Datafari - Detailed Review
Search Tools
Datafari - Product Overview
Introduction to Datafari
Datafari is a sophisticated search solution that falls squarely within the Search Tools AI-driven product category. Here’s a breakdown of its primary function, target audience, and key features:Primary Function
Datafari is an enterprise search solution aimed at helping organizations find and utilize their internal data efficiently. It acts as an Insight Engine, providing a comprehensive view of the company’s data to facilitate internal collaboration and knowledge management.Target Audience
Datafari is primarily targeted at large and medium-sized enterprises that struggle with managing and accessing vast amounts of data. It is particularly useful for knowledge workers who spend significant time searching for documents and information within their organizations.Key Features
Crawling and Indexing
Datafari includes a robust crawling phase that retrieves data from various external and internal sources, capturing both the full content and metadata. This ensures that all relevant data is indexed and ready for search.Advanced Search Capabilities
The search phase involves querying and computing the relevance of the results, which are then displayed in a matter of milliseconds. This is enhanced by AI-driven mechanisms such as machine learning and semantic entity extraction, which optimize the search experience.Relevancy and Personalization
Datafari offers a complete set of automatic and manual tools to analyze, understand, and optimize the relevancy of search results. This is done based on user profiles and content, ensuring that users receive highly relevant and personalized search results.Security
Security is a top priority for Datafari. It includes features such as user authentication, authorization, encryption, and respect for document-level access rights. All transmissions are secured via HTTPS, and Single Sign-On (SSO) is supported for a seamless user experience.Scalability and Performance
Datafari is built with Big Data technologies in mind, allowing it to scale easily to meet the needs of growing organizations. Its clustered distributed approach ensures high performance, whether in terms of the number of documents, users, or required functionalities.User-Friendly Interface
The platform is user-friendly, leveraging paradigms common in web search engines, which means almost no training is required for users. It also features smart autocompletion and the ability to save search queries and receive email notifications about updates.Additional Benefits
Datafari allows organizations to focus their budget on optimizing the search experience rather than on expensive license costs. It integrates well with existing knowledge management systems and supports various customization options using CSS and HTML. In summary, Datafari is a powerful tool for enterprises seeking to enhance their internal data management and search capabilities, ensuring that knowledge workers can find the information they need quickly and efficiently. Datafari - User Interface and Experience
User Interface
- The interface is responsive and adaptable to various devices, ensuring a consistent user experience across different platforms. It is built using HTML and CSS, allowing for a high level of customization.
Customization
- Users can customize the interface extensively, including themes, colors, fonts, logos, and specific components. The DatafariUI, a React app, provides the flexibility to update or add new components without requiring extensive coding.
Ease of Use
- Datafari features a simple and intuitive interface that incorporates advanced AI and Machine Learning techniques to enhance the search experience. The Smart Autocomplete function suggests queries to speed up the search process, making it easier for users to find relevant data quickly.
Search Experience
- The search page is central to the interface, with facets such as query and field facets defined in the side panel. The AutocompleteContainer uses suggesters configured via a `ui-config.json` file to provide suggestions as the user types, enhancing the search efficiency.
Alerts and Saved Searches
- Users can save their search queries and receive email notifications about updates or additions to the results, which helps in keeping them informed without the need for constant manual searches.
Security and Access Control
- Datafari ensures security through authentication with AD or LDAP, authorization management to enforce access rights, and encryption of data exchanges via HTTPS. This ensures that users can only access data they are authorized to see.
Administration
- The administration interface is comprehensive, allowing administrators to manage connectors, system status, document retrieval, and user access. It also provides tools for search experts to tune the search algorithm, manage fields weights, and handle other advanced search configurations.
Overall, Datafari’s user interface is designed to be user-friendly, highly customizable, and efficient, making it a powerful tool for enterprise search needs.
