SolrCommerce Integrations

DbVisualizer is one of the world's most popular database editors. With almost 7 million downloads and Pro users in 150 countries worldwide, it won't disappoint you. Free and Pro versions are available. Developers, analysts, and DBAs use it to elevate their SQL experience with modern tools to visualize and manage their databases, schemas, objects, and table data, auto-generate, write, and optimize queries, and so much more. It connects to all popular databases, such as MySQL, PostgreSQL, SQL Server, Oracle, Cassandra, Snowflake, SQLite, BigQuery, and 30+ others, and runs on all popular OSes (Windows, macOS, and Linux). A powerful SQL editor with intelligent autocomplete, visual query builders, variables, and more. You can fully control window layouts, key bindings, UI theme, mark scripts, and database objects as favorites for quick access or even work outside of DbVisualizer. DbVisualizer is also built to meet rigorous security standards, all configurable within the product.

Telegraf is the open source server agent to help you collect metrics from your stacks, sensors and systems. Telegraf is a plugin-driven server agent for collecting and sending metrics and events from databases, systems, and IoT sensors. Telegraf is written in Go and compiles into a single binary with no external dependencies, and requires a very minimal memory footprint. Telegraf can collect metrics from a wide array of inputs and write them into a wide array of outputs. It is plugin-driven for both collection and output of data so it is easily extendable. It is written in Go, which means that it is a compiled and standalone binary that can be executed on any system with no need for external dependencies, no npm, pip, gem, or other package management tools required. With 300+ plugins already written by subject matter experts on the data in the community, it is easy to start collecting metrics from your end-points.

*GraphDB allows you to link diverse data, index it for semantic search and enrich it via text analysis to build big knowledge graphs.* GraphDB is a highly efficient and robust graph database with RDF and SPARQL support. The GraphDB database supports a highly available replication cluster, which has been proven in a number of enterprise use cases that required resilience in data loading and query answering. If you need a quick overview of GraphDB or a download link to its latest releases, please visit the GraphDB product section. GraphDB uses RDF4J as a library, utilizing its APIs for storage and querying, as well as the support for a wide variety of query languages (e.g., SPARQL and SeRQL) and RDF syntaxes (e.g., RDF/XML, N3, Turtle).

NetOwl EntityMatcher provides accurate, fast, and scalable identity resolution based not only on similarities of the entity names but also other key entity attributes such as date of birth, place of birth, address, and nationality. Identity resolution can also be based on social network information such as employer, spouse, associate, etc. NetOwl performs identity resolution based on any combination of available entity record attributes by utilizing its unique proprietary search and indexing engine that allows combination of evidence from multiple matching attributes in a highly robust, scalable, and intuitive fashion. Application-specific business rules can be implemented by determining what combination of record attributes should be matched and what weights should be assigned to each attribute. NetOwl leverages our machine learning-based multicultural, multi-lingual name matching product, NetOwl NameMatcher, to enable sophisticated name matching of various entity types.

NetOwl Extractor offers highly accurate, fast, and scalable entity extraction in multiple languages using AI-based natural language processing and machine learning technologies. NetOwl's named entity recognition software can be deployed on premises or in the cloud, enabling a variety of Big Data Text Analytics applications. With over 100 types of entities, NetOwl offers a broad semantic ontology for entity extraction that goes beyond that of standard named entity extraction software. It includes people, various types of organizations (e.g., companies, governments), several types of places (e.g., countries, cities), addresses, artifacts, phone numbers, titles, etc. This expansive named entity recognition (NER) forms the foundation for more advanced relationship extraction and event extraction. Domains include Business, Finance, Politics, Homeland Security, Law Enforcement, Military, National Security, and Social Media.

NetOwl TextMiner combines our award winning NetOwl Extractor with Elasticsearch to provide unique text analytics software. TextMiner leverages all aspects of NetOwl capabilities and is ideal for supporting “what if” analysis, discovery, quick response investigation, and detailed research. NetOwl TextMiner integrates all text analytics capabilities of NetOwl Extractor, including entity extraction, relationship, and event extraction, sentiment analysis, text categorization, and geotagging into all-encompassing text mining software. Extractor output is stored in Elasticsearch for a variety of intelligent search and analytic capabilities. The combination of Elasticsearch and NetOwl provides fast and scalable real-time text analysis for Big Data. TextMiner’s Web-based UI is an easy to use and configurable text analytics tool for different analysis scenarios and enables users to gain quick access to all and only high-value information derived from a vast amount of texts.

NetOwl NameMatcher, the winner of the MITRE Multicultural Name Matching Challenge, offers the most accurate, fast, and scalable name matching available. Using a revolutionary machine learning-based approach, NetOwl addresses complex fuzzy name matching challenges. Traditional name matching approaches, such as Soundex, edit distance, and rule-based methods, suffer from both precision (false positives) and recall (false negative) problems in addressing the variety of fuzzy name matching challenges discussed above. NetOwl applies an empirically driven, machine learning-based probabilistic approach to name matching challenges. It derives intelligent, probabilistic name matching rules automatically from large-scale, real-world, multi-ethnicity name variant data. NetOwl utilizes different matching models optimized for each of the entity types (e.g., person, organization, place) In addition, NetOwl performs automatic name ethnicity detection as well.

Building underneath an established front-end, Broadleaf's Headless Commerce Solution lets you build a best-of-breed commerce platform. Use our API layer to centralize commerce administration and backend processes while enabling web, point of sale, native, and other applications. Broadleaf APIs are packaged as microservices with over 30 independently deployable and scalable bounded contexts. These services work together seamlessly to meet the needs of any next generation of commerce solution. But just being headless is not enough. With Broadleaf, you'll start with over 1,000 API endpoints each backed by completely customizable Java and Spring components. This functionality is surfaced in an admin application that utilizes metadata to provide backend users with a rich, cohesive experience while also allowing for independently maintainable microservices. With Broadleaf, you can manage the experience for multiple brands with separate carts, catalogs, customers, and orders.