Dataiku DSS

Basic Information

Dataiku DSS (Data Science Studio) is a collaborative platform designed for data professionals to build, deploy, and manage AI and analytics solutions. It offers a unified environment for data preparation, visualization, machine learning, and model deployment.

• Model: Dataiku DSS (Data Science Studio)
• Version: Documentation for version 14 is available, with previous versions like 13.3.2 also referenced. Specific release dates vary by version.
• Release Date: Specific release dates are version-dependent and typically announced by Dataiku.
• Minimum Requirements: Refer to the Technical Requirements section for detailed specifications.
• Supported Operating Systems: Primarily Linux x86-64 server distributions, including Red Hat Enterprise Linux (8.10, 9.x), AlmaLinux (8.10, 9.x), Rocky Linux (8.10, 9.x), Oracle Linux (8.10, 9.x), Ubuntu Server (20.04 LTS, 22.04 LTS), Debian (11, 12), Amazon Linux 2023, and SUSE Linux Enterprise Server (15 SP5, SP6). Experimental support is available for Windows for testing purposes.
• Latest Stable Version: Version 14 is referenced in current documentation.
• End of Support Date: End of support dates are typically governed by Dataiku's commercial lifecycle policies and vary by version.
• End of Life Date: End of life dates are typically governed by Dataiku's commercial lifecycle policies and vary by version.
• Auto-update Expiration Date: Not explicitly specified; updates are managed through Dataiku's release cycles and deployment methods.
• License Type: Commercial license, with various editions available for different organizational needs.
• Deployment Model: Dataiku DSS supports flexible deployment models including on-premise installations on Linux servers, cloud deployments via Dataiku Cloud Stacks (on AWS, GCP, Azure), and a fully managed Software-as-a-Service (SaaS) offering called Dataiku Cloud. It can also be run in virtual machine environments.

Technical Requirements

Dataiku DSS requires robust server infrastructure for optimal performance, especially when handling large datasets and multiple users.

• RAM: A minimum of 32 GB of RAM is required for the DSS server. More RAM is necessary for loading large datasets into memory (e.g., in Jupyter notebooks) or accommodating a higher number of concurrent users. For virtual machine deployments, the host machine should have at least 8 GB of RAM, with 4 GB allocated to the DSS virtual machine (can be lowered to 2 GB if host RAM is limited).
• Processor: DSS must be installed on a Linux x86-64 server. While there are no specific CPU requirements, more cores are needed to maintain performance for larger DSS instances or increased workloads. A 64-bit CPU is mandatory for virtual machine installations.
• Storage: Running DSS on SSD drives is highly recommended to prevent severe performance impact, particularly for larger instances and numerous users. The filesystem must be POSIX compliant, case-sensitive, and support POSIX file locks, POSIX ACLs, and symbolic links. XFS or ext4 are strongly recommended. NFS filesystems (v3 or v4) are not supported. The data directory requires at least 100 GB of space.
• Display: Not directly applicable to the server, but user interaction occurs via web browsers.
• Ports: DSS utilizes a base TCP port (e.g., 11000) and several subsequent ports (typically up to base+10).
• Operating System: Refer to the "Supported Operating Systems" in the Basic Information section.
• Browser Support: Google Chrome (latest version), Mozilla Firefox (latest ESR version), and Microsoft Edge (latest version) are supported for accessing the DSS web interface.
• Other Software: DSS supports Java 17 and Python versions 3.9, 3.10, and 3.11 for its built-in environment.

Analysis of Technical Requirements

Dataiku DSS is a resource-intensive platform designed for enterprise-scale data science and AI workloads. The emphasis on substantial RAM and high-speed SSD storage underscores its need for fast data processing and access. The platform's primary support for Linux x86-64 servers highlights its focus on robust, scalable, and stable environments typical of enterprise IT infrastructures. While it offers experimental Windows support, the core design targets Linux, ensuring optimal performance and compatibility within a server-grade ecosystem. Users should plan for significant hardware investment to fully leverage DSS capabilities, especially for large-scale deployments or complex analytical tasks.

Support & Compatibility

Dataiku DSS offers extensive compatibility with various data ecosystems and robust support options.

