Dataiku vs SAS Viya

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Our analysts compared Dataiku vs SAS Viya based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews and our own crowdsourced data from our free software selection platform.

Product Basics

Dataiku is a powerful data analytics platform designed to empower organizations with data-driven insights and machine learning capabilities. It offers a comprehensive suite of features, including data integration, preparation, and advanced machine learning, all within a user-friendly interface. Dataiku facilitates collaboration among data professionals and business users, streamlining the data analytics process. Its AutoML capabilities simplify machine learning model development, making it accessible to users with varying levels of expertise. Real-time insights and scalability are key benefits, allowing organizations to make timely decisions and adapt to changing data requirements. Despite some learning curve challenges, Dataiku remains a favored choice for medium and large businesses seeking robust data analytics solutions.
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SAS Viya is a cloud-based in-memory analytics engine that provides data visualization, reporting and analytics to businesses for actionable data insights. Powered by AI, it brings together visual analytics, visual statistics and data science for enterprises to achieve end-to-end self-service analytics. It uses a standardized code base with support for programming in R, Python, SAS, Java and Lua.

Deployable in the cloud, on-premises and hybrid environments, it integrates with a wide range of business applications through an agile, scalable architecture. The vendor offers an introductory 30-day free trial.

Pros
  • Comprehensive features
  • Powerful analytics capabilities
  • User-friendly interface
  • Scalable architecture
  • Strong support from SAS
Cons
  • Steep learning curve
  • Limited customization options
  • High cost of ownership
  • Potential vendor lock-in
  • Resource-intensive
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$4,000 Monthly
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$10,000 Annual
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Tailored to your specific needs
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Product Assistance

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Knowledge Base
24/7 Live Support
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Product Insights

  • Advanced Data Preparation: Dataiku offers powerful data preparation tools, allowing users to clean, transform, and enrich data easily. This ensures that data is ready for analysis and modeling.
  • Collaborative Environment: Dataiku provides a collaborative workspace where data scientists, analysts, and business users can work together seamlessly, fostering cross-functional teamwork.
  • Scalable Machine Learning: With Dataiku, organizations can build, deploy, and monitor machine learning models at scale. This empowers data-driven decision-making across the enterprise.
  • AutoML Capabilities: Dataiku's AutoML functionality automates the model selection and hyperparameter tuning process, making it easier for users to create accurate machine learning models.
  • Visual Data Exploration: Users can visually explore data, gaining valuable insights through interactive charts and graphs, simplifying the process of uncovering trends and patterns.
  • Model Deployment: Dataiku streamlines the deployment of machine learning models into production, ensuring that insights generated from data are put into action efficiently.
  • Data Governance: Dataiku offers robust data governance features, including data lineage, access controls, and auditing, to maintain data quality and compliance.
  • Automated Reporting: Users can create automated reports and dashboards to share insights with stakeholders, saving time and ensuring data-driven decisions are accessible to all.
  • Flexible Integration: Dataiku seamlessly integrates with a variety of data sources and tools, making it adaptable to existing data ecosystems and technologies.
  • Interpretable AI: Dataiku places a strong emphasis on model explainability, helping users understand why AI models make specific predictions, enhancing trust and transparency.
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  • Improve Decision-Making: Make informed business decisions by using historical and current proprietary information to derive analytical insights. Compute vast amounts of data faster and resolve complexities through parallel processing. Boost workflow efficiencies by deploying operational decisions that define real-time best actions at scale. 
  • Self-Service Analytics: Easily perform automated forecasting, goal-seeking and scenario analysis — no technical skills needed. Identify user sentiment through text analytics and incorporate geographical data for a complete picture of business metrics. 
  • Maximize ROI: Save time through built-in automation for data prep, feature engineering, algorithm selection and AI-powered data discovery. Innovate, rather than spending time on tedious data management and analytics tasks. 
  • Data Security: Ensure data encryption at rest and while moving across systems, in addition to auditing protocols. Connect to external data management systems like Oracle, Teradata, Facebook, Amazon and Esri seamlessly through Kerberos, SAML, OAuth and OpenID. 
  • Data Management: Import data by using an IDE or through REST APIs, and visualize and analyze it through self-service data prep. Join tables, apply functions and perform calculations, or drag-and-drop, pivot, and slice and dice to view desired metrics. 
  • Augmented Analytics: Identify relationships in data through automatically generated suggestions and guided analysis, and track anomalies and outliers. Tell data stories by generating easy-to-understand visuals and dashboard summaries in natural language. Derive meaningful insights for the future by creating what-if scenarios for forecasting and predictive analytics. 
  • Mobility: Access business reports on the go through a native mobile app that supports a variety of charts, graphs and tables. Configure app functionality per device for specific users to add and view reports, share links, add and view comments and view alerts. 
  • Scalable Architecture: Leverage its modular microservices architecture to scale as per business needs. Monitor and manage the health and configuration of individual microservices instances through the SAS Environment Manager. Deploys seamlessly to any type of environment, including the cloud, and runs on Cloud Foundry as a platform-as-a-service (PaaS). 
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  • Data Integration: Dataiku offers seamless data integration capabilities, allowing users to connect to various data sources, import data, and prepare it for analysis.
  • Machine Learning Studio: Users can build, train, and deploy machine learning models in a user-friendly visual interface, streamlining the data science workflow.
  • Data Preparation: Dataiku provides robust data preparation tools, enabling users to clean, transform, and enrich data efficiently for analysis.
  • AutoML: The AutoML functionality automates model selection and hyperparameter tuning, simplifying the process of creating accurate machine learning models.
  • Collaboration Hub: Dataiku offers a collaborative environment where data scientists, analysts, and business users can work together on data projects.
  • Model Deployment: Users can easily deploy machine learning models into production, ensuring that insights generated from data are put into action.
  • Visual Data Exploration: Dataiku enables users to visually explore data through interactive charts and graphs, facilitating data discovery and insights.
  • Data Governance: The platform includes robust data governance features, such as data lineage, access controls, and auditing, ensuring data quality and compliance.
  • Automated Reporting: Users can create automated reports and dashboards to share insights with stakeholders, streamlining communication.
  • Scalability: Dataiku is designed to scale with an organization's data needs, making it suitable for both small teams and large enterprises.
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  • Visualization and Reporting: Dig into data for in-depth analysis and view key business metrics through autonomous data exploration and manipulation. Create and customize interactive reports and charts to share with others across the organization for collaborative insight. Get suggestions on graphics best suited to display pertinent data through auto charting. 
  • Data Modeling: Analyze data with predictive models through regression, clustering and neural networks. Ensure version control by tracking data models from creation through usage by registering, validating and monitoring each version. Creates snapshots of model properties and files and retains them for the future. 
  • Visual Statistics: Build diverse scenarios simultaneously and refine them with what-if analyses to uncover insights through experimentation. Unifies all business tools, irrespective of the language they support, into a common visual analytics solution. 
  • Cloud Integrations: Develop low-code technologies by porting SAS open-source models into mobile and business applications through its cloud-native capability. Optimize analytics workloads on clouds like Microsoft Azure and ensure cost-efficient migration of analytics to the cloud through a workload management tool. 
  • ML-Based Insights: Get valuable insights from new data types by combining structured and unstructured data in integrated machine learning programs. Choose the desired ML algorithm from a range of options and easily find the optimal parameter settings. Use Python within Jupyter notebooks for deep learning functions like computer vision, natural language processing, forecasting and speech processing. 
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Product Ranking

