Connect to traditional data sources
Ability to connect to traditional data sources like relational databases, flat files, XML files and packaged applications
Cat avg: 8.8
Top Performing Features
Connecto to Big Data and NoSQL
Ability to connect to non-traditional data sources like Hadoop and other big data technologies, and NoSQL databases
Cat avg: 7.8
Simple transformations
Simple data transformations are calculations, data type conversions, aggregations and search and replace operations
Cat avg: 8.1
Complex transformations
Complex data transformations are data normalization, advanced data parsing, etc.
Cat avg: 7.2
Worst Performing Features
Data model creation
Ability to create and maintain data models using a graphical tool to define relationships between data
Cat avg: 8.4
Metadata management
Automated discovery of metadata with ability to synchronize and share metadata with other tools like Master Data Management
Cat avg: 7.9
Business rules and workflow
Ability to define and manage business rules and workflows
Cat avg: 8.3
IBM DataStage Features from Reviews
Data Source Connection
Ability to connect to multiple data sources
9.5+13%
Connect to traditional data sources
Ability to connect to traditional data sources like relational databases, flat files, XML files and packaged applications
Category average: 8.8
Connecto to Big Data and NoSQL
Ability to connect to non-traditional data sources like Hadoop and other big data technologies, and NoSQL databases
Category average: 7.8
Data Transformations
Data transformations include calculations, search and replace, data normalization and data parsing
8.0+4%
Simple transformations
Simple data transformations are calculations, data type conversions, aggregations and search and replace operations
Category average: 8.1
Complex transformations
Complex data transformations are data normalization, advanced data parsing, etc.
Category average: 7.2
Data Modeling
A data model is a diagram or flowchart that illustrates the relationships between data
6.3-21%
Data model creation
Ability to create and maintain data models using a graphical tool to define relationships between data
Category average: 8.4
Metadata management
Automated discovery of metadata with ability to synchronize and share metadata with other tools like Master Data Management
Category average: 7.9
Business rules and workflow
Ability to define and manage business rules and workflows
Category average: 8.3
Collaboration
Collaboration is enabled by a shared repository of project information and metadata
Category average: 7.3
Testing and debugging
Tool to debug and tune for optimal performance
Category average: 7
Data Governance
Data governance is the practise of implementing policies defining effective use of an organization's data assets
6.0-23%
Integration with data quality tools
Integration with tools for cleansing, parsing and normalizing data according to business rules
Category average: 7.9
Integration with MDM tools
Integration with master data management tools to ensure data consistency across the organization
Category average: 7.8