What is a graph database and what are its use cases - Definition, examples & trends
Graph databases are a relatively new and exciting type of NoSQL database that have been increasingly used for sophisticated applications over the past decade. Graph databases model their data using nodes and edges that form a “graph” (hence the name).
Graphs are mathematical abstractions of networks: for example, a social network where each person (or organization) is a node, and the connections between them are edges. This allows intuitive queries like: "Find me all people I went to school with", or "Get me all friends of my friends who work in advertising".
How graph databases work
A graph database is a kind of NoSQL database. It's a type of database that focuses on storing data as entities and their relations.
Graphs are a natural way to model complex relationships in data, such as user profiles, social connections and recommendations.
The term "graph" might seem a bit abstract, but it actually refers to a type of data structure that's used to model relationships between different pieces of information. For example, if you have a list of friends and their contact information, you can use a graph database to store this data in the form of nodes (pieces of information) and edges (connections between them).
Graph databases are different from other types of NoSQL databases because it stores data as nodes (vertices) and edges, rather than as rows and columns. This makes them ideal for storing highly connected data, such as social networks or recommendation systems.
They are used for a variety of applications, including social networking, recommendation systems, fraud detection and analysis, and logistics.
What are the benefits of a graph database?
Graph databases are a relatively new concept in the database world. They are best known for their ability to handle relational data, such as relationships between people, places and things. Graphs are a good match for complex relationships between entities, and graph databases can be used to model complex data in many domains.
This makes them a good fit for many real-world use cases, from social networks to supply chains, where multiple entities are connected in complex ways.
The power of graph databases in a nutshell: The best way to understand the power of using a graph database is to compare it with its traditional counterpart — the relational database.
Some of the benefits of using a graph database include:
Every entity and their relationships in the real world can be represented as a graph. This means you get all of the benefits of traditional relational databases without having to worry about foreign keys or other limitations. Also, the modeling of real-world data will be more intuitive because you are essentially abstracting the real world into a graph of entities and relations.
Graph databases support structured or unstructured data. You can store any type of data in a graph database without worrying about restructuring it first. You can also combine different types of data sources into one query if they're all represented as nodes and edges.
Simple querying for multi-hop queries. Graph databases allow you to perform multi-hop queries to find out how far away something is from another point in just one line of GQL statement, which is almost impossible in traditional relational databases.
Supports both structured data and unstructured data. Graph databases support both structured data, like tabular data, and unstructured data, like JSON, making them ideal for storing semi-structured data such as sensor data collected by IoT devices.
Graph databases are also highly performant because they allow you to query complex queries over the network of connections between objects rather than just individual objects themselves. For example: find all people who know each other through their friend groupings; find all people who like each other's posts on Facebook; or find all people who live in the same city as each other even if they don't know each other directly but may have passed by each other during their daily commute.
Graph database use cases
Graph databases can be used to support various graph-based scenarios. These include social networking, recommendation systems, fraud detection, and chatbot systems, etc.
Fraud detection is important to any organization, be it a financial service or simply a web service. With the increase in technology and the ease of information being shared with other parties, traditional methods of fraud detection have been rendered obsolete.
Graph technology is the state of the art of fraud detection. The reason is that it contains a massive amount of data, and even if one piece of information is incorrect or missing, the system will still be able to identify the user as fraudulent. Graph technology is used by financial institutions to detect fake identities (e.g., people trying to open accounts with fake ID cards), credit card fraud (e.g., someone applying for credit with a stolen credit card), and money laundering (e.g., someone trying to move money from one account into another).
Recommendation engines are widely used by websites such as Amazon, Netflix, Spotify and others for generating personalized recommendations for products or content based on a user's prior interests or history with the website.
For example, if you've bought X before then perhaps we should recommend Y? The most common type of recommendation engine is collaborative filtering which makes recommendations by analyzing user behavior data collected from previous purchases etc.
Graph databases are well suited for this task because they can leverage users' relationships with friends or colleagues to recommend relevant content.
Natural languages can be transformed into knowledge graphs and stored in a graph database. A question organized in a natural language can be resolved by a semantic parser in an intelligent question-answer system and re-organized. Then, possible answers to the question can be retrieved from the knowledge graph and provided to the one who asked the question.
