Name: Dgraph
Author: Dgraph Labs

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Financial Risk Management

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Applicant Tracking

Corporate Learning Management

Corporate Volunteering

HR Management

International Payroll

Payroll

Succession Planning

Talent Intelligence

Talent Management

Workforce Analytics

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Human Resources

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Business Internet Service Providers (ISPs)

Cloud Storage

Component Content Management

Container Management

Fraud Detection

IT Alert Management

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Integrated Development Environment (IDE)

Integration Platform as a Service (iPaaS)

Internet of Things (IoT)

Intranet

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Managed Print

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Information Technology

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Presentation

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Survey & Forms Building

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Transcription

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Sales

Antivirus

Security

Digital Forensics

Estate Planning

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Library Management

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### Query Performance
Users generally believe that Dgraph's query performance capabilities are a strong point of the platform. The consensus among reviewers is that Dgraph excels in handling complex queries efficiently, providing fast response times, and enabling users to fetch data quickly and accurately. The ability to optimize query performance, especially when dealing with intricate relationships and large datasets, has been highlighted as a key advantage of using Dgraph. Additionally, users appreciate the platform's support for various query types and its ability to streamline the querying process, ultimately enhancing overall database performance.

### Database Scalability
Users generally praise Dgraph for its exceptional database scalability and performance capabilities. The consensus among reviewers is that Dgraph's database scalability features are robust and efficient, allowing for easy scaling and management of clusters. Users appreciate the platform's ability to handle complex relationships and dynamic schema management without compromising on performance. While some users have noted minor drawbacks such as high RAM requirements for graph storage, overall, Dgraph's database scalability remains a key strength for many users.

### Performance Optimization
Users consistently highlight Dgraph's performance optimization as a key strength, emphasizing its ability to handle complex queries efficiently and deliver fast response times. The platform's optimal indexing and schema design contribute to high hop query performance, enabling users to fetch interconnected data with ease. Additionally, Dgraph's support for querying reverse edges swiftly sets it apart from other databases, particularly beneficial for applications requiring quick access to related information. The platform's scalability, coupled with its efficient loading capabilities, further enhances its performance optimization features, making it a preferred choice for handling vast datasets and addressing evolving business needs.

### Data Modeling
Users consistently highlight Dgraph's data modeling as a standout feature, emphasizing its ability to handle complex and highly interconnected data structures with ease. The platform's support for GraphQL and DQL enables users to efficiently model and query data, facilitating the creation of dynamic relationships and uncovering valuable insights from data patterns. Additionally, the intuitive interface and robust data modeling tools contribute to a positive user experience, empowering users to effectively structure and manage their data within the platform.

### Schema Management
Users generally praise Dgraph's schema management capabilities, highlighting its ease of use and efficiency in handling complex relationships and dynamic schema requirements. The platform's support for GraphQL simplifies schema validation and expansion of graph edges, enabling users to quickly adapt and optimize their data structures. Additionally, the ability to iterate on schemas without performance concerns allows for a streamlined development process, empowering users to focus on delivering value through their applications.

### Node and Edge Relationships
Users generally appreciate Dgraph's capabilities in managing nodes, edges, and relationships for their data modeling needs. The consensus among reviewers is that Dgraph excels in facilitating complex relationships and dynamic schema management, allowing for efficient querying and analysis of interconnected data. The ability to easily expand graph edges and iterate on schemas without performance concerns has been highlighted as a significant advantage by users leveraging Dgraph for various applications. Additionally, the platform's support for GraphQL enables users to filter data based on node properties and cluster variables, enhancing the flexibility and precision of data retrieval.

### Developer Tools and Resources
Users generally praise Dgraph's developer tools and resources functionality. Reviewers highlight the ease of getting started with Dgraph, especially for those familiar with GraphQL. The availability of DQL alongside GraphQL is seen as a valuable feature, although some users mention a learning curve associated with DQL. Additionally, the community support and documentation are commended for aiding developers in navigating the platform effectively. Despite some minor concerns about documentation clarity and tooling, the overall consensus leans towards positive experiences with Dgraph's developer tools and resources.

### Community and Support
Users consistently praise Dgraph's community engagement and responsive support features. The developer community on discuss.dgraph.io is highlighted as being very helpful, providing valuable assistance and guidance. Additionally, the developer advocate team is commended for their responsiveness and dedication to assisting users with any queries or issues they may encounter. The documentation is also noted for being comprehensive and user-friendly, further enhancing the overall support experience for Dgraph users.

### Enterprise Features
Users generally appreciate Dgraph's enterprise capabilities and the support provided by its professional team. While some users find the pricing model challenging for smaller companies, the overall consensus is positive regarding the platform's ability to handle real-time transactional workloads effectively. The hierarchical data structure of DGraph is particularly well-suited for large enterprise clients, outperforming traditional SQL or NoSQL databases in this aspect. However, there are some concerns about certain enterprise-only features like Multitenancy and Binary backups. Despite these limitations, users value Dgraph's unique approach to GraphQL in a Graph database and the flexibility it offers in terms of data exports.

Users commonly recommend Dgraph for graph-based applications due to its high availability. They suggest taking the time to learn the product using the excellent learning materials provided. It is also advised to ensure sufficient memory for optimal performance. Dgraph is highly recommended, even for beginners looking to set up a GraphQL database easily. It is particularly recommended for starting off with Greenfield projects, although schema adjustments may be necessary when migrating from other databases. Users appreciate the helpfulness of the Dgraph team and frequently recommend it to others.

Fast and Scalable: Dgraph is highly appreciated for its fast and scalable performance, with many users praising its impressive speed and scalability even with large datasets. Several reviewers find that scalability is built straight into Dgraph, making it easily scalable and performant.

GraphQL Support: Many reviewers find Dgraph's GraphQL support to be a standout feature, allowing for easy parsing of schemas and building of queries. Users appreciate that Dgraph eliminates the need for complex and slow SQL JOIN queries, replacing them with simple GraphQL queries that perform well regardless of the relationships in the schema.

Excellent Integration Capabilities: Reviewers appreciate that Dgraph easily integrates with other technologies such as GraphQL, allowing for more seamless connections and data sharing. Additionally, users appreciate Dgraph's suite of first-class open-source tools like Ratel, Data Visualizer, Cluster Manager as well as its integration with other services databases and technologies.

Difficult to use for new users: Reviewers have stated that Dgraph can be difficult to use for individuals who are coming from a SQL or MySQL background. The user interface is not particularly user-friendly, and the documentation can be challenging to understand for new users.

Ratel UI needs improvement: According to reviewers, Ratel UI is not very intuitive and requires some improvements in terms of its usability.

Lack of transparency about roadmap: Some users have expressed frustration with what they see as a lack of transparency from the Dgraph team regarding their roadmap. They would like more information on what features will be prioritized and when updates will occur.

Dgraph Labs headquartered in San Francisco offers a non relational database via Dgraph, an open source, distributed graph database.

Neo4j is an open source embeddable graph database developed by Neo Technologies based in San Mateo, California with an office in Sweden.

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MongoDB is an open source document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.

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Non-relational databases have the goal of increasing scalability and availability.

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Demo: Intro to Ratel UI (DQL)

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Dgraph

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What is Dgraph?