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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.)
New User to Dataiku
What do you like best about the product?
I like the possibilities of the AI features of Dataiku. I am a new user so I do not have a lot of use yet.
What do you dislike about the product?
I have limited use at the moment so I do not any dislikes so far. My company is transferring all of our Alteryx workflows to Dataiku. Alteryx is my mine tool that I utilize. My only dislike is the amount of time it'll take to transfer all workflows to Dataiku.
What problems is the product solving and how is that benefiting you?
Dataiku will be taking the place of all Alteryx workflows
Accelerating Data Workflows with Dataiku
What do you like best about the product?
It strikes a good balance between no-code workflows and also helps integrate code when required. It's a visual tool that handles the whole lifecycle of the data, it has the visual workflow style of KNIME and FME with the depth and flexibility of custom coding and cloud-based architecture. Great customer support by the way! It's also an intuitive platform to have new users onboarded on.
What do you dislike about the product?
Performance can lag with large datasets, and partitioning isn't intuitive. Version controlling is great, but the roll back does not always work as intended especially within the coding environments.
What problems is the product solving and how is that benefiting you?
Collaboration, versioning and non-local infrastructure. Those were the major pain-points that Dataiku alleviated.
Simplifying the Machine Learning Workflow
What do you like best about the product?
Love how this app makes ML development so easy! It takes care of the complicated stuff and lets you focus on building cool models
What do you dislike about the product?
after some recent updates, we've experienced a few issues that disrupted our workflow.
What problems is the product solving and how is that benefiting you?
Dataiku simplifies the machine learning workflow by providing built-in recipes that eliminate the need to rewrite repetitive code. This allows me to focus more on the overall pipeline and strategy, rather than getting bogged down in routine coding tasks. It saves time and helps maintain consistency across projects
Revolutionizing the way we interact with data
What do you like best about the product?
Dataiku is unbelievably easy to use and implement. It is almost unreal how powerful it is but even with all of the power behind it the set up and mapping is so intuitive you feel like you must be missing steps. It is slowly becoming a daily used system for our company and a cornerstone as we start to modernize our data. The support we have received from the internal team has been nothing short of fantastic. They are there to answer questions, walk you through implementations, and make the already easy integration process, even easier.
What do you dislike about the product?
I haven't found anything I have disliked up until this point.
What problems is the product solving and how is that benefiting you?
We wanted a solution that would allow non technical people interact with our data and gain insights. We wanted them to be able to do this independently and without having a team of report order takers to fulfill requests or random questions. That team still exists but for larger and more strategic requests.
Dataiku has changed my job for the better!
What do you like best about the product?
Dataiku excels at providing a collaborative, end-to-end platform that bridges the gap between visual data preparation and advanced coding for diverse teams.
What do you dislike about the product?
Dataiku Application could be better. The concept of instances and concurrent users sometimes can conflict.
What problems is the product solving and how is that benefiting you?
My job is to translate manual data related processes into dataiku flows and integrations.
Great product
What do you like best about the product?
Extremely easy to use and cheap, excellent customer support
What do you dislike about the product?
Difficulty in scalability of the data and feature integration
What problems is the product solving and how is that benefiting you?
Accelerating our AI use cases and their solutions can be easily tailored to the different industries of our clients
Best Platform to work with
What do you like best about the product?
Unified distributed computing platform with Age tic AI.
What do you dislike about the product?
Becoming thick client and getting difficult to manage
What problems is the product solving and how is that benefiting you?
Market forecasts and sentiment analysis
DataIku customer
What do you like best about the product?
Their customer success team and user friendly platform.
What do you dislike about the product?
Can be overwhelming for beginners in particular.
What problems is the product solving and how is that benefiting you?
Data wrangling and predictive modeling.
Great program
What do you like best about the product?
Love that I can combine data from multiple sources in one spot
What do you dislike about the product?
Can be hard if as it is not intuitive without training
What problems is the product solving and how is that benefiting you?
It is giving insights
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