Reviews from AWS Marketplace
0 AWS reviews
-
5 star0
-
4 star0
-
3 star0
-
2 star0
-
1 star0
External reviews
External reviews are not included in the AWS star rating for the product.
User friendly interface for business and non-technical oriented people.
What do you like best about the product?
Has a friendly interface and SAAS which is easier to upgrade between version updates
What do you dislike about the product?
It rund on top of database so it will always be a bit slower than running queries directly in database.
What problems is the product solving and how is that benefiting you?
Enabaling data manipulation, transformation, modeling, predictive, prescriptive, etc.
- Leave a Comment |
- Mark review as helpful
The one tool to rule theM alL!
What do you like best about the product?
It's hard to highlight a single feature so I will have to mention several:
- The ability to serve different personas, such as "coders" and "clickers" means that Dataiku is well received by non Data Scientists. Yet for those that prefer to code they can code as well.
- The ability to integrate with so many technologies and compute and storage engines both for ingestion and parallel compute means there is no job too big to be done in Dataiku when you use the right technology.
- The Flow makes complex data pipelines simple to understand and design. It also makes it very esy to use.
- The integration of Jupyter Notebooks, built-in Git versioning and Python code environment management makes the creation of new projects and project management very easily.
- And finally I would like to specifically mention their incredible Support team. In my IT career I have dealt with a myriad of enterprise software vendors including all the large ones and I can honestly say that Dataiku Support is the best one I have dealt with by miles. Response speed is amazing even at weekends or out of hours. It's clear they run a 24x7 operation across the globe. The quality and quantity of the responses from Support es exceptional. Even when asking for code snippets to use Dataiku API, which most vendors will normally charge for under professional services, we have been surprised by their willingness to help and always achieved a outcome.
- The ability to serve different personas, such as "coders" and "clickers" means that Dataiku is well received by non Data Scientists. Yet for those that prefer to code they can code as well.
- The ability to integrate with so many technologies and compute and storage engines both for ingestion and parallel compute means there is no job too big to be done in Dataiku when you use the right technology.
- The Flow makes complex data pipelines simple to understand and design. It also makes it very esy to use.
- The integration of Jupyter Notebooks, built-in Git versioning and Python code environment management makes the creation of new projects and project management very easily.
- And finally I would like to specifically mention their incredible Support team. In my IT career I have dealt with a myriad of enterprise software vendors including all the large ones and I can honestly say that Dataiku Support is the best one I have dealt with by miles. Response speed is amazing even at weekends or out of hours. It's clear they run a 24x7 operation across the globe. The quality and quantity of the responses from Support es exceptional. Even when asking for code snippets to use Dataiku API, which most vendors will normally charge for under professional services, we have been surprised by their willingness to help and always achieved a outcome.
What do you dislike about the product?
The GUI is inconsistent at times on how certain actions need to be done. While we found Dataiku Support to be exceptional we had less luck in getting new features implemented. Bug fixing has also been slow in our view even though Dataiku has a good release schedule (they usually release a patch release every 2 weeks!). In our view core features and bug fixing should take more priority than LLM features and other new features.
It needs more work to improve ML Ops. For instance model drift is also available via an additional plugin and only on certain algorithms. This should be a core capability. Collaboration could also be improved as there are some concurrency issues that need to be fixed.
It needs more work to improve ML Ops. For instance model drift is also available via an additional plugin and only on certain algorithms. This should be a core capability. Collaboration could also be improved as there are some concurrency issues that need to be fixed.
What problems is the product solving and how is that benefiting you?
Dataiku is our main ML learning platform for all our advanced analytics work loads.
Dataiku Makes Data-Driven Solutions Simple and Fast
What do you like best about the product?
What I like most about Dataiku is how easy it is to use for creating and managing data-driven solutions. The platform has a very friendly interface, so even if you are not expert, you can start to work on your data projects without much trouble. It is simple to make data pipelines, do analytics, and even create machine learning models, all in one place. Also, I really like that Dataiku can connect very easily with different cloud services and data sources. This makes my work much faster and more efficient because I do not need to spend much time on integration. Overall, Dataiku helps a lot to move quickly from raw data to useful results.
What do you dislike about the product?
There are not many things I dislike about Dataiku, but sometimes, if you want to use more advanced features, you need to have deeper technical knowledge. For someone who is just starting, this can be a bit difficult. Also, because Dataiku always adds new features, sometimes it is not easy to keep up and find the best way to use everything. Sometimes, when integrating with some cloud systems, there can be small technical problems, but usually there is good support and documentation to help.