#26

among all
Big Data Analytics Tools

#41

among all
Big Data Analytics Tools

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Analyst Rating Summary

91
94
96
96
84
100
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98
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Availability and Scalability
Data Management
Dashboarding and Data Visualization
Machine Learning
Augmented Analytics
Availability and Scalability
Computer Vision and Internet of Things (IoT)
Platform Security
Dashboarding and Data Visualization
Augmented Analytics

Analyst Ratings for Functional Requirements Customize This Data Customize This Data

Dataiku
SAS Viya
+ Add Product + Add Product
Augmented Analytics Computer Vision And Internet Of Things (IoT) Dashboarding And Data Visualization Data Management Data Preparation Geospatial Visualizations And Analysis Machine Learning Mobile Capabilities Platform Capabilities Reporting 96 84 98 100 71 98 0 96 96 100 98 96 86 93 70 96 0 25 50 75 100
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86%
14%
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71%
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29%
86%
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86%
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Analyst Ratings for Technical Requirements Customize This Data Customize This Data

100%
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86%
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14%
82%
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18%
86%
7%
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User Sentiment Summary

Excellent User Sentiment 7 reviews
Great User Sentiment 203 reviews
91%
of users recommend this product

Dataiku has a 'excellent' User Satisfaction Rating of 91% when considering 7 user reviews from 1 recognized software review sites.

85%
of users recommend this product

SAS Viya has a 'great' User Satisfaction Rating of 85% when considering 203 user reviews from 3 recognized software review sites.

n/a
4.2 (157)
4.57 (7)
n/a
n/a
4.3 (33)
n/a
4.7 (13)

Awards

SelectHub research analysts have evaluated Dataiku and concluded it earns best-in-class honors for Augmented Analytics. Dataiku stands above the rest by achieving an ‘Excellent’ rating as a User Favorite.

User Favorite Award
Augmented Analytics Award

SelectHub research analysts have evaluated SAS Viya and concluded it deserves the award for the Best Overall Big Data Analytics Tools available today and earns best-in-class honors for Augmented Analytics and Computer Vision and Internet of Things (IoT).

