TigerGraph Description
The TigerGraph™, a graph platform based on its Native Parallel Graph™, technology, represents the next evolution in graph database evolution. It is a complete, distributed parallel graph computing platform that supports web-scale data analytics in real time. Combining the best ideas (MapReduce, Massively Parallel Processing, and fast data compression/decompression) with fresh development, TigerGraph delivers what you've been waiting for: the speed, scalability, and deep exploration/querying capability to extract more business value from your data.
TigerGraph Alternatives
MongoDB Atlas
MongoDB Atlas stands out as the leading cloud database service available, offering unparalleled data distribution and seamless mobility across all major platforms, including AWS, Azure, and Google Cloud. Its built-in automation tools enhance resource management and workload optimization, making it the go-to choice for modern application deployment. As a fully managed service, it ensures best-in-class automation and adheres to established practices that support high availability, scalability, and compliance with stringent data security and privacy regulations. Furthermore, MongoDB Atlas provides robust security controls tailored for your data needs, allowing for the integration of enterprise-grade features that align with existing security protocols and compliance measures. With preconfigured elements for authentication, authorization, and encryption, you can rest assured that your data remains secure and protected at all times. Ultimately, MongoDB Atlas not only simplifies deployment and scaling in the cloud but also fortifies your data with comprehensive security features that adapt to evolving requirements.
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DataHub
DataHub is a versatile open-source metadata platform crafted to enhance data discovery, observability, and governance within various data environments. It empowers organizations to easily find reliable data, providing customized experiences for users while avoiding disruptions through precise lineage tracking at both the cross-platform and column levels. By offering a holistic view of business, operational, and technical contexts, DataHub instills trust in your data repository. The platform features automated data quality assessments along with AI-driven anomaly detection, alerting teams to emerging issues and consolidating incident management. With comprehensive lineage information, documentation, and ownership details, DataHub streamlines the resolution of problems. Furthermore, it automates governance processes by classifying evolving assets, significantly reducing manual effort with GenAI documentation, AI-based classification, and intelligent propagation mechanisms. Additionally, DataHub's flexible architecture accommodates more than 70 native integrations, making it a robust choice for organizations seeking to optimize their data ecosystems. This makes it an invaluable tool for any organization looking to enhance their data management capabilities.
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Objectivity/DB
Objectivity/DB (or Object Database Management System) is a distributed, highly scalable, high-performance, and highly scalable Object Database (ODBMS). It excels at complex data handling, including many types of connections between objects as well as many variants.
Objectivity/DB can also be used as a graph database that is highly scalable and high-performance. Its DO query language allows for standard data retrieval queries and high-performance path-based navigational inquiries.
Objectivity/DB is a distributed data base that presents a single logical view of its managed data. Data can be hosted on one machine or distributed over up to 65,000 machines. Machines can be connected to one another.
Objectivity/DB can be used on 32- or 64-bit processors that run Windows, Linux, and Mac OS X.
APIs are C++, C# Java, Python, and Java.
All platforms and languages are interoperable. A C++ program on Linux can store objects and a Java program on Mac OS X can read them.
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Amazon Neptune
Amazon Neptune is an efficient and dependable graph database service that is fully managed, facilitating the development and operation of applications that handle intricate, interconnected datasets. At its heart, Amazon Neptune features a specialized, high-performance database engine tailored for the storage of billions of relationships while enabling rapid querying with latency measured in milliseconds. It accommodates widely-used graph models, including Property Graph and W3C's RDF, along with their associated query languages, Apache TinkerPop Gremlin and SPARQL, which simplifies the process of crafting queries for navigating complex datasets. This service supports various graph-based applications, including recommendation systems, fraud detection mechanisms, knowledge graphs, drug discovery initiatives, and enhanced network security protocols. With a proactive approach, it enables the detection and analysis of IT infrastructure threats through a multi-layered security framework. Furthermore, it allows users to visualize their entire infrastructure to effectively plan, forecast, and address potential risks, while also enabling the creation of graph queries for the near-real-time identification of fraudulent patterns in financial and purchasing activities, thereby enhancing overall security and efficiency.
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Integrations
Company Details
Company:
TigerGraph
Headquarters:
United States
Website:
www.tigergraph.com
Media
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Product Details
Platforms
Web-Based
TigerGraph Features and Options
TigerGraph User Reviews
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Likelihood to Recommend to Others1 2 3 4 5 6 7 8 9 10
Native and Parellel indeed! Edited: Mar 02 2021
Summary: I am a user and a developer of this Graph DB technology, GSQL, and satisfied about the outcome so far and excited for future enhancements.
Positive: The response time of a query is RealTime and the GSQL as a language is easy to learn.And there is a lot of data loaders like Kafka, CSV, Node Red and many other connectors.
And did i mention that TigerGraph is the Graph db Leader.Negative: - Fast
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