What problems is the product solving and how is that benefiting you?
I use it for OCR automations to extract data from documents, which saves me a lot of manual work.
I use Dataiku to create RAG chatbots and connect with large language models, which makes it easy to answer questions and help users quickly.
I use Dataiku to make and automate operational reports.
Another use case is predictive maintenance for my equipment.
I use Dataiku to create RAG chatbots and connect with large language models, which makes it easy to answer questions and help users quickly.
I use Dataiku to make and automate operational reports.
Another use case is predictive maintenance for my equipment.
Great Event
What do you like best about the product?
Thanks for hosting us in the AI conference. Got to learn a lot!!
What do you dislike about the product?
Tough to integrate with existing organization tools
What problems is the product solving and how is that benefiting you?
Using analytics to solve business solutions
Alteryx to Dataiku Convert
What do you like best about the product?
Dataiku is very flexible and allows me to utilize fewer tools compared to Alteryx. This has allowed me to create smaller flows making them easier to follow.
What do you dislike about the product?
It can be hard to find certain steps since there are many tools inside of one tool.
What problems is the product solving and how is that benefiting you?
I work with a lot of data, especially dirty data and Dataiku allows me to manipulate the data I receive and turn it into something I can interpret.
Great orchestration tool for AI/ML/GenAI use cases
What do you like best about the product?
I love the fact that Dataiku makes the orchestration of AI/ML/GenAI models so easy and everything is in a single place.
What do you dislike about the product?
There is nothing specific that I dislike but there were certain features that we discovered as part of exploring Dataiku but were later fixed by the Dataiku team. Given it is an evolving product and the AI landscape is changing so fast, they need to catch up faster than their competitors.
What problems is the product solving and how is that benefiting you?
Making the orchestration easier not having to worry about building my own connectors with different sources, code environments, and having to write long lines of code. I can simply use the recipes that are inbuilt.
Review of dataiku as a developer
What do you like best about the product?
Bringing everything into one place, from data to model development and deployment.
What do you dislike about the product?
Clusters shutting down for no reason, not that much stability in connections and the time it takes to add a library to a template cluster and rebuilding it.
What problems is the product solving and how is that benefiting you?
It is benefiting prototyping an app and delivering it to clients
Positive experiences using dataiku
What do you like best about the product?
I appreciate the low code environment. Makes it easier for a newer user to understand.
What do you dislike about the product?
Although there are plenty of resources to review use cases, it can be unintuitive for new users.
What problems is the product solving and how is that benefiting you?
Using the OCR capabilities to read and extract data from high volumes of documents uploaded to our sharepoint folder.
Dataiku Makes Data Analytics Accessible to End Users
What do you like best about the product?
Dataiku’s vast connections to different data structures allows backend users to connect multiple end users’ data together to make meaningful connections for the end users through user friendly and customizable web apps.
What do you dislike about the product?
Even though Dataiku is great in many aspects, understanding all that is has to offer takes some time and I am always finding new things I didn’t know it could do. Understanding how it can be combined with what we have already is challenging.
What problems is the product solving and how is that benefiting you?
Being able to have technical and non technical users in the same space allowing for technical users to build in the same platform the end users can view.
Dataiku is Awesome
What do you like best about the product?
🔄 Smart Data Preparation
Transform raw data into structured, ready-to-use assets using intuitive tools enhanced by AI-driven suggestions, auto-schema detection, and intelligent type recognition.
🧪 Continuous Development
Support agile analytics with a CI/CD-style environment where data flows, scripts, and models evolve continuously, promoting rapid iteration and improvement.
⚙️ Ease of Implementation
Minimize setup complexity with modular components, drag-and-drop interfaces, and seamless integration with existing data ecosystems (cloud, on-prem, hybrid).
✅ Robust Data Validation
Ensure data quality through built-in validation checks, profiling dashboards, and the flexibility to implement custom Python logic for complex or domain-specific rules.
🧠 Scenario Building
Model and simulate different business or analytical scenarios using parameterized workflows, branching logic, and reusable components to support what-if analyses.
🌀 Flow Zones
Organize and manage data processes in "Flow Zones" — clearly defined stages (e.g., Ingest → Transform → Validate → Output) that make pipeline orchestration transparent and scalable.
📚 Integrated WIKI Page
Empower collaboration and knowledge sharing with an embedded WIKI page. Document logic, share best practices, track changes, and onboard new users effortlessly.