Social networking is a very popular use case for graph databases. Graph databases such as NebulaGraph and Neo4j are specifically designed to support social networks. A social network is a way of representing relationships between people or things. They are often represented visually in the form of graphs that show how things are connected. Graphs can be used to represent both online and offline relationships between people; for example, Facebook uses them to represent friendships between users and LinkedIn uses them to represent professional connections between users.
Latest trends in graph database
Graph database is a somewhat novel piece of technology and it is consistently evolving. The field was once dominated by traditional powerhouse Neo4j, but now the market is much more dynamic, with new players like NebulaGraph, AWS Neptune, and Janus Graph.
Graph databases have been around for more than 20 years. They were first used by computer scientists working on artificial intelligence (AI) projects, who needed a way to model complex systems that can't be represented as tables or lists of records.
Today, businesses are increasingly using graph databases for their own needs. The graph database market is expected to grow from $1.59 billion in 2020 to $11.25 billion by 2030, according to Emergen Research.
The popularity of graph databases has been driven by several factors:
Data grows rapidly — Every day we generate more than 2.5 quintillion bytes of data globally. This is expected to grow exponentially over time as sensors become more prevalent, mobile devices become more ubiquitous and machine-to-machine communication increases
Data types vary widely — From unstructured text documents and images to structured data such as financial transaction records or sensor readings, many different types of data exist today
Data distribution varies widely — Data can be distributed across multiple locations at different scales (local vs global) and in different formats (JSON vs XML).
Because of the explosion of global data volume and the distribution of data, graph databases are becoming more important and are adapting themselves to these trends.
Here are some of the latest trends in the graph database field.
Distributed graph databases
Distributed graph databases is the latest trend in graph database as global data grows rapidly. A distributed database is a database that is split into multiple servers, so that the throughput and storage capacity of the entire system is greater than the sum of its parts.
A distributed graph database is a database that consists of two or more files located in different sites either on the same network or on entirely different networks. Portions of the database are stored in multiple physical locations and processing is distributed among multiple database nodes.
Distributed databases provide high availability and fault tolerance because they maintain multiple copies of data that can be accessed through processes known as replication and shadowing. The cost of maintaining such systems has traditionally been considered too high for general purpose applications like spreadsheets or word processors, but with the advent of inexpensive disk storage there are now many applications that require such features.
Distributed databases are ideal for large-scale applications with complex data requirements, such as e-commerce systems and the Internet. They can also be useful when there are some constraints on the location of the data — for example, if you need to keep certain records in a certain jurisdiction (eg. the EU) because they contain sensitive information or if you have regional offices that need to share data across their networks.
The technical and cost advantages of distributed systems (like NebulaGraph) over single machines (e.g. Neo4j) or small machines are more obvious due to the increasing volume of data and computation. Distributed systems allow applications to access these thousands of machines as if they were local systems, without the need for much modification at the code level.
NebulaGraph is a distributed, easily scalable, and native graph database. It is capable of hosting graphs with hundreds of billions of vertices and trillions of edges, and serving queries with millisecond-latency.
With a shared-nothing distributed architecture, NebulaGraph offers linear scalability, meaning that you can add more nodes or services to the cluster without affecting performance. It also means that if you want to horizontally scale out NebulaGraph, you don’t need to change the configuration of the existing nodes. As long as the network bandwidth is sufficient, you can add more nodes without changing anything else.
GQL - The graph query language standard
One of the disadvantages of graph databases was the lack of a standard graph query language like SQL for relational databases.
In the past, you have got Cypher, a declarative graph query language invented by Neo4j; Gremlin, a graph traversal language developed by Apache TinkerPop; and nGQL, NebulaGraph’s SQL-like graph query language.
The lack of a standard in query languages made it difficult to build applications that used different graph databases from different vendors.
However, in the last few years, there has been a lot of research activity in this area and some standardization efforts have been made by the industry.
In September 2019, members of ISO/IEC Joint Technical Committee, which is responsible for international Information Technology standards, proposed a project to create a new standard graph query language (ISO/IEC 39075 Information Technology — Database Languages — GQL). GQL is intended to be a declarative database query language, like SQL.
