Best Data Management Software for SolrCommerce

DbVisualizer is one of the world’s most popular database clients. Developers, analysts, and DBAs use it to advance their SQL experience with modern tools to visualize and manage their databases, schemas, objects, and table data and to auto-generate, write and optimize queries. It has extended support for 30+ of the major databases and has basic-level support for all databases that can be accessed with a JDBC driver. DbVisualizer runs on all major OSes. Free and Pro versions are available.

Telegraf is an open-source server agent that helps you collect metrics from your sensors, stacks, and systems. Telegraf is a plugin-driven agent that collects and sends metrics and events from systems, databases, and IoT sensors. Telegraf is written in Go. It compiles to a single binary and has no external dependencies. It also requires very little memory. Telegraf can gather metrics from a wide variety of inputs and then write them into a wide range of outputs. It can be easily extended by being plugin-driven for both the collection and output data. It is written in Go and can be run on any system without external dependencies. It is easy to collect metrics from your endpoints with the 300+ plugins that have been created by data experts in the community.

*GraphDB allows the creation of large knowledge graphs by linking diverse data and indexing it for semantic search. * GraphDB is a robust and efficient graph database that supports RDF and SPARQL. The GraphDB database supports a highly accessible replication cluster. This has been demonstrated in a variety of enterprise use cases that required resilience for data loading and query answering. Visit the GraphDB product page for a quick overview and a link to download the latest releases. GraphDB uses RDF4J to store and query data. It also supports a wide range of query languages (e.g. SPARQL and SeRQL), and RDF syntaxes such as RDF/XML and Turtle.

NetOwl Extractor provides highly accurate, fast and scalable entity extraction in multiple language using AI-based natural languages processing and machine learning technologies. NetOwl's named-entity recognition software is available on premises and in the cloud. This allows for a variety Big Data Text Analytics applications. NetOwl's named entity recognition software can be deployed on premises or in the cloud. It supports entity extraction from over 100 entities. It includes people, different types of organizations (e.g. companies, governments), various types of places (e.g. countries, cities), addresses and artifacts as well as phone numbers, titles and titles. This vast named entity recognition (NER), forms the foundation for advanced relationship extraction and event extraction. Domains include Finance, Politics and Homeland Security, Law Enforcement, Military, National Security and Social Media.

NetOwl NameMatcher was the winner of the MITRE Multicultural Name Matching Challenge. It offers the fastest, most accurate, and scalable name match possible. NetOwl solves complex fuzzy name matching problems by using a machine learning-based approach. Traditional name matching methods such as Soundex edit distance and rule-based methods have problems with precision (false positivities) and recall (false negativities) when it comes to addressing the various fuzzy name matching challenges. NetOwl uses a machine learning-based probabilistic approach that is empirically driven to solve name matching problems. It automatically derives intelligent, probabilistic names matching rules from large-scale, real world, multi-ethnicity variant data. NetOwl uses different matching models that are optimized for each entity type (e.g., person or organization, place). NetOwl also performs automatic detection of name ethnicity.