• Latest Version: Version 14 is the most current version referenced in documentation.
• OS Support: Comprehensive support for various 64-bit Linux distributions (RHEL, AlmaLinux, Rocky Linux, Oracle Linux, Ubuntu Server LTS, Debian, Amazon Linux, SLES). Experimental support for Windows.
• End of Support Date: Specific end-of-support dates are typically provided directly by Dataiku as part of their product lifecycle and commercial agreements.
• Localization: While not explicitly detailed in available information, enterprise software like Dataiku DSS typically offers multi-language support for its user interface.
• Available Drivers/Connectors: Dataiku DSS natively supports over 50 data sources and file formats. This includes a wide range of SQL databases, cloud storage platforms (Amazon S3, Azure Blob Storage, Google Cloud Storage), NoSQL databases (MongoDB, Elasticsearch, Cassandra), HDFS, FTP, SCP/SFTP (SSH), HTTP, SharePoint Online, and various file-based formats (e.g., CSV, JSON, Parquet, ORC). Custom connectors can be installed via the Dataiku plugin store or developed using generic APIs and custom code.

Analysis of Overall Support & Compatibility Status

Dataiku DSS boasts strong overall support and compatibility, making it highly adaptable to diverse enterprise data environments. Its native integration with a vast array of data sources, including relational, NoSQL, and cloud-based storage, ensures that organizations can connect to their existing data infrastructure without significant hurdles. The platform's primary focus on Linux for server deployments aligns with enterprise standards for scalability and reliability. While specific end-of-support dates are not publicly listed, they are typically managed through Dataiku's commercial support agreements. The extensibility through plugins and custom code further enhances its compatibility, allowing organizations to integrate with niche or proprietary systems.

Security Status

Dataiku DSS incorporates a comprehensive suite of security features and adheres to industry best practices to protect data and user access.

• Security Features: Granular access control is implemented at various levels, including projects, project folders, connections, and user profiles. It supports audit trails for tracking user actions, robust password security, and secure cookie usage. Advanced options include session management (e.g., forcing a single session per user, session expiration), hiding sensitive information like error stacks and version details, and restricting exports.
• Known Vulnerabilities: Dataiku actively monitors and researches potential vulnerabilities. The company has confirmed that Dataiku DSS is not vulnerable to several widely publicized exploits, including specific OpenSSL (versions 3.x not shipped), Text4Shell (CVE-2022-42889), SpringShell (CVE-2022-22965), and Log4J (CVE-2021-44228, CVE-2021-45046, CVE-2021-45105) vulnerabilities.
• Blacklist Status: No information indicates a blacklist status, which is generally not applicable to enterprise software platforms.
• Certifications: Dataiku is ISO 27001:2022 certified, demonstrating adherence to international standards for information security management systems.
• Encryption Support: While not explicitly detailed for all aspects, ISO 27001 certification implies robust encryption practices for data at rest and in transit. Secure cookies are used for user connections.
• Authentication Methods: Dataiku DSS supports multiple authentication methods:
  • Local authentication (username/password stored in DSS).
  • Single Sign-On (SSO) using SAML v2, OpenID Connect, and SPNEGO/Kerberos protocols, integrating with identity providers like Azure AD, Okta, PingFederate, and Google.
  • LDAP for integration with directory services such as Microsoft Active Directory.
  • PAM (Pluggable Authentication Modules).
  • Azure AD as a user supplier for provisioning and synchronization.
  • Custom authenticators for specific needs.
  Multi-factor authentication (MFA) is supported by delegating to SSO identity providers.
• General Recommendations: Dataiku recommends leveraging SSO for stronger authentication and enabling MFA through the chosen identity provider. Implementing secure cookies and restricting visibility of groups and users are also advised.

Analysis on the Overall Security Rating

Dataiku DSS exhibits a high overall security rating. Its comprehensive security framework covers authentication, authorization, and data protection, aligning with enterprise security requirements. The platform's ISO 27001:2022 certification provides assurance of a well-managed information security system. Proactive monitoring and confirmation against major vulnerabilities demonstrate a commitment to maintaining a secure environment. The wide range of supported authentication methods, including robust SSO and LDAP integrations, allows organizations to enforce their existing security policies. The ability to implement MFA via identity providers further strengthens user access security.

Performance & Benchmarks

Dataiku DSS is engineered for high performance and scalability in data science and machine learning workflows.

• Benchmark Scores: Specific public benchmark scores are not readily available in the provided information. Performance is generally discussed in terms of scalability and efficiency in real-world scenarios.
• Real-world Performance Metrics: The platform efficiently handles large datasets, enabling quick data exploration, preparation, and the building and deployment of machine learning models. Performance is significantly enhanced by the use of SSD drives for storage. It is designed to scale with increased users and larger data volumes, with more CPU cores contributing to better performance in such scenarios.
• Power Consumption: As a software platform, direct power consumption metrics are not applicable. Power consumption depends on the underlying hardware infrastructure where DSS is deployed.
• Carbon Footprint: As a software platform, direct carbon footprint metrics are not applicable. The carbon footprint is determined by the energy efficiency of the data centers or on-premise hardware used for deployment.
• Comparison with Similar Assets: Users praise Dataiku DSS for its versatility in handling various data sources (Python, R, SQL) and its ability to transform unorganized data into valuable insights through intuitive dashboards. It is often seen as a comprehensive solution for data science applications, with some users noting its intuitiveness and broad applicability compared to alternatives.

