IBM DataStage vs. IBM Watson Studio on Cloud Pak for Data

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM DataStage
Score 7.6 out of 10
N/A
IBM® DataStage® is a data integration tool that helps users to design, develop and run jobs that move and transform data. At its core, the DataStage tool supports extract, transform and load (ETL) and extract, load and transform (ELT) patterns. A basic version of the software is available for on-premises deployment, and the cloud-based DataStage for IBM Cloud Pak® for Data offers automated integration capabilities in a hybrid or multicloud environment.N/A
IBM Watson Studio
Score 10.0 out of 10
N/A
IBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies AI lifecycle management and accelerates time to value with an open, flexible multicloud architecture.N/A
Pricing
IBM DataStageIBM Watson Studio on Cloud Pak for Data
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
IBM DataStageIBM Watson Studio
Free Trial
YesNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
IBM DataStageIBM Watson Studio on Cloud Pak for Data
Features
IBM DataStageIBM Watson Studio on Cloud Pak for Data
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
IBM DataStage
9.5
Ratings
12% above category average
IBM Watson Studio on Cloud Pak for Data
-
Ratings
Connect to traditional data sources10.00 Ratings00 Ratings
Connecto to Big Data and NoSQL9.00 Ratings00 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
IBM DataStage
8.0
Ratings
2% below category average
IBM Watson Studio on Cloud Pak for Data
-
Ratings
Simple transformations8.00 Ratings00 Ratings
Complex transformations8.00 Ratings00 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
IBM DataStage
6.3
Ratings
23% below category average
IBM Watson Studio on Cloud Pak for Data
-
Ratings
Data model creation5.00 Ratings00 Ratings
Metadata management5.00 Ratings00 Ratings
Business rules and workflow6.00 Ratings00 Ratings
Collaboration6.00 Ratings00 Ratings
Testing and debugging6.00 Ratings00 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
IBM DataStage
6.0
Ratings
30% below category average
IBM Watson Studio on Cloud Pak for Data
-
Ratings
Integration with data quality tools6.00 Ratings00 Ratings
Integration with MDM tools6.00 Ratings00 Ratings
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
IBM DataStage
-
Ratings
IBM Watson Studio on Cloud Pak for Data
8.1
Ratings
3% below category average
Connect to Multiple Data Sources00 Ratings8.00 Ratings
Extend Existing Data Sources00 Ratings8.00 Ratings
Automatic Data Format Detection00 Ratings10.00 Ratings
MDM Integration00 Ratings6.40 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
IBM DataStage
-
Ratings
IBM Watson Studio on Cloud Pak for Data
10.0
Ratings
18% above category average
Visualization00 Ratings10.00 Ratings
Interactive Data Analysis00 Ratings10.00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
IBM DataStage
-
Ratings
IBM Watson Studio on Cloud Pak for Data
9.5
Ratings
15% above category average
Interactive Data Cleaning and Enrichment00 Ratings10.00 Ratings
Data Transformations00 Ratings10.00 Ratings
Data Encryption00 Ratings8.00 Ratings
Built-in Processors00 Ratings10.00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
IBM DataStage
-
Ratings
IBM Watson Studio on Cloud Pak for Data
9.5
Ratings
13% above category average
Multiple Model Development Languages and Tools00 Ratings10.00 Ratings
Automated Machine Learning00 Ratings10.00 Ratings
Single platform for multiple model development00 Ratings10.00 Ratings
Self-Service Model Delivery00 Ratings8.00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
IBM DataStage
-
Ratings
IBM Watson Studio on Cloud Pak for Data
8.0
Ratings
6% below category average
Flexible Model Publishing Options00 Ratings9.00 Ratings
Security, Governance, and Cost Controls00 Ratings7.00 Ratings
User Ratings
IBM DataStageIBM Watson Studio on Cloud Pak for Data
Likelihood to Recommend
8.0
(0 ratings)
8.0
(0 ratings)
Likelihood to Renew
-
(0 ratings)
8.2
(0 ratings)
Usability
8.0
(0 ratings)
9.6
(0 ratings)
Availability
-
(0 ratings)
8.2
(0 ratings)
Performance
9.0
(0 ratings)
8.2
(0 ratings)
Support Rating
9.6
(0 ratings)
8.2
(0 ratings)
In-Person Training
-
(0 ratings)
8.2
(0 ratings)
Online Training
-
(0 ratings)
8.2
(0 ratings)
Implementation Rating
-
(0 ratings)
7.3
(0 ratings)
Product Scalability
-
(0 ratings)
8.2
(0 ratings)
Vendor post-sale
-
(0 ratings)
7.3
(0 ratings)
Vendor pre-sale
-
(0 ratings)
8.2
(0 ratings)
User Testimonials
IBM DataStageIBM Watson Studio on Cloud Pak for Data
Likelihood to Recommend
Excellent Cloud data mapping tool and easy creating multiple project data analytics in real-time and the report distribution are excellent via this IBM product. Easy tool to provide data visualization and the integration is effective and helpful to migrating huge amounts of data across other platforms and different websites insights gathering.