The GQL project proposal states:
"Using graph as a fundamental representation for data modeling is an emerging approach in data management. In this approach, the data set is modeled as a graph, representing each data entity as a vertex (also called a node) of the graph and each relationship between two entities as an edge between corresponding vertices.> The graph data model has been drawing attention for its unique advantages. Firstly, the graph model can be a natural fit for data sets that have hierarchical, complex, or even arbitrary structures. Such structures can be easily encoded into the graph model as edges. This can be more convenient than the relational model, which requires the normalization of the data set into a set of tables with fixed row types. Secondly, the graph model enables efficient execution of expensive queries or data analytic functions that need to observe multi-hop relationships among data entities, such as reachability queries, shortest or cheapest path queries, or centrality analysis.> There are two graph models in current use: the Resource Description Framework (RDF) model and the Property Graph model. The RDF model has been standardized by W3C in a number of specifications. The Property Graph model, on the other hand, has a multitude of implementations in graph databases, graph algorithms, and graph processing facilities.> However, a common, standardized query language for property graphs (like SQL for relational database systems) is missing. GQL is proposed to fill this void."
It is expected that the GQL standard become available by the end of this year(2022). In June 2022, the GQL Standard website said in an update that “It turns out that writing a database language standards is a lot of work, but we are making progress.”
Just like how SQL provided to a boost to the popularity of relational databases, the availability of GQL standard is also expected to further promote the use of graph database.
Graph database is the future of business intelligence
Graph databases have been around for decades. In recent years, however, they’ve become increasingly popular because they offer new ways of visualizing and analyzing data that can help companies make better decisions about their business processes.
It's not just a buzzword; companies like Facebook, LinkedIn, Google and Twitter are using graph databases to store their data and make sense of it.
Graph databases are a carrier of big data, which makes it possible to process anything you need using an intuitive and effective means; by using information, relational data and non-relational data as input. The graph databases market is now young enough to raise your interest – the future looks very promising for graph databases.
For now, the future of business intelligence appears to be a distinctive blend of "old and new". Relational databases will continue to be widely used but increasingly supplemented with or replaced by graph databases – with the caveat that this switch is not an "either/or" affair. Nor does it represent a radical change in attitude toward data storage: the key lies in adopting an enterprise-wide understanding of data, rather than one based on individual silos.
Market overview of graph databases
Now that we have discussed what a graph is, let's move on to further understanding graph databases developed based on graph theory and the property graph model.
Different graph databases may differ slightly in terms of terminology, but in the end, they all talk about vertices, edges, and properties. As for more advanced features such as labels, indexes, constraints, TTL, long tasks, stored procedures, and UDFs, these advanced features will vary significantly from one graph database to another.
Graph databases use graphs to store data, and the graph structure is one of the structures that are closest to high flexibility and high performance. A graph database is a storage engine specifically designed to store and retrieve large information, which efficiently stores data as vertices and edges and allows high-performance retrieval and querying of these vertex-edge structures. We can also add properties to these vertices and edges.
Third-party services market predictions
According to DB-Engines.com, the world's leading database ranking site, graph databases have been the fastest growing database category since 2013 ^dbe.
The site counts trends in the popularity of each category based on several metrics, including records and trends based on search engines such as Google, technical topics discussed on major IT technology forums and social networking sites, job posting changes on job boards. 371 database products are included in the site and are divided into 12 categories. Of these 12 categories, a category like graph databases is growing much faster than any of the others.
Gartner, one of the world's top think tanks, identified graph databases as a major business intelligence and analytics technology trend long before 2013 ^Gartner1. At that time, big data was hot as ever, and data scientists were in a hot position.
Until recently, graph databases and related graph technologies were ranked in the Top 10 Data and Analytics Trends for 2021 ^Gartner2.
!!! quote "Trend 8: Graph Relates Everything"
Graphs form the foundation of many modern data and analytics capabilities to find relationships between people, places, things, events, and locations across diverse data assets. D&A leaders rely on graphs to quickly answer complex business questions which require contextual awareness and an understanding of the nature of connections and strengths across multiple entities. Gartner predicts that by 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision-making across the organization.
It can be noted that Gartner's predictions match the DB-Engines ranking well. There is usually a period of rapid bubble development, then a plateau period, followed by a new bubble period due to the emergence of new technologies, and then a plateau period again.
Market size of graph databases
According to statistics and forecasts from Verifiedmarketresearc^ver, fnfresearch^fnf, MarketsandMarkets^mam, and Gartner^gar, the global graph database market size is about to grow from about USD 0.8 billion in 2019 to USD 3-4 billion by 2026, at a Compound Annual Growth Rate (CAGR) of about 25%, which corresponds to about 5%-10% market share of the global database market.
