The Dataiku platform unifies data work from analytics to Generative AI. It supports enterprise analytics with visual, cloud-based tooling for data preparation, visualization, and workflow automation.
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Informatica PowerCenter (legacy)
Score 9.1 out of 10
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Informatica PowerCenter was data integration technology designed to form the foundation for data integration initiatives, application migration, or analytics. It is a legacy product.
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Pricing
Dataiku
Informatica PowerCenter (legacy)
Editions & Modules
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Pricing Offerings
Dataiku
Informatica PowerCenter (legacy)
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
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Community Pulse
Dataiku
Informatica PowerCenter (legacy)
Features
Dataiku
Informatica PowerCenter (legacy)
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Dataiku
8.6
5 Ratings
3% above category average
Informatica PowerCenter (legacy)
-
Ratings
Connect to Multiple Data Sources
8.05 Ratings
00 Ratings
Extend Existing Data Sources
10.04 Ratings
00 Ratings
Automatic Data Format Detection
10.05 Ratings
00 Ratings
MDM Integration
6.52 Ratings
00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Dataiku
10.0
5 Ratings
17% above category average
Informatica PowerCenter (legacy)
-
Ratings
Visualization
10.05 Ratings
00 Ratings
Interactive Data Analysis
10.05 Ratings
00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Dataiku
9.5
5 Ratings
15% above category average
Informatica PowerCenter (legacy)
-
Ratings
Interactive Data Cleaning and Enrichment
9.05 Ratings
00 Ratings
Data Transformations
9.05 Ratings
00 Ratings
Data Encryption
10.04 Ratings
00 Ratings
Built-in Processors
10.04 Ratings
00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Dataiku
8.5
5 Ratings
1% above category average
Informatica PowerCenter (legacy)
-
Ratings
Multiple Model Development Languages and Tools
8.05 Ratings
00 Ratings
Automated Machine Learning
8.05 Ratings
00 Ratings
Single platform for multiple model development
8.05 Ratings
00 Ratings
Self-Service Model Delivery
10.04 Ratings
00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Dataiku
8.0
5 Ratings
6% below category average
Informatica PowerCenter (legacy)
-
Ratings
Flexible Model Publishing Options
8.05 Ratings
00 Ratings
Security, Governance, and Cost Controls
8.05 Ratings
00 Ratings
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Dataiku
-
Ratings
Informatica PowerCenter (legacy)
8.5
18 Ratings
3% above category average
Connect to traditional data sources
00 Ratings
9.018 Ratings
Connecto to Big Data and NoSQL
00 Ratings
8.014 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Dataiku
-
Ratings
Informatica PowerCenter (legacy)
7.5
18 Ratings
7% below category average
Simple transformations
00 Ratings
8.018 Ratings
Complex transformations
00 Ratings
7.018 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Dataiku
-
Ratings
Informatica PowerCenter (legacy)
8.2
18 Ratings
5% above category average
Data model creation
00 Ratings
9.015 Ratings
Metadata management
00 Ratings
8.016 Ratings
Business rules and workflow
00 Ratings
9.018 Ratings
Collaboration
00 Ratings
6.116 Ratings
Testing and debugging
00 Ratings
9.017 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Dataiku is an awesome tool for data scientists. It really makes our lives easier. It is also really good for non technical users to see and follow along with the process. I do think that people can fall into the trap of using it without any knowledge at all because so much is automated, but I dont think that is the fault of Dataiku.
1.- Scenaries with poor sources of data is not recomended (Very bad ROI). The solution is for medium-big enterprises with a lot of sources of data and users. 2.- Bank and finance enviroment to integrate differente data form trading, Regulatory reports, decisions makers, fraud and financial crimes because in this kind of scenary the quality of data is the base of the business. 3.- Departments of development and test of applications in enterprises because you can design enviroments, out of the production systems, to development and test the new API's or updateds made.
Informatica Powercenter is an innovative software that works with ETL-type data integration. Connectivity to almost all the database systems.
Great documentation and customer support.
It has a various solution to address data quality issues. data masking, data virtualization. It has various supporting tools or MDM, IDQ, Analyst, BigData which can be used to analyze data and correct it.
The integrated windows of frontend and backend in web applications make it cumbersome for the developer.
When dealing with multiple data flows, it becomes really confusing, though they have introduced a feature (Zones) to cater to this issue.
Bundling, exporting, and importing projects sometimes create issues related to code environment. If the code environment is not available, at least the schema of the flow we should be able to import should be.
There are too many ways to perform the same or similar functions which in turn makes it challenging to trace what a workflow is doing and at which point (ex. sessions can be designed as static or re-usable and the override can occur at the session or workflow, or both which can be counter productive and confusing when troubleshooting).
The power in structured design is a double edged sword. Simple tasks for a POC can become cumbersome. Ex. if you want to move some data to test a process, you first have to create your sources by importing them which means an ODBC connection or similar will need to be configured, you in turn have to develop your targets and all of the essential building blocks before being able to begin actual development. While I am on sources and targets, I think of a table definition as just that and find it counter intuitive to have to design a table as both a source and target and manage them as different objects. It would be more intuitive to have a table definition and its source/target properties defined by where you drag and drop it in the mapping.
There are no checkpoints or data viewer type functions without designing an entire mapping and workflow. If you would like to simply run a job up to a point and check the throughput, an entire mapping needs to be completed and you would workaround this by creating a flat file target.
The user experience is very good. Everything feels intuitive and "flows" (sorry excuse the pun) so nicely, and the customization level is also appropriate to the tool. Even as a newer data scientist, it felt easy to use and the explanations/tutorials were very good. The documentation is also at a good level
Positives; - Multi User Development Environment - Speed of transformation - Seamless integration between other Informatica products. Negatives; - There should be less windows to maintain developers' focus while using. You probably need 2 big monitors when you start development with Informatica Power Center. - Oracle Analytical functions should be natively used. - E-LT support as well as ETL support.
PowerCenter is robust and fast, and it does a great job meeting all the needs, not just the most commercially vocal needs. In the hands of an expert power user, you can accomplish almost anything with your data. It is not for new users or intermittent users-- for that the Cloud version is a better fit. Be prepared for costly connectors (priced differently for each source or destination you are working with), and just be planful of your projects so you are not paying for connectors you no longer need or want
The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.
Informatica power center is a leader of the pack of ETL tools and has some great abilities that make it stand out from other ETL tools. It has been a great partner to its clients over a long time so it's definitely dependable. With all the great things about Informatica, it has a bit of tech burden that should be addressed to make it more nimble, reduce the learning curve for new developers, provide better connectivity with visualization tools.
Anaconda is mainly used by professional data scientists who have profound knowledge of Python coding, mainly used for building some new algorithm block or some optimization, then the module will be integrated into the Dataiku pipeline/workflow. While Dataiku can be used by even other kinds of users.
While Talend offers a much more comfortable interface to work with, Informatica's forte is performance. And on that front, Informatica Enterprise Data Integration certainly leaves Talend in the dust. For a more back-end-centric use case, Informatica is certainly the ETL tool of choice. On the other hand, if business users would be using the tool, then Talend would be the preferred tool.
The data pipeline automation capability of Informatica means that few resources are needed to pre-process the data that ultimately resides in a Data Warehouse. Once a workflow is implemented, manual intervention is not needed.
PowerCenter did require more resources and time for installation and configuration than was expected/planned for.
The lack of or minimal support of unstructured data means that newer sources of dynamic/changing data cannot be easily processed/transformed through PowerCenter workflows.