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It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.
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Pros
  • Very reliable in handling data extraction, data transformation and loading
  • Flexibility in connecting to different type of databases, relational or non-relational
  • Great features such as parallel processing, hash handling, etc.
  • You can also take advantage of its FTP functions, and scheduling features if you need to.
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  • Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
  • SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
  • Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
  • Estimator validation lets data scientists test and prove different models.
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Cons
  • You must understand and know the algorithms, since the wrong use of them generates more time in processing.
  • Metadata. You need to develop with connectors, and taking all the Metadata from the menu, all the data that you complete manually, you can't track it.
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  • The cost is steep and so only companies with resources can afford it
  • It will be nice to have Chinese versions so that Chinese engineers can also use it easily
  • It takes a while to learn how to input different kinds of skin defects for detection
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Likelihood to Renew
No answers on this topic
because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)
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Usability
Because it is a flexible tool that can manage many flows and create a strong solution with a interesting use of variables. Easy to scale up as you can copy jobs arleady build and modify them. SQL queries allow to be fast in development and have the pushdown feature, but you loose a little of user friendly look. Metadata management is not strong as a visual feature, but can be determine by job codes.
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The UI flawlessly merges this offering by providing a neat, minimal, responsive interface
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Reliability and Availability
No answers on this topic
From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected
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Performance
It could load thousands of records in seconds. But in the Parallel version, you need to understand how to particionate the data. If you use the algorithms erroneously, or the functionalities that it gives for the parsing of data, the performance can fall drastically, even with few records. It is necessary to have people with experience to be able to determine which algorithm to use and understand why.
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Never had slow response even on our very busy network
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Support Rating
IBM offers different levels of support but in my experience being and IBM shop helps to get direct support from more knowledgeable technicians from IBM. Not sure on the cost of having this kind of support, but I know there's also general support and community blogs and websites on the Internet make it easy to troubleshoot issues whenever there's need for that.
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I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path
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In-Person Training
No answers on this topic
The trainers on the job are very smart with solutions and very able in teaching
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Online Training
No answers on this topic
The Platform is very handy and suggests further steps according my previous interests
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Implementation Rating
No answers on this topic
It surprised us with unpredictable case of use and brand new points of view
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Alternatives Considered
No, it wasn’t my decision to use such an ETL product. I’m just the administrator at this point. I’ve heard there are other products there that are even on cloud support. That is much easier to use, more agile, and user-friendly. That doesn’t have that barrier from user to administrator to the developer standpoint.
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The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.
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Scalability
No answers on this topic
It helped us in getting from 0 to DSX without getting lost
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Return on Investment
  • Not directly related to ROI or cost figures. Only comment here is that IBM tools tend to be more costly than average ETL tools, but it depends on if the company is an IBM shop.
  • One positive aspect is the company has had not a need to switch ETL tool for years.
  • Upgrading to newer versions of the tool brings flexibility in the tool and up-to-date features in relation to other applications.
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  • Could instantly show data driven insights to drive 20% incremental revenue over existing results
  • Still don't have a real use case for unstructured data like twitter feed
  • Some of the insights around user actions have driven new projects to automate mundane tasks
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ScreenShots