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AnalyticsCreatorAccelerate your data initiatives with AnalyticsCreator—a metadata-driven data warehouse automation solution purpose-built for the Microsoft data ecosystem. AnalyticsCreator simplifies the design, development, and deployment of modern data architectures, including dimensional models, data marts, data vaults, and blended modeling strategies that combine best practices from across methodologies. Seamlessly integrate with key Microsoft technologies such as SQL Server, Azure Synapse Analytics, Microsoft Fabric (including OneLake and SQL Endpoint Lakehouse environments), and Power BI. AnalyticsCreator automates ELT pipeline generation, data modeling, historization, and semantic model creation—reducing tool sprawl and minimizing the need for manual SQL coding across your data engineering lifecycle. Designed for CI/CD-driven data engineering workflows, AnalyticsCreator connects easily with Azure DevOps and GitHub for version control, automated builds, and environment-specific deployments. Whether working across development, test, and production environments, teams can ensure faster, error-free releases while maintaining full governance and audit trails. Additional productivity features include automated documentation generation, end-to-end data lineage tracking, and adaptive schema evolution to handle change management with ease. AnalyticsCreator also offers integrated deployment governance, allowing teams to streamline promotion processes while reducing deployment risks. By eliminating repetitive tasks and enabling agile delivery, AnalyticsCreator helps data engineers, architects, and BI teams focus on delivering business-ready insights faster. Empower your organization to accelerate time-to-value for data products and analytical models—while ensuring governance, scalability, and Microsoft platform alignment every step of the way.
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What is IBM DataStage?
Accelerate the development of AI innovations with the cloud-native data integration solutions provided by IBM Cloud Pak for Data. With AI-enhanced data integration functionalities available from any location, the impact of your AI and analytics initiatives is closely tied to the caliber of the underlying data. Leveraging a contemporary container-based framework, IBM® DataStage® within IBM Cloud Pak® for Data guarantees the provision of high-quality data. This offering combines exceptional data integration with DataOps, governance, and analytics into a cohesive data and AI ecosystem. By streamlining administrative processes, it contributes to a reduction in total cost of ownership (TCO). The platform's AI-driven design accelerators, in conjunction with readily available integrations for DataOps and data science services, significantly expedite the pace of AI development. Moreover, its capabilities for parallel processing and multicloud integration facilitate the delivery of consistent data across extensive hybrid or multicloud environments. Additionally, the IBM Cloud Pak for Data platform allows for the effective management of the complete data and analytics lifecycle, incorporating a range of services such as data science, event messaging, data virtualization, and data warehousing, all supported by a parallel engine and automated load balancing. This all-encompassing strategy equips your organization to remain at the forefront of the swiftly changing data and AI landscape, ensuring that you can adapt and thrive in a competitive market.
What is Data Virtuality?
Unify and streamline your data operations. Transform your data ecosystem into a dynamic force. Data Virtuality serves as an integration platform that ensures immediate access to data, centralizes information, and enforces data governance. The Logical Data Warehouse merges both materialization and virtualization techniques to deliver optimal performance. To achieve high-quality data, effective governance, and swift market readiness, establish a single source of truth by layering virtual components over your current data setup, whether it's hosted on-premises or in the cloud. Data Virtuality provides three distinct modules: Pipes Professional, Pipes Professional, and Logical Data Warehouse, which collectively can reduce development time by as much as 80%. With the ability to access any data in mere seconds and automate workflows through SQL, the platform enhances efficiency. Additionally, Rapid BI Prototyping accelerates your time to market significantly. Consistent, accurate, and complete data relies heavily on maintaining high data quality, while utilizing metadata repositories can enhance your master data management practices. This comprehensive approach ensures your organization remains agile and responsive in a fast-paced data environment.
Integrations Supported
Acquisio
Amazon Redshift
Amazon S3
HubSpot CRM
IBM Cognos Analytics
IRI FieldShield
Kendo UI
Marin Software
Microsoft Excel
Mixpanel
Integrations Supported
Acquisio
Amazon Redshift
Amazon S3
HubSpot CRM
IBM Cognos Analytics
IRI FieldShield
Kendo UI
Marin Software
Microsoft Excel
Mixpanel
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
IBM
Date Founded
1911
Company Location
United States
Company Website
www.ibm.com/products/infosphere-datastage
Company Facts
Organization Name
Data Virtuality
Date Founded
2012
Company Location
Germany
Company Website
datavirtuality.com
Categories and Features
Big Data
Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates
Data Lineage
Database Change Impact Analysis
Filter Lineage Links
Implicit Connection Discovery
Lineage Object Filtering
Object Lineage Tracing
Point-in-Time Visibility
User/Client/Target Connection Visibility
Visual & Text Lineage View
ETL
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control
Categories and Features
Data Extraction
Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction
Data Warehouse
Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge
ETL
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control
Integration
Dashboard
ETL - Extract / Transform / Load
Metadata Management
Multiple Data Sources
Web Services