Datafari - Key Features and Functionality
Datafari Overview
Datafari is a comprehensive enterprise search solution that integrates advanced technologies, including AI and machine learning, to provide efficient and relevant search results. Here are the key features and functionalities of Datafari, particularly in the context of its AI-driven capabilities:Data Integration and Crawling
Datafari can integrate multiple data sources such as SharePoint, emails, Dropbox, file shares, Alfresco, and more, using Apache ManifoldCF. This allows for the crawling of diverse data formats from various sources, including CMS, ECM systems, and social networks.Indexing and Scalability
Datafari uses a big data architecture, similar to Hadoop, to index hundreds of millions of documents across several machines. It employs SolrCloud and Zookeeper for automatic management of system failures, ensuring reliability and scalability.Search Capabilities
Vector Search and Natural Language Queries
Datafari is transitioning to vector search and large language models (LLMs) through the Neural Datafari project. This enables users to ask questions in natural language and receive relevant documents or generated answers based on their queries.Retrieval Augmented Generation (RAG)
This two-step approach involves running a vector search to identify relevant authorized documents, extracting the most relevant chunks, and then using a generative AI model to generate answers from these chunks. This ensures that users only receive answers generated from documents they are authorized to access.AI and Machine Learning
Document Embedding
The Neural Datafari project enhances the embedding phase for incoming documents, making it simpler to enrich documents at indexing time using LLM-based vector embeddings.Query Embedding
Automatic embedding of user queries for KNN (K-Nearest Neighbors) search is also implemented, external to Solr but simplified for use.Administration and Security
Authentication and Authorization
Datafari connects to AD or LDAP for user authentication and manages authorizations and ACLs to ensure users can only see what they are allowed to see. It also supports HTTPS for secure data exchanges.Administration Tools
The platform offers administration tools for managing connectors, system status, document retrieval, alerts, and more. It also allows for the customization of fields weights, promolinks, statistics, synonyms, and stopwords.User Interface and Experience
Responsive Design
The user interface is responsive and adaptable to different devices, built using HTML and CSS, and is fully customizable.Smart Autocomplete
The autocomplete feature suggests queries to speed up the search process for users. Users can also save queries and receive email notifications about updates or additions to the results.Relevancy and Customization
Algorithmic Tuning
The search algorithm can be tuned to give relative importance to different components of documents (content, metadata) and boost specific documents for particular queries.Semantic Features
Datafari includes entity extraction and recognition, spellchecker, content suggestion, and results clustering to improve the relevance of search results.Alerts and Notifications
Users can save complex searches and receive email notifications when new or modified documents match their saved queries, ensuring they stay updated without constant manual searches.Conclusion
In summary, Datafari combines traditional keyword-based search with advanced AI-driven technologies like vector search and generative AI, making it a powerful tool for enterprise search needs. Datafari - Performance and Accuracy
Performance
Datafari is built with a clustered distributed approach, leveraging top-level Big Data technologies. This design allows it to adapt quickly to various needs, whether in terms of performance, the number of documents, or the number of users.
Scalability
Datafari can handle a large volume of documents and users efficiently, making it suitable for large-scale enterprise search needs.
Speed
The system is capable of querying and computing relevance, and displaying results in a few milliseconds, which is crucial for user satisfaction.
Modularity
Datafari integrates various Big Data technologies, making it modular, reliable, and well-documented. This modularity helps in maintaining and optimizing the system.
Accuracy
Datafari employs several features to ensure the accuracy and relevance of search results:
Relevancy Tools
It uses a complete set of automatic and manual tools to analyze, understand, and optimize the relevancy of search results based on user profiles and content.
Machine Learning and Semantic Entity Extraction
Datafari leverages machine learning and semantic entity extraction to optimize the personalized search experience. This helps in providing more accurate and relevant results to users.
User Signals
The system uses signals generated by users to optimize the search experience, which includes smart autocompletion and other personalized features.
Limitations and Areas for Improvement
While the provided information does not detail specific limitations of Datafari, some potential areas for improvement can be inferred:
User Feedback and Testing
Continuous testing and gathering qualitative feedback from users are essential to ensure that the data and search results meet business goals and deliver value. This is a general best practice in AI-driven products but is not explicitly mentioned in the context of Datafari.
Data Quality
Ensuring high-quality data is crucial for any AI-driven system. Datafari’s performance and accuracy could be impacted if the data it processes is inaccurate, outdated, or incomplete. Maintaining data quality based on criteria such as accuracy, coverage, granularity, and timeliness is vital.
Security and Privacy
While Datafari mentions security features like user authentication, authorization, and encryption, ensuring compliance with current and upcoming legislation regarding data privacy is essential. This includes transparent data handling practices, which are increasingly important in AI systems.