Graph database market participants
Neo4j, the pioneer of (first generation) graph databases
Although some graph-like data models and products, and the corresponding graph language G/G+ had been proposed in the 1970s (e.g. CODASYL ^DDIA). But it is Neo4j, the main pioneer in this market, that has really made the concept of graph databases popular, and even the two main terms (labeled) property graphs and graph databases were first introduced and practiced by Neo4j.
[^Glang]: I. F. Cruz, A. O. Mendelzon, and P. T. Wood. A Graphical Query Language Supporting Recursion. In Proceedings of the Association for Computing Machinery Special Interest Group on Management of Data, pages 323–330. ACM Press, May 1987.
!!! Info "This section on the history of Neo4j and the graph query language it created, Cypher, is largely excerpted from the ISO WG3 paper An overview of the recent history of Graph Query Languages [^Tobias2018] and [^Glang]. To take into account the latest two years of development, the content mentioned in this topic has been abridged and updated by the authors of this book.
!!! Note "About GQL (Graph Query Language) and the development of an International Standard"
Readers familiar with databases are probably aware of the Structured Query Language SQL. by using SQL, people access databases in a way that is close to natural language. Before SQL was widely adopted and standardized, the market for relational databases was very fragmented. Each vendor's product had a completely different way of accessing. Developers of the database product itself, developers of the tools surrounding the database product, and end-users of the database, all had to learn each product. When the SQL-89 standard was developed in 1989, the entire relational database market quickly focus on SQL-89. This greatly reduced the learning costs for the people mentioned above. GQL (Graph Query Language) assumes a role similar to SQL in the field of graph databases. Uses interacts with graphs with GQL. Unlike international standards such as SQL-89, there are no international standards for GQL. Two mainstream graph languages are Neo4j’s Cypher and Apache TinkerPop's Gremlin. The former is often referred to as the DQL, Declarative Query Language. DQL tells the system "what to do", regardless of "how to do". The latter is referred to as the IQL, Imperative Query Language. IQL explicitly specifies the system's actions. The GQL International Standard is in the process of being developed.
[^Tobias2018]: "An overview of the recent history of Graph Query Languages". Authors: Tobias Lindaaker, U.S. National Expert.Date: 2018-05-14
Overview of the recent history of graph databases
- In 2000, the idea of modeling data as a network came to the founders of Neo4j.
- In 2001, Neo4j developed the earliest core part of the code.
- In 2007, Neo4j started operating as a company.
- In 2009, Neo4j borrowed XPath as a graph query language. Gremlin [^Gremlin] is also similar to XPath.
- In 2010, Marko Rodriguez, a Neo4j employee, used the term Property Graph to describe the data model of Neo4j and TinkerPop (Gremlin).
- In 2011, the first public version Neo4j 1.4 was released, and the first version of Cypher was released.
- In 2012, Neo4j 1.8 enabled you to write a Cypher. Neo4j 2.0 added labels and indexes. Cypher became a declarative graph query language.
- In 2015, Cypher was opened up by Neo4j through the openCypher project.
- In 2017, the ISO WG3 organization discussed how to use SQL to query property graph data.
- In 2018, Starting from the Neo4j 3.5 GA, the core of Neo4j only for the Enterprise Edition will no longer be open source.
- In 2019, ISO officially established two projects ISO/IEC JTC 1 N 14279 and ISO/IEC JTC 1/SC 32 N 3228 to develop an international standard for graph database language.
- In 2021, the $325 million Series F funding round for Neo4j marks the largest investment round in database history.
[^Gremlin]: Gremlin is a graph language developed based on Apache TinkerPop.
The early history of Neo4j
The data model property graph was first conceived in 2000. The founders of Neo4j were developing a media management system, and the schema of the system was often changed. To adapt to such changes, Peter Neubauer, one of the founders, wanted to enable the system to be modeled to a conceptually interconnected network. A group of graduate students at the Indian Institute of Technology Bombay implemented the earliest prototypes. Emil Eifrém, the Neo4j co-founder, and these students spent a week extending Peter's idea into a more abstract model: vertices were connected by relationships, and key-values were used as properties of vertices and relationships. They developed a Java API to interact with this data model and implemented an abstraction layer on top of the relational database.
Although this network model greatly improved productivity, its performance has been poor. So Johan Svensson, Neo4j co-founder, put a lot of effort into implementing a native data management system, that is Neo4j. For the first few years, Neo4j was successful as an in-house product. In 2007, the intellectual property of Neo4j was transferred to an independent database company.