Analysts' Pick Award
Augmented Analytics Award
Computer Vision and Internet of Things (IoT) Award

Synopsis of User Ratings and Reviews

Comprehensive Feature Set: Users appreciate Dataiku's wide range of features, from data preparation to advanced machine learning, enabling end-to-end data analytics.
Intuitive Interface: Dataiku's user-friendly interface receives praise for its ease of use, making it accessible to both data professionals and business users.
Effective Collaboration: Many users find Dataiku's collaborative environment conducive to teamwork, facilitating cross-functional collaboration on data projects.
Scalability: Dataiku's scalability is highly regarded, making it suitable for small teams and large enterprises, adapting to evolving data requirements.
AutoML Capabilities: Users value Dataiku's AutoML functionality, which simplifies machine learning, making it accessible to users with varying levels of expertise.
Real-Time Insights: Dataiku's ability to provide real-time insights is a significant benefit, enabling timely decision-making based on up-to-date data.
Data Governance: Dataiku's robust data governance features are highly regarded, helping maintain data quality and ensuring compliance with regulations.
Community and Support: Users appreciate the Dataiku community and support resources, which provide valuable assistance and guidance.
Integration Capabilities: Many users highlight Dataiku's seamless integration with other tools and systems, enhancing their data workflows.
Transparency and Explainability: Dataiku's focus on model transparency and explainability is praised, enhancing trust in machine learning models.
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Ease of Use: All users who mention its interface say that it makes autonomous analysis and data modeling accessible to users of all skill levels.
Support: All users who review support say that representatives are responsive and helpful in resolving issues and queries.
Functionality: Around 71% of the users who comment on its feature set say that the software helps discover data insights through powerful visualizations and on-the-fly calculations.
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Steep Learning Curve: Some users find Dataiku's learning curve to be relatively steep, particularly for those new to data science and machine learning.
Resource Intensive: Running complex operations and large-scale data processing in Dataiku can be resource-intensive, potentially requiring substantial computing power.
Costly Licensing: The cost of Dataiku's licensing can be a concern for small organizations or startups with limited budgets.
Limited Free Version: The free community edition of Dataiku has limitations in terms of features and scalability, which may not meet the needs of larger enterprises.
Integration Challenges: Some users encounter challenges when integrating Dataiku with certain legacy systems or non-standard data sources, requiring additional effort and customization.
Dependency on Data Quality: The effectiveness of Dataiku's analysis and modeling heavily relies on the quality of input data, which can be a challenge if data is not well-maintained.
Customization Complexity: Highly customized data workflows may require a deeper understanding of the platform, potentially making customization more complex.
Real-Time Processing: Dataiku may not be the ideal choice for applications requiring real-time data processing, as it primarily focuses on batch processing.
Competitive Market: Dataiku operates in a competitive market with various alternatives, making it essential for users to evaluate if it aligns with their specific needs and budget.
Security Concerns: While Dataiku offers security features, organizations handling highly sensitive data may need additional security measures to meet compliance standards.
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Cost: All users who discuss its pricing say that the cost of acquisition is high.
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User reviews for Dataiku reveal a mixed sentiment, with notable strengths and weaknesses. Users appreciate Dataiku's comprehensive feature set, user-friendly interface, and its effectiveness in facilitating collaboration among diverse teams. Scalability is another advantage, making it suitable for various organizational sizes. AutoML capabilities and real-time insights are well-received for their accessibility and timeliness. However, several users express concerns about a steep learning curve, especially for newcomers to data science. The platform's resource-intensive nature can be challenging, and the cost of licensing may be a barrier for smaller organizations. Some users find limitations in the free community edition and face integration challenges with legacy systems or non-standard data sources. Data quality dependency and customization complexity are other reported cons. Dataiku is often compared to similar products in a competitive market, and users stress the importance of evaluating it against specific needs and budgets. Security-conscious organizations may need additional measures when handling sensitive data. Despite its limitations, Dataiku maintains a strong user base due to its robust feature set and collaborative capabilities, enabling data-driven decision-making in various industries.

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SAS Viya is an AI-powered data management and visual analytics platform with a robust, scalable architecture. All users who reviewed data source connectivity said that it connects to multiple sources and integrates easily with business applications, giving a seamless user experience. With fast in-memory processing of big data sets, it leverages the power of R to enable visual statistics. All users who mentioned predictive analysis said that it enables automated forecasting through what-if scenarios, goal-seeking, text mining and decision trees. Citing ease of use, all users say that the platform is intuitive and enables easy data modeling and self-service visual analytics. All users who mentioned support said that they are responsive and knowledgeable. Around 71% of the users who comment on its functionality say that it is a robust, scalable and flexible platform that enables visualization and analysis of business data, though some users say visual statistics need improvement. On the flip side, all users who review its cost say that the tool is expensive. In summary, SAS Viya is an analytics tool that provides data management, visualization and AI-powered analytics to enterprises for improved decision making, though small organizations and startups might find it cost-prohibitive.

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