Analysis of the Overall Performance Status

Dataiku DSS is a high-performance platform optimized for demanding data science and AI tasks. Its architecture is built to manage and process large volumes of data efficiently, supporting complex analytical workflows and machine learning model development. The platform's performance is directly tied to the underlying hardware, with a strong recommendation for SSD storage to avoid bottlenecks. While specific benchmark numbers are not provided, user feedback and design principles indicate strong real-world performance and scalability for enterprise use cases. It excels in environments requiring rapid iteration on AI/ML models and handling diverse data sources.

User Reviews & Feedback

User reviews and feedback for Dataiku DSS generally highlight its comprehensive capabilities and user-friendly design, alongside some areas for improvement.

• Strengths:
  • User-Friendly Interface: Reviewers frequently praise its intuitive drag-and-drop functionality, visual programming, and visual modeling capabilities, making it accessible for both technical and non-technical users.
  • Extensive Features & Integration: Users appreciate the broad range of features, including data exploration, preparation, and machine learning, as well as seamless integration with various data sources and technologies (Python, R, SQL).
  • Scalability & Efficiency: The platform is commended for its ability to handle large datasets efficiently and its scalability for enterprise-level deployments.
  • Collaboration & Governance: Strong collaboration tools facilitate team-based projects, and robust security features provide necessary governance.
  • Pre-built Components: An extensive library of pre-built components and templates aids in quick data exploration and analysis.
  • Manageable Data Pipelines: Inbuilt "recipes" simplify data pipeline creation and management.
  • Customer Support: Some users report positive experiences with Dataiku's customer service.
• Weaknesses:
  • Cost: Can be expensive, especially for smaller businesses or those with limited budgets.
  • Learning Curve: A steep learning curve is noted for advanced features.
  • Performance Issues: Occasional performance issues are reported, often linked to insufficient hardware or specific configurations.
  • Limited Advanced Statistical Analysis: Some users feel it has limited support for highly advanced statistical analysis and modeling techniques.
  • Data Accuracy/Reliability: A few users have reported issues with data accuracy and reliability.
  • Visibility & Support for Niche Problems: Difficulty in getting help for specific problems due to less widespread use compared to some tools, and low visibility inside flows when editing or connecting to new data sources.
• Recommended Use Cases:
  • End-to-end data pipelines, from data wrangling to analysis and modeling.
  • Building and deploying AI and ML models, including time series forecasting, NLP, and business optimization.
  • Data preparation and ETL processes before AI/ML model building.
  • Collaborative data science projects across various departments (e.g., finance, sales).
  • Creating interactive dashboards and visualizations.

Summary

Dataiku DSS is a powerful and comprehensive enterprise-grade platform for data science and artificial intelligence. Its primary strengths lie in its user-friendly visual interface, extensive feature set covering the entire data lifecycle from ingestion to deployment, and broad compatibility with a multitude of data sources and cloud environments. The platform fosters collaboration across diverse skill sets, enabling both technical and non-technical users to contribute to AI initiatives. Furthermore, Dataiku DSS demonstrates a strong commitment to security, evidenced by its ISO 27001:2022 certification and robust authentication and access control mechanisms. It is designed for high performance and scalability, particularly when supported by appropriate hardware, such as SSD storage and ample RAM.

However, the platform presents some challenges. Its comprehensive nature can lead to a steep learning curve for advanced functionalities, and the cost may be a barrier for smaller organizations. While generally performant, occasional issues can arise, often linked to underlying infrastructure limitations. Some users also note a desire for more advanced statistical analysis capabilities and improved visibility within complex data flows.

Overall, Dataiku DSS is an excellent choice for enterprises seeking a unified, collaborative, and secure platform to accelerate their data science and AI initiatives. It is particularly well-suited for organizations with diverse data landscapes and a need for robust governance and scalable operations. Prospective users should be prepared for a significant investment in both the software and the necessary hardware infrastructure to maximize its potential.

The information provided is based on publicly available data and may vary depending on specific device configurations. For up-to-date information, please consult official manufacturer resources.