access 2.0, 95, 97, 2000
    • Microsoft Azure SQL Database
    • Microsoft SQL Server 6.5, 7, 2000, 2005, 2008, 2012, 2014
    • Microsoft SQL Server on Azure
    • Microsoft Visual FoxPro® 2, 3, 5
    • MongoDB 2.4, 2.6, 3.0
    • MySQL® 3.x, 4.x, 5.x
    • Netezza 4.6, 5.0, 6.0, 7.0
    • Oracle® 7.3, 8.x, 9i, 10g, 11g, 12c
    • PostgreSQL 8.x, 9.x
    • Sybase® Adaptive Server® Enterprise (ASE) 11.9.2, 12.x, 12.5, 15.0
    • Sybase Adaptive Server Anywhere (ASA) 5, 6, 7, 8, 9, 10
    • Sybase IQ 12.x, 15.x, 16.x
    • Sybase Watcom SQL
    • Teradata® V2R4, V2R5, V2R6, 12, 13.0, 14.x, 15.10