In the first public release of Neo4j ( Neo4j 1.4, 2011), the data model was consisted of vertices and typed edges. Vertices and edges have properties. The early versions of Neo4j did not have indexes. Applications had to construct their search structure from the root vertex. Because this was very unwieldy for the applications, Neo4j 2.0 (2013.12) introduced a new concept label on vertices. Based on labels, Neo4j can index some predefined vertex properties.
"Vertex", "Relationship", "Property", "Relationships can only have one label.", "Vertices can have zero or multiple labels.". All these concepts form the data model definitions for Neo4j property graphs. With the later addition of indexing, Cypher became the main way of interacting with Neo4j. This is because the application developer only needs to focus on the data itself, not on the search structure that the developer built himself as mentioned above.
The creation of Gremlin
Gremlin is a graph query language based on Apache TinkerPop, which is close in style to a sequence of function (procedure) calls. Initially, Neo4j was queried through the Java API. applications could embed the query engine as a library into the application and then use the API to query the graph.
The early Neo4j employees Tobias Lindaaker, Ivarsson, Peter Neubauer, and Marko Rodriguez used XPath as a graph query. Groovy provides loop structures, branching, and computation. This was the original prototype of Gremlin, the first version of which was released in November 2009.
Later, Marko found a lot of problems with using two different parsers (XPath and Groovy) at the same time and changed Gremlin to a Domain Specific Language (DSL) based on Groovy.
The creation of Cypher
Gremlin, like Neo4j's Java API, was originally intended to be a procedural way of expressing how to query databases. It uses shorter syntaxes to query and remotely access databases through the network. The procedural nature of Gremlin requires users to know the best way to query results, which is still burdensome for application developers. Over the last 30 years, the declarative language SQL has been a great success. SQL can separate the declarative way to get data from how the engine gets data. So the Neo4j engineers wanted to develop a declarative graph query language.
In 2010, Andrés Taylor joined Neo4j as an engineer. Inspired by SQL, he started a project to develop graph query language, which was released as Neo4j 1.4 in 2011. The language is the ancestor of most graph query languages today - Cypher.
Cypher's syntax is based on the use of ASCII art to describe graph patterns. This approach originally came from the annotations on how to describe graph patterns in the source code. An example can be seen as follows.
Simply put, ASCII art uses printable text to describe vertices and edges. Cypher syntax uses
() for vertices and
--> for edges.
(query)-[modeled as]->(drawing) is used to represent a simple graph relationship (which can also be called graph schema):
the starting vertex - query,
the destination vertex - drawing, and
the edge - modeled as.
The first version of Cypher implemented graph reading, but users should specify vertices from which to start querying. Only from these vertices could graph schema matching be supported.
In a later version, Neo4j 1.8, released in October 2012, Cypher added the ability to modify graphs. However, queries still need to specify which nodes to start from.
In December 2013, Neo4j 2.0 introduced the concept of a label, which is essentially an index. This allows the query engine to use the index to select the vertices matched by the schema, without requiring the user to specify the vertex to start the query.
With the popularity of Neo4j, Cypher has a wide community of developers and is widely used in a variety of industries. It is still the most popular graph query language.
In September 2015, Neo4j established the openCypher Implementors Group (oCIG) to open source Cypher to openCypher, to govern and advance the evolution of the language itself through open source.
Cypher has inspired a series of graph query languages, including:
2015, Oracle released PGQL, a graph language used by the graph engine PGX.
2016, the Linked Data Benchmarking Council (short for LDBC) an industry-renowned benchmarking organization for graph performance, released G-CORE.
2018, RedisGraph, a Redis-based graph library, adopted Cypher as its graph language.
2019, the International Standards Organization ISO started two projects to initiate the process of developing an international standard for graph languages based on existing industry achievements such as openCypher, PGQL, GSQL^GSQL, and G-CORE.
2019, NebulaGraph released NebulaGraph Query Language (nGQL) based on openCypher.
Distributed graph databases market participants
From 2005 to 2010, with the release of Google's cloud computing "Troika", various distributed architectures became increasingly popular, including Hadoop and Cassandra, which have been open-sourced. Several implications are as follows:
The technical and cost advantages of distributed systems over single machines (e.g. Neo4j) or small machines are more obvious due to the increasing volume of data and computation. Distributed systems allow applications to access these thousands of machines as if they were local systems, without the need for much modification at the code level.
The open-source approach allows more people to know emerging technologies and feedback to the community in a more cost-effective way, including code developers, data scientists, and product managers.