Conclusion
In summary, Datafari appears to be a strong performer in the Search Tools AI-driven product category, with its ability to scale, provide fast and relevant results, and leverage advanced technologies like machine learning. However, like any system, it would benefit from ongoing user feedback, rigorous data quality checks, and stringent security and privacy measures.
Datafari - Pricing and Plans
Datafari Pricing Overview
Datafari, an AI-driven enterprise search solution, offers a clear and structured pricing model based on the number of indexed documents and the level of features required. Here’s a breakdown of their pricing plans and the features associated with each:
Free Community Version
Datafari provides a free, open-source community version, which is ideal for developers and those who want to test the product. This version is free forever and includes:
- Unlimited documents
- DIY (Do-It-Yourself) setup for big data, security, exploitation, relevancy optimization, and customer support.
Basic Plan
The Basic plan is priced at $8,900 per year (or €8,900) and is suitable for teams with a small amount of documents. Key features include:
- Support for up to 500,000 documents
- Advanced clustering for big data
- Security features such as Active Directory (AD), LDAP, Single Sign-On (SSO), HTTPS, and Access Control Lists (ACLs)
- Exploitation features including backups, redundancy, monitoring, alerts, and business intelligence (BI)
- Relevancy optimization using machine learning and semantic autotuning
- Customer support.
Expert Plan
The Expert plan is priced at $19,900 per year (or €16,900) and is useful for Small to Medium-sized Enterprises (SMEs) or large teams. This plan includes:
- Support for up to 1,000,000 documents
- All the features from the Basic plan, including advanced clustering, security, exploitation, relevancy optimization, and customer support.
Large Group Plan
This plan is priced at $32,000 per year (or €26,900) and is suitable for large groups and knowledge workers. It supports:
- Up to 5,000,000 documents
- All the features from the Expert plan.
Corporate Search Plan
The Corporate Search plan is priced at $49,000 per year (or €42,900) and is usually for large groups and corporate search needs. It includes:
- Support for up to 30,000,000 documents
- All the features from the Large Group plan.
Custom Projects
For projects requiring more than 30,000,000 documents, Datafari offers a custom pricing plan. Users need to contact Datafari directly to scope their project and receive a personalized quote.
Each plan scales with the size of your business and the number of documents you need to index, ensuring you only pay for what you need. The open-source community version provides a free entry point for developers and small-scale users.
Datafari - Integration and Compatibility
Datafari Overview
Datafari, an open-source enterprise search solution, is designed to integrate seamlessly with a variety of tools and platforms, ensuring broad compatibility and flexibility.
Integration with Other Tools
Datafari leverages several renowned Apache projects, including Solr, ManifoldCF, and Cassandra, which enhances its integration capabilities. Here are some key integrations:
- Apache Solr and SolrCloud: Datafari integrates with Solr 9.5, allowing it to scale easily and manage large volumes of data efficiently. This integration enables the indexing of content hosted on Apache Solr 9.x.
- ManifoldCF: The use of ManifoldCF 2.26 provides a framework for connecting to various data sources such as databases, social networks, and emails. This allows Datafari to crawl and index data from multiple external sources.
- Cassandra and PostgreSQL: Datafari uses Cassandra 4.1.3 and PostgreSQL 15.4 for data storage, ensuring reliable and scalable data management.
- Zookeeper: The integration with Zookeeper 3.9.1 helps in managing system failures automatically in distributed mode.
Connectors and Plugins
Datafari offers a plugin mechanism that allows users to develop new connectors. This flexibility enables the integration with a wide range of data sources. For example, there are new connectors for regex transformations and CSV files, which can index each line of a CSV as a separate entry in the index.
Authentication and Authorization
Datafari supports integration with Active Directory (AD) and LDAP for user authentication. It also connects to systems managing authorizations and Access Control Lists (ACLs) to ensure that users can only access data they are authorized to see.
Platform Compatibility
Datafari can be deployed on various platforms, making it highly versatile:
- Docker Containers: Datafari can be easily installed using Docker containers, which simplifies the deployment process and ensures consistent environments.
- Virtual Machines: It is also available as a preconfigured Virtual Machine (OVA), providing another convenient installation option.
- Hardware Requirements: The solution can run on different hardware configurations, with reduced hardware requirements compared to previous versions.
Device Compatibility
The user interface of Datafari is designed to be responsive, adapting to various devices. It is built using HTML and CSS, making it fully customizable and accessible across different devices.
