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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

Enterprise Enabler brings together disparate information from various sources and isolated data sets, providing a cohesive view within a unified platform; this includes data housed in the cloud, distributed across isolated databases, stored on instruments, located in Big Data repositories, or found within different spreadsheets and documents. By seamlessly integrating all your data, it empowers you to make timely and well-informed business choices. The system creates logical representations of data sourced from its original locations, enabling you to effectively reuse, configure, test, deploy, and monitor everything within a single cohesive environment. This allows for the analysis of your business data as events unfold, helping to optimize asset utilization, reduce costs, and enhance your business processes. Remarkably, our deployment timeline is typically 50-90% quicker, ensuring that your data sources are connected and operational in record time, allowing for real-time decision-making based on the most current information available. With this solution, organizations can enhance collaboration and efficiency, leading to improved overall performance and strategic advantage in the market.

Description

Boost the pace of AI innovation through cloud-native data integration offered by IBM Cloud Pak for Data. With AI-driven data integration capabilities accessible from anywhere, the effectiveness of your AI and analytics is directly linked to the quality of the data supporting them. Utilizing a modern container-based architecture, IBM® DataStage® for IBM Cloud Pak® for Data ensures the delivery of superior data. This solution merges top-tier data integration with DataOps, governance, and analytics within a unified data and AI platform. By automating administrative tasks, it helps in lowering total cost of ownership (TCO). The platform's AI-based design accelerators, along with ready-to-use integrations with DataOps and data science services, significantly hasten AI advancements. Furthermore, its parallelism and multicloud integration capabilities enable the delivery of reliable data on a large scale across diverse hybrid or multicloud settings. Additionally, you can efficiently manage the entire data and analytics lifecycle on the IBM Cloud Pak for Data platform, which encompasses a variety of services such as data science, event messaging, data virtualization, and data warehousing, all bolstered by a parallel engine and automated load balancing features. This comprehensive approach ensures that your organization stays ahead in the rapidly evolving landscape of data and AI.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

ActiveBatch Workload Automation No 
Control-M No 
FairCom DB No 
FairCom EDGE No 
IBM Cloud Pak for Applications No 
IBM Watson Studio No 
IRI FieldShield No 
MettleCI No 
Origina No 
Pantomath No 
Stonebranch No 

Integrations

ActiveBatch Workload Automation Yes 
Control-M Yes 
FairCom DB Yes 
FairCom EDGE Yes 
IBM Cloud Pak for Applications Yes 
IBM Watson Studio Yes 
IRI FieldShield Yes 
MettleCI Yes 
Origina Yes 
Pantomath Yes 
Stonebranch Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Stone Bond Technologies

Founded

2002

Country

United States

Website

stonebond.com

Vendor Details

Company Name

IBM

Founded

1911

Country

United States

Website

www.ibm.com/products/infosphere-datastage

Product Features

Big Data

Collaboration No 
Data Blends No 
Data Cleansing No 
Data Mining No 
Data Visualization No 
Data Warehousing No 
High Volume Processing No 
No-Code Sandbox No 
Predictive Analytics No 
Templates No 

Business Intelligence

Ad Hoc Reports No 
Benchmarking No 
Budgeting & Forecasting No 
Dashboard Yes 
Data Analysis No 
Key Performance Indicators No 
Natural Language Generation (NLG) No 
Performance Metrics No 
Predictive Analytics No 
Profitability Analysis No 
Strategic Planning No 
Trend / Problem Indicators No 
Visual Analytics No 

Data Warehouse

Ad hoc Query No 
Analytics No 
Data Integration No 
Data Migration No 
Data Quality Control No 
ETL - Extract / Transfer / Load No 
In-Memory Processing No 
Match & Merge No 

ETL

Data Analysis No 
Data Filtering No 
Data Quality Control No 
Job Scheduling No 
Match & Merge No 
Metadata Management No 
Non-Relational Transformations No 
Version Control No 

Integration

Dashboard Yes 
ETL - Extract / Transform / Load Yes 
Metadata Management Yes 
Multiple Data Sources No 
Web Services No 

Master Data Management

Data Governance No 
Data Masking No 
Data Source Integrations No 
Hierarchy Management No 
Match & Merge No 
Metadata Management No 
Multi-Domain No 
Process Management No 
Relationship Mapping No 
Visualization No 

Product Features

Big Data

Collaboration No 
Data Blends No 
Data Cleansing No 
Data Mining No 
Data Visualization No 
Data Warehousing No 
High Volume Processing No 
No-Code Sandbox No 
Predictive Analytics No 
Templates No 

Data Lineage

Database Change Impact Analysis No 
Filter Lineage Links No 
Implicit Connection Discovery No 
Lineage Object Filtering No 
Object Lineage Tracing No 
Point-in-Time Visibility No 
User/Client/Target Connection Visibility No 
Visual & Text Lineage View No 

ETL

Data Analysis No 
Data Filtering No 
Data Quality Control No 
Job Scheduling No 
Match & Merge No 
Metadata Management No 
Non-Relational Transformations No 
Version Control No 

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