[ { "id": "intelligent-wolf", "name": "IntelligentWolf", "logo": "/assets/logo_intelligent_wolf.png", "description": "IntelligentWolf is the active intelligence platform designed for businesses that have outgrown rigid systems.", "website": "https://intelligentwolf.com/", "tags": ["Enterprise", "Web-Based"], "sponsor": "silver", "sponsor_primary": true, "details": { "sections": [ { "title": "About IntelligentWolf", "content": "Unlike read-only dashboards, IntelligentWolf is engineered for bi-directional data flow, meaning it can both read data from and write data back to every system in a company’s tech stack—including CRMs, ERPs, financial systems, and internal databases. At its core, the platform uses integrated Vector Databases to process vast amounts of structured and unstructured data—like documents, meeting recordings, and code—by converting them into AI-generated embeddings. This enables semantic search, contextual understanding, and predictive analytics that uncover hidden patterns and relationships across disconnected systems." }, { "title": "Key Features", "content": [ "Vector searching is the engine that powers semantic understanding: Data (text, code snippets, document sections) is transformed into dense numerical vectors (embeddings) using advanced AI models. These vectors capture the semantic essence and context of the data, placing conceptually similar items close together in a high-dimensional space.", "The IntelligenceWolf framework is designed to be an enterprise integration powerhouse: The system offers native connectivity to any cloud storage system (AWS, Azure, GCP, etc.). This ensures all your data, regardless of its current location, is indexed and ready for intelligent analysis without complex migrations.", "The proprietary Hunt system unifies the retrieval process, bringing disparate capabilities into one cohesive experience: The Hunt system runs complex retrieval pipelines—combining the speed of Approximate Nearest Neighbor (ANN) vector search with traditional keyword matching (Hybrid Search)—and presents all the highly relevant results in a single, streamlined interface." ] }, { "title": "MariaDB Support", "content": "IntelligentWolf uses MariaDB to read from and write back to connected systems, turning AI-driven insights into automated outcomes without manual intervention." } ], "links": [ { "label": "Official Website", "url": "https://intelligentwolf.com/" }, { "label": "Sponsorship Announcement", "url": "https://mariadb.org/welcoming-wolf-software-systems-ltd/" } ] } }, { "id": "datography", "visible": true, "name": "Datography", "logo": "/assets/logo_datography.jpg", "description": "A data environment intelligence platform that helps organizations understand, map, and manage complex data ecosystems.", "website": "https://www.datography.net/", "tags": ["Business", "Enterprise"], "sponsor": "silver", "sponsor_primary": true, "contributor": false, "details": { "sections": [ { "title": "About Datography", "content": "Datography provides deep visibility into how data flows across databases, applications, and infrastructure, enabling teams to track dependencies, analyze impact, and maintain control over increasingly distributed data environments. As modern architectures grow more complex, Datography acts as a unifying layer that reveals how systems are interconnected, helping organizations reduce risk, improve governance, and operate with greater confidence." }, { "title": "Key Features", "content": [ "Automated discovery and mapping of data flows across systems", "Visualization of data lineage, dependencies, and relationships", "Impact analysis for schema changes, migrations, and updates", "Cross-platform visibility across multiple databases and applications", "Support for governance, compliance, and audit requirements", "Improved operational awareness in complex, distributed environments" ] }, { "title": "MariaDB Support", "content": "Datography complements MariaDB deployments by providing visibility into how data stored in MariaDB interacts with other systems across the broader environment. It enables teams to understand dependencies, trace data flows, and assess the impact of changes before they are introduced." } ], "links": [ { "label": "Official Website", "url": "https://www.datography.net/" }, { "label": "Contact", "url": "https://www.datography.net/#contact" }, { "label": "Sponsorship Announcement", "url": "https://mariadb.org/datography-joins-mariadb-foundation-as-silver-sponsor/" } ] } }, { "id": "matomo", "name": "Matomo", "logo": "/assets/logo_matomo.png", "description": "Free, open-source web analytics platform that serves as a privacy-focused alternative to Google Analytics.", "website": "https://matomo.org/", "tags": ["Open Source", "Free", "Business", "Enterprise", "Docker"], "sponsor": false, "details": { "sections": [ { "title": "About Matomo", "content": "Matomo tracks website and app visits, providing detailed reports on visitor behavior, traffic sources, conversions, and more, all without data sampling." }, { "title": "Key Features", "content": [ "Self-hosted or cloud-based deployment: You can install Matomo on your own server (on-premise) or use a cloud service, giving you complete control over your data.", "Privacy-first design: Built-in compliance with GDPR, CCPA, and other privacy regulations through features like IP anonymization, cookie-less tracking, and consent management.", "Comprehensive analytics: Offers real-time reporting, A/B testing, heatmaps, session recordings, funnels, event tracking, and custom reports." ] }, { "title": "MariaDB Support", "content": "MariaDB is part of the recommended configuration options for Matomo." } ], "links": [ { "label": "Official Website", "url": "https://matomo.org/" }, { "label": "GitHub", "url": "https://github.com/matomo-org/" }, { "label": "Download", "url": "https://matomo.org/download/" }, { "label": "Documentation", "url": "https://matomo.org/guides/" } ] } }, { "id": "opensearch", "name": "OpenSearch", "logo": "/assets/logo_opensearch.svg", "description": "Open source search, analytics, and visualization suite, distributed under the Apache 2.0 license.", "website": "https://opensearch.org/", "tags": ["Open Source", "Free", "Business", "Enterprise", "Linux", "Windows", "macOS", "Docker"], "sponsor": false, "details": { "sections": [ { "title": "About OpenSearch", "content": "OpenSearch is a