Conclusion
In summary, Datafari’s integration with various Apache projects, its plugin mechanism, and its support for multiple deployment options make it a highly compatible and flexible enterprise search solution. Its ability to adapt to different platforms and devices ensures that it can meet the diverse needs of organizations.
Datafari - Customer Support and Resources
Support Services
Datafari provides a range of support services based on the maturity level of your project. Here are some key services:Audit
This service includes evaluating your current setup, providing architecture recommendations, and optimizing security and system performance. Audits start at 3000 € for 3 days.Integration
Datafari offers installation and configuration of the search solution, integration with third-party frameworks (such as CMS, wikis, and portals), and coaching for exploitation teams. You need to contact them for specific costs.Development
For specific development needs, Datafari provides expertise for R&D activities, technical roadmaps, and architecture guidance. Again, costs are available upon contact.Support
Datafari offers annual support packages starting from 5000 € per year. This includes solving critical bugs, incident management, web-based management systems, and 24×7 access. Upgrades and hotfixes are also part of this package.Documentation and Resources
Datafari provides comprehensive documentation for users, administrators, and developers. This includes:Installation Guides
Detailed guides on how to install Datafari using Docker, Virtual Machines (OVA), and other methods.User and Admin UI Guides
Instructions on how to use the search UI and admin UI, including how to optimize relevancy and manage security settings.Hardware Requirements
Information on the necessary hardware resources to avoid crashes and ensure smooth operation.Community and Expertise
Datafari benefits from a community-driven approach, leveraging renowned Apache projects such as Cassandra, Solr, and ManifoldCF. This community support ensures long-term viability and continuous improvement of the product. Additionally, France Labs, the company behind Datafari, offers consulting services to accompany users at all phases of their search project.Future Developments and AI Integration
Datafari is moving towards integrating the latest AI search technologies, including vector search and large language models (LLMs), thanks to funding from the NGI Search EU consortium. This will enable users to ask questions in natural language and receive relevant documents or generated answers, enhancing the search experience significantly. By leveraging these support options and resources, users can ensure they get the most out of Datafari’s enterprise search capabilities. Datafari - Pros and Cons
Advantages of Datafari
Big Data Capability
Datafari is designed with Big Data in mind, leveraging top-tier technologies like Solr, Cassandra, and PostgreSQL. This allows it to handle large volumes of data efficiently, making it ideal for enterprises dealing with massive amounts of information.
Ease of Use
The platform uses widely recognized search paradigms, similar to those used by web search engines, which means users require almost no training to use it effectively. This ergonomic design makes it accessible to everyone within the organization.
Security
Datafari prioritizes security with features such as user authentication, authorization, encryption, and respect for document-level access rights. It integrates with Active Directory and LDAP, ensuring that only authorized users can view specific information. It also supports Single Sign-On (SSO) with Kerberos and SAMLv2.
Advanced Search and Relevancy
Datafari employs machine learning, semantic entity extraction, and smart autocompletion to optimize search results. It includes tools like the Automatic Relevancy Algorithm optimizer and the Golden Query retriever to enhance the relevance of search results based on user profiles and content.
Federated Search
The platform can search across various data sources, regardless of their location, helping to organize and find files and documents efficiently. This federated search capability makes it a valuable tool for content management and knowledge mining within organizations.
Cost-Effective
Datafari is released under the Apache License 2.0, which means it is free to use and has no license fees. This allows companies to allocate their budget to system administration and management rather than expensive licensing costs.
Advanced Monitoring and Backup
Datafari offers advanced monitoring features, including configuration of Glances, logs downloadable per component, and cron jobs to watch and restart processes automatically. It also includes backup scripts for MCF configuration and Solr indexes, ensuring data safety and system reliability.
Disadvantages of Datafari
Complexity in Setup and Administration
While Datafari is user-friendly for end-users, its administration and setup can be complex, especially given its Big Data architecture and the need to manage multiple components like Solr, Cassandra, and PostgreSQL. This may require significant technical expertise.
Resource Intensive
The platform’s ability to handle large volumes of data and its use of advanced technologies like machine learning and semantic entity extraction can make it resource-intensive. This might require substantial hardware and infrastructure to run efficiently, particularly in large-scale deployments.
