Dgraph vs Memgraph: What are the differences?

<Write Introduction here>

1. **Data Model**: Dgraph uses a graph-based data model while Memgraph employs a property graph data model. This means that Dgraph focuses on relationships between entities through edges and nodes, whereas Memgraph places emphasis on properties assigned to nodes and relationships.
2. **Query Language**: Dgraph utilizes GraphQL for querying and manipulating data, providing a powerful and flexible approach for retrieving information. On the other hand, Memgraph uses Cypher, a declarative query language specifically designed for graph databases, allowing for complex graph pattern matching.
3. **Data Consistency**: Dgraph ensures strong consistency by default, meaning that all reads reflect the latest state of data. In contrast, Memgraph provides eventual consistency, which may result in temporary discrepancies between data replicas.
4. **Scalability**: Dgraph is optimized for horizontal scalability, allowing it to efficiently handle large volumes of data across distributed environments. Memgraph also supports scalability but may require additional configurations to achieve the same level of scalability as Dgraph.
5. **Built-in Algorithms**: Memgraph offers a wide range of built-in graph algorithms, making it convenient for users to perform complex analytics tasks directly within the database. Dgraph, on the other hand, may require external libraries or services for certain algorithm implementations.
6. **Community Support and Adoption**: Dgraph has gained significant traction within the developer community and is known for its active user base and frequent updates. While Memgraph is also supported by an active community, it may have a smaller user base compared to Dgraph due to its more recent entry into the market.

In Summary, Dgraph and Memgraph differ in their data models, query languages, data consistency, scalability, built-in algorithms, and community support and adoption.