Strictly speaking, Neo4j also offers several distributed capabilities, which are quite different from the industry's sense of the distributed system.
Neo4j 3. x requires that the full amount of data must be stored on a single machine. Although it supports full replication and high availability between multiple machines, the data cannot be sliced into different subgraphs.
Neo4j 4. x stores a part of data on different machines (subgraphs), and then the application layer assembles data in a certain way (called Fabric)^fosdem20 and distributes the reads and writes to each machine. This approach requires a log of involvement and work from the application layer code. For example, designing how to place different subgraphs on which machines they should be placed and how to assemble some of the results obtained from each machine into the final result.
The style of its syntax is as follows:
Cypher USE graphA MATCH (movie:Movie) Return movie.title AS title UNION USE graphB MATCH (move:Movie) RETURN movie.title AS title
The second generation (distributed) graph database: Titan and its successor JanusGraph
In 2011, Aurelius was founded to develop an open-source distributed graph database called Titan ^titan. By the first official release of Titan in 2015, the backend of Titan can support many major distributed storage architectures (e.g. Cassandra, HBase, Elasticsearch, BerkeleyDB) and can reuse many of the conveniences of the Hadoop ecosystem, with Gremlin as a unified query language on the frontend. It is easy for programmers to use, develop and participate in the community. Large-scale graphs could be sharded and stored on HBase or Cassandra (which were relatively mature distributed storage solutions at the time), and the Gremlin language was relatively full-featured though slightly lengthy. The whole solution was competitive at that time (2011-2015).
The following picture shows the growth of Titan and Neo4j stars on Github.com from 2012 to 2015.
After Aurelius (Titan) was acquired by DataStax in 2015, Titan was gradually transformed into a closed-source commercial product(DataStax Enterprise Graph).
After the acquisition of Aurelius(Titan), there has been a strong demand for an open-source distributed graph database, and there were not many mature and active products in the market. In the era of big data, data is still being generated in a steady stream, far faster than Moore's Law. The Linux Foundation, along with some technology giants (Expero, Google, GRAKN.AI, Hortonworks, IBM, and Amazon) replicated and forked the original Titan project and started it as a new project JanusGraph^Janus. Most of the community work including development, testing, release, and promotion, has been gradually shifted to the new JanusGraph.
The following graph shows the evolution of daily code commits (pull requests) for the two projects, and we can see:
Although Aurelius(Titan) still has some activity in its open-source code after its acquisition in 2015, the growth rate has slowed down significantly. This reflects the strength of the community.
After the new project was started in January 2017, its community became active quickly, surpassing the number of pull requests accumulated by Titan in the past 5 years in just one year. At the same time, the open-source Titan came to a halt.
Famous products of the same period OrientDB, TigerGraph, ArangoDB, and DGraph
In addition to JanusGraph managed by the Linux Foundation, more vendors have been joined the overall market. Some distributed graph databases that were developed by commercial companies use different data models and access methods.
The following table only lists the main differences.
|Vendors||Creation time||Core product||Open source protocol||Data model||Query language|
|OrientDB LTD (Acquired by SAP in 2017)||2011||OrientDB||Open source||Document + KV + Graph||OrientDB SQL (SQL-based extended graph abilities)|
|GraphSQL (was renamed TigerGraph)||2012||TigerGraph||Commercial version||Graph (Analysis)||GraphSQL (similar to SQL)|
|ArangoDB GmbH||2014||ArangoDB||Apache License 2.0||Document + KV + Graph||AQL (Simultaneous operation of documents, KVs and graphs)|
|DGraph Labs||2016||DGraph||Apache Public License 2.0 + Dgraph Community License||Originally RDF, later changed to GraphQL||GraphQL+-|
Traditional giants Microsoft, Amazon, and Oracle
In addition to vendors focused on graph products, traditional giants have also entered the graph database field.
Microsoft Azure Cosmos DB^cosmos is a multimodal database cloud service on the Microsoft cloud that provides SQL, document, graph, key-value, and other capabilities. Amazon AWS Neptune^neptune is a graph database cloud service provided by AWS support property graphs and RDF two data models. Oracle Graph^Oracle is a product of the relational database giant Oracle in the direction of graph technology and graph databases.
NebulaGraph, a new generation of open-source distributed graph databases
In the following topics, we will formally introduce NebulaGraph, a new generation of open-source distributed graph databases.