Transform raw data into structured, ready-to-use assets using intuitive tools enhanced by AI-driven suggestions, auto-schema detection, and intelligent type recognition.
🧪 Continuous Development
Support agile analytics with a CI/CD-style environment where data flows, scripts, and models evolve continuously, promoting rapid iteration and improvement.
⚙️ Ease of Implementation
Minimize setup complexity with modular components, drag-and-drop interfaces, and seamless integration with existing data ecosystems (cloud, on-prem, hybrid).
✅ Robust Data Validation
Ensure data quality through built-in validation checks, profiling dashboards, and the flexibility to implement custom Python logic for complex or domain-specific rules.
🧠 Scenario Building
Model and simulate different business or analytical scenarios using parameterized workflows, branching logic, and reusable components to support what-if analyses.
🌀 Flow Zones
Organize and manage data processes in "Flow Zones" — clearly defined stages (e.g., Ingest → Transform → Validate → Output) that make pipeline orchestration transparent and scalable.
📚 Integrated WIKI Page
Empower collaboration and knowledge sharing with an embedded WIKI page. Document logic, share best practices, track changes, and onboard new users effortlessly.
What do you dislike about the product?
While DSS offers a powerful visual interface and flexibility, working with large datasets often introduces significant friction, particularly during scenario execution and debugging.
🚧 Key Pain Points:
Performance Bottlenecks:
Executing complex scenarios on large datasets directly in the DSS engine is slow and resource-intensive, often making it impractical for time-sensitive analytics.
Dependence on External Engines:
To achieve acceptable performance, teams must offload processing to SQL or Spark engines, requiring:
Additional infrastructure setup (clusters, permissions, connections)
Advanced SQL or PySpark expertise, which can be a barrier for data analysts or citizen data scientists.
Debugging Overhead:
Troubleshooting large workflows is cumbersome due to:
Limited transparency into underlying code execution
Multi-layered architecture (visual flow → Spark/SQL translation → execution engine)
Slower iteration cycles, especially with Spark
🚧 Key Pain Points:
Performance Bottlenecks:
Executing complex scenarios on large datasets directly in the DSS engine is slow and resource-intensive, often making it impractical for time-sensitive analytics.
Dependence on External Engines:
To achieve acceptable performance, teams must offload processing to SQL or Spark engines, requiring:
Additional infrastructure setup (clusters, permissions, connections)
Advanced SQL or PySpark expertise, which can be a barrier for data analysts or citizen data scientists.
Debugging Overhead:
Troubleshooting large workflows is cumbersome due to:
Limited transparency into underlying code execution
Multi-layered architecture (visual flow → Spark/SQL translation → execution engine)
Slower iteration cycles, especially with Spark
What problems is the product solving and how is that benefiting you?
✅ Automated Data Validation
Prebuilt validation rules with customizable logic (Python/SQL)
Auto-profiling and anomaly detection at ingest
Validation integrated directly into data pipelines and alerts
🧠 Smart Data Ingestion & Reading
Intelligent schema detection, auto-type inference, and data previews
Efficient sampling of large datasets without full-load requirements
Flexible connectors for cloud, on-prem, and APIs with minimal setup
📊 Quick Insights Through Data Visualization
One-click data summaries with charts, distributions, and KPIs
Drill-down capabilities for root-cause analysis
Seamless embedding of visuals into flows, dashboards, and WIKI pages
🔐 Built-in Data Governance
Centralized metadata catalog and lineage tracking
Role-based access controls and audit trails
Versioning, change tracking, and approval workflows
Integration with data privacy and compliance frameworks (GDPR, HIPAA, etc.)
Prebuilt validation rules with customizable logic (Python/SQL)
Auto-profiling and anomaly detection at ingest
Validation integrated directly into data pipelines and alerts
🧠 Smart Data Ingestion & Reading
Intelligent schema detection, auto-type inference, and data previews
Efficient sampling of large datasets without full-load requirements
Flexible connectors for cloud, on-prem, and APIs with minimal setup
📊 Quick Insights Through Data Visualization
One-click data summaries with charts, distributions, and KPIs
Drill-down capabilities for root-cause analysis
Seamless embedding of visuals into flows, dashboards, and WIKI pages
🔐 Built-in Data Governance
Centralized metadata catalog and lineage tracking
Role-based access controls and audit trails
Versioning, change tracking, and approval workflows
Integration with data privacy and compliance frameworks (GDPR, HIPAA, etc.)
showing 71 - 80