Limited Support for Community Edition
The Community Edition of Datafari lacks some of the advanced features and support available in the Enterprise Edition, such as machine learning for smart re-ranking, advanced indexing time concept extractors, and dedicated support from the makers of Datafari.
Potential Learning Curve for Administrators
Although the end-user interface is intuitive, administrators may need to invest time in learning how to configure and manage the various components of Datafari, such as the graphical AD configuration, multiple AD OU management, and advanced monitoring dashboards.
In summary, Datafari offers significant advantages in terms of its Big Data capabilities, ease of use, and advanced search features, but it may also present challenges related to setup complexity, resource requirements, and the need for technical expertise, especially for administrators.
Datafari - Comparison with Competitors
When Comparing Datafari to Other Products
When comparing Datafari to other products in the search tools and AI-driven product category, several key features and alternatives stand out.
Unique Features of Datafari
- Big Data Ready: Datafari is built with big data in mind, utilizing Apache SolrCloud to handle large volumes of data efficiently. This allows it to index millions of documents quickly and provide results in milliseconds.
- Integration of Multiple Data Sources: Datafari integrates various data sources such as SharePoint, emails, Dropbox, file shares, and Alfresco using Apache ManifoldCF. This comprehensive integration makes it a powerful tool for enterprise search.
- Open Source and Cost-Effective: Released under the Apache v2 license, Datafari is free to use, which can be a significant advantage for companies looking to optimize their search experience without incurring high license costs.
- Security and Access Control: Datafari prioritizes security with features like user authentication, authorization, encryption, and document-level access rights. All transmissions are secured via HTTPS, and it supports Single Sign-On (SSO) for a seamless user experience.
- Personalized Search Experience: Datafari uses machine learning, semantic entity extraction, and smart autocompletion to optimize the search experience based on user profiles and content.
Alternatives to Datafari
Guru
- Guru is a knowledge base and intranet solution that centralizes essential information for teams. It is powered by AI and gets smarter with use. While it is not primarily a search engine, it helps in organizing and sharing information efficiently.
- Key Difference: Guru focuses more on knowledge management and intranet functions rather than a broad enterprise search capability.
GoLinks
- GoLinks transforms long URLs into short, memorable, and searchable links, making it easier for employees to find and share knowledge. It connects teams intuitively to the apps and information they use daily.
- Key Difference: GoLinks is more about URL management and shortening rather than a comprehensive search solution.
Bloomreach
- Bloomreach is an enterprise search solution that also offers content management and personalization features. It is known for its ability to integrate with various systems and provide relevant search results.
- Key Difference: Bloomreach is more commercial and may have different pricing models compared to the open-source nature of Datafari.
Yext and Doofinder
- Yext and Doofinder are other alternatives that offer enterprise search capabilities. Yext is known for its AI-powered search and knowledge graph technology, while Doofinder focuses on e-commerce search solutions.
- Key Difference: These solutions may have more specialized use cases (e.g., Yext for general knowledge graphs, Doofinder for e-commerce) compared to the broad enterprise search capabilities of Datafari.
Comparison with Other Search Tools
Datafari stands out due to its open-source nature, big data readiness, and comprehensive integration of multiple data sources. Here are a few points to consider:
- Cost and Licensing: Unlike many commercial search tools, Datafari is free to use, making it an attractive option for companies looking to manage costs.
- Customization and Flexibility: Datafari’s modular design and use of REST APIs allow for significant customization and integration with existing systems.
- Ease of Use: Datafari is designed to be user-friendly, requiring minimal training due to its use of widespread search paradigms.
In summary, while alternatives like Guru, GoLinks, Bloomreach, Yext, and Doofinder offer unique features, Datafari’s combination of big data readiness, comprehensive data source integration, and open-source licensing makes it a strong contender in the enterprise search category.
Datafari - Frequently Asked Questions
Frequently Asked Questions about Datafari
What is Datafari and what does it do?
Datafari is an enterprise search solution that acts as an Insight Engine, helping organizations find and manage their internal data efficiently. It leverages big data technologies to ease internal collaboration and is modular, reliable, and well-documented. Datafari integrates various tools to analyze, optimize, and secure search results, making it a comprehensive solution for knowledge management.What are the key features of Datafari?
Datafari includes several key features:- Crawling: Retrieves data from external sources, including full content and metadata.
- Searching: Queries and computes the relevance of search results, displaying them quickly.
