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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

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.

Description

Liquid Data Mapper is an adaptable tool designed for data transformation and mapping that allows users to create diverse mapping combinations, including integration from different data sources. It features a visual interface that illustrates the mapping process across various data sources and destinations. Users can execute transformations directly within Liquid Studio or generate code for incorporation into their applications. The debugger allows for step-by-step examination with features like breakpoints, stack traces, and variable watch windows for enhanced troubleshooting. Additionally, Liquid Studio Data Mapper boasts an easy-to-use graphical interface for crafting data transformations, enabling users to connect multiple data inputs to various outputs while implementing conditional logic, filtering, string manipulation, statistical functions, among other capabilities. This versatility makes it a valuable asset for managing complex data workflows efficiently.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

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 
Mazaal AI No 
MettleCI Yes 
Origina Yes 
Pantomath Yes 
Stonebranch Yes 
XML No 

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 
Mazaal AI Yes 
MettleCI No 
Origina No 
Pantomath No 
Stonebranch No 
XML Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$149 one-time payment
Free Trial Yes 
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 No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
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

IBM

Founded

1911

Country

United States

Website

www.ibm.com/products/infosphere-datastage

Vendor Details

Company Name

Liquid Technologies

Website

www.liquid-technologies.com/data-mapper

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 

Product Features

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