community-driven, open source search and analytics suite distributed under the Apache 2.0 license. It consists of two core components: OpenSearch, the search and analytics engine for data indexing, querying, and retrieval, and OpenSearch Dashboards, a visualization interface for creating dashboards and exploring data. The suite includes advanced security, alerting, SQL support, automated index management, and deep performance analysis." }, { "title": "Key Features", "content": [ "Full-Text and Vector Search: Supports traditional full-text search alongside k-NN vector search, semantic search, and neural queries for AI-powered retrieval.", "OpenSearch Dashboards: Built-in visualization and exploration interface for creating dashboards, analysing data, and building charts and maps.", "Security Analytics: Built-in threat detection, correlation rules, and support for multiple log types including CloudTrail, DNS, and Windows events.", "Multiple Query Languages: Native SQL support, Piped Processing Language (PPL), and Query DSL for flexible data querying." ] }, { "title": "MariaDB Support", "content": "OpenSearch can be used alongside MariaDB to add full-text search and analytics capabilities to MariaDB-backed applications. Data from MariaDB can be indexed into OpenSearch using tools such as Logstash, allowing organisations to combine MariaDB's relational data management with OpenSearch's search and visualization features." } ], "links": [ { "label": "Official Website", "url": "https://opensearch.org/" }, { "label": "GitHub", "url": "https://github.com/opensearch-project/OpenSearch" }, { "label": "Download", "url": "https://opensearch.org/downloads.html" }, { "label": "Documentation", "url": "https://docs.opensearch.org/latest/about/" } ] } }, { "id": "dashtera", "name": "Dashtera", "logo": "/assets/logo_dashtera.png", "description": "A no-code, GPU-accelerated data visualization platform for building real-time dashboards across large and fast-moving datasets.", "website": "https://dashtera.com/", "tags": ["Free", "Business", "Enterprise", "Web-Based", "SaaS"], "sponsor": false, "contributor": false, "details": { "sections": [ { "title": "About Dashtera", "content": [ "Built on GPU-accelerated rendering technology, Dashtera is optimized for high-performance data visualization at scale. Unlike traditional dashboarding tools that rely on data sampling or aggregation, Dashtera can handle millions of data points in real time while maintaining smooth interactivity and responsiveness. This enables teams to monitor systems, analyze high-frequency data, and explore large datasets without performance bottlenecks.", "The platform is particularly effective for real-time analytics, operational monitoring, industrial IoT, financial data analysis, and scientific workloads where data precision and speed are critical." ] }, { "title": "Key Features", "content": [ "Extensive chart library spanning standard business dashboards to advanced financial, engineering, and scientific visualizations, including 3D, polar, and technical analysis charts.", "No-code drag-and-drop editor with data filtering, grouping, aggregation, and drill-down capabilities for fast dashboard setup without development effort.", "Flexible deployment options including cloud-hosted, on-premises, and OEM/white-label configurations." ] }, { "title": "MariaDB Support", "content": "Dashtera supports MariaDB as one of its SQL data connectors. Connection is established via a standard connection string, following the same setup process used for other supported relational databases. MariaDB connectivity is available on the free plan." } ], "links": [ { "label": "Official Website", "url": "https://dashtera.com/" }, { "label": "Data Connectors", "url": "https://dashtera.com/data-connectors/" }, { "label": "Tutorials", "url": "https://dashtera.com/tutorials/" }, { "label": "Videos", "url": "https://www.youtube.com/@Dashtera-official" } ] } }, { "id": "wendelin", "name": "Wendelin", "logo": "/assets/logo_wendelin.png", "description": "Open source big data platform built on MariaDB, NEO distributed storage, and scikit-learn for processing and analyzing datasets beyond hardware memory limits.", "website": "https://www.wendelin.io/", "tags": ["Open Source", "Linux", "Free", "Business", "Enterprise", "Web-Based"], "sponsor": false, "contributor": false, "details": { "sections": [ { "title": "About Wendelin", "content": [ "Wendelin is an open source big data platform developed by Nexedi, combining MariaDB, NEO distributed storage, and standard Python scientific computing tools for large-scale data ingestion, analysis, and machine learning. Its out-of-core architecture allows processing datasets larger than available RAM by streaming data through NumPy arrays backed by distributed storage, removing hardware memory constraints from data analysis workflows.", "Wendelin integrates with Jupyter, SciPy, pandas, scikit-learn, and Plotly, and can be deployed via SlapOS on Rapid.Space, other cloud providers, or on-premise." ] }, { "title": "Key Features", "content": [ "Out-of-core analytics — Processes datasets larger than available RAM using NEO distributed storage, presenting data as standard NumPy arrays so existing Python code runs without modification.", "Python-native stack — Integrates scikit-learn, NumPy, pandas, SciPy, and Plotly, minimizing data format conversions and letting data scientists work with familiar tools.", "Jupyter integration — Provides interactive IPython/Jupyter notebooks for exploratory analysis, visualization, and machine learning model development.", "Distributed architecture — Scales horizontally through NEO distributed storage with virtually no upper limit on data processing capacity.", "SlapOS deployment — Orchestrated and deployed via SlapOS, enabling installation on Rapid.Space, public clouds, or on-premise infrastructure." ] }, { "title": "MariaDB Support", "content": "Wendelin uses MariaDB as one of its core data layers alongside NEO distributed storage. Nexedi, the company behind Wendelin, is a long-standing contributor to the MariaDB ecosystem and uses MariaDB extensively across its ERP5 and SlapOS platforms." } ], "links": [ { "label": "Official Website", "url": "https://www.wendelin.io/" }, { "label": "GitLab", "url": "https://lab.nexedi.com/nexedi/erp5" }, { "label": "Documentation", "url": "https://wendelin.nexedi.com/" } ] } } ]