- Relevancy: Uses automatic and manual tools to analyze and optimize search result relevance based on user profiles and content.
- Security: Ensures user authentication, authorization, encryption, and document-level access rights.
- Big Data and Machine Learning: Utilizes advanced clustering, machine learning, and semantic entity extraction to optimize the search experience.
How does Datafari optimize search results?
Datafari optimizes search results using several mechanisms:- Machine Learning: Uses machine learning to personalize the search experience based on user behavior.
- Semantic Entity Extraction: Extracts meaningful entities from the content to improve search relevance.
- Smart Autocompletion: Provides suggestions as users type their queries.
- User Profiles: Analyzes user profiles and content to deliver more relevant results.
What are the pricing plans for Datafari?
Datafari offers several pricing plans based on the number of indexed documents:- Initial Plan: $9,900 (or €8,900) per year, up to 500,000 documents.
- Expert Plan: $19,900 (or €16,900) per year, up to 1,000,000 documents.
- Large Group Plan: $32,000 (or €26,900) per year, up to 5,000,000 documents.
- Corporate Search Plan: $49,000 (or €42,900) per year, up to 30,000,000 documents.
- Custom Projects: For more than 30,000,000 documents, contact Datafari for a custom quote.
Does Datafari offer any free options?
Yes, Datafari offers a free, open-source community version aimed at developers. This version is DIY and includes features like big data handling, security, exploitation, and relevancy optimization, but it does not come with customer support.How secure is Datafari?
Datafari prioritizes security with features such as:- User Authentication: Supports AD, LDAP, and SSO.
- Encryption: Uses HTTPS for secure transmissions.
- Access Control: Implements ACLs (Access Control Lists) to ensure users can only see what they are authorized to see.
- Backups and Redundancy: Includes backups, redundancy, monitoring, and alerts to ensure data integrity and availability.
Can I try Datafari before purchasing?
Yes, Datafari offers an online demo that uses a default configuration with a document set from the Energy industry. This demo gives you a basic idea of what Datafari can achieve, and feedback is welcome.What industries use Datafari?
Datafari is used by various industries, including banking, government administration, and venture capital. Its versatility makes it suitable for any organization needing robust enterprise search capabilities.How does Datafari integrate with existing systems?
Datafari is designed to be complementary to your existing knowledge management systems. It integrates well with other systems by leveraging common paradigms used by web search engines, making it easy to implement and use without extensive training.What kind of support does Datafari offer?
Datafari provides customer support for its paid plans, which includes assistance with backups, redundancy, monitoring, alerts, and business intelligence (BI). The free, open-source version is DIY and does not include customer support. Datafari - Conclusion and Recommendation
Final Assessment of Datafari
Datafari is a comprehensive and advanced search solution that leverages big data technologies and AI to enhance internal collaboration and knowledge management within organizations. Here’s a detailed assessment of its features and who would benefit most from using it.Key Features
Crawling and Indexing
Datafari efficiently retrieves data from various external sources, including full content and metadata, and indexes it quickly using its Enhanced Data Extraction Server.
Search and Relevance
The platform optimizes search results using machine learning, semantic entity extraction, and smart autocompletion. It also features tools like the Automatic Relevancy Algorithm optimizer and Golden Query retriever to ensure highly relevant search results.
Security
Datafari prioritizes security with features such as user authentication, authorization, encryption, and respect for document-level access rights. It integrates with Single Sign-On (SSO) protocols like Kerberos and SAMLv2, ensuring secure and seamless access for employees.
Big Data and Scalability
The solution is built with big data in mind, using technologies like SolrCloud and Docker containers to support multiserver environments. This allows for scalable performance and efficient management of large volumes of data.
Who Would Benefit Most
Datafari is particularly beneficial for large and medium-sized enterprises that struggle with managing and finding data across various internal sources. Here are some key groups that would benefit:Knowledge Workers
Employees who spend significant time searching for documents and information will find Datafari’s efficient search capabilities and personalized search experience highly valuable.
IT and Administrative Teams
These teams will appreciate the advanced security features, easy integration with Active Directory, and the ability to manage multiple AD organization units. The automated backup scripts and advanced monitoring dashboards also simplify maintenance and troubleshooting.
Organizations with Large Data Volumes
Companies dealing with vast amounts of data will benefit from Datafari’s scalable architecture and big data technologies, which ensure high performance and efficient data management.