Memgraph is a streaming graph application platform that helps you wrangle your streaming data, build sophisticated models that you can query in real-time, and develop applications you never thought possible in days, not months.
Memgraph directly connects to your streaming infrastructure, so you and your team don’t spend countless hours building and maintaining complex data pipelines. You can ingest data from sources like Kafka, SQL, or plain CSV files. Memgraph provides a standard interface to query your data with Cypher, a widely-used and declarative query language that is easy to write, understand and optimize for performance. This is achieved by using the property graph data model, which stores data in terms of objects, their attributes, and the relationships that connect them. This is a natural and effective way to model many real-world problems without relying on complex SQL schemas.
Memgraph is implemented in C/C++ and leverages an in-memory first architecture to ensure that you’re getting the best possible performance consistently and without surprises. It’s also ACID-compliant and highly available.
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The product is very robust and easy to use. I highly recommend it to anyone who needs to analyze streaming data in real-time.
Memgraph might be a bit more popular than Dgraph. We know about 21 links to it since March 2021 and only 20 links to Dgraph. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Memgraph | Staff C++ Database Engineer | REMOTE (Central/Western Europe, LatAm, or North America) https://memgraph.com/ Memgraph is a Seed stage, open source graph database vendor. Graph DBs are a great solution for GenAI, logistics, cybersecurity and fintech so we are looking to grow aggressively this year. We're looking for a staff-level engineer to set technical direction, mentor junior team members, and solve... - Source: Hacker News / 3 months ago
Relational databases have a much longer history of development, and much more engineering time has went into designing RDBMS. It is not a surprise that they are mature on more levels. By looking at the age of a product, you can get a sense of how mature RDBMS systems are compared to most GraphDB projects. Horizontal scaling is hard in GraphDBs due to the nature of how the graph is structured and how you interact... - Source: Hacker News / 7 months ago
NoSQL databases are non-relational databases with flexible schema designed for high performance at a massive scale. Unlike traditional relational databases, which use tables and predefined schemas, NoSQL databases use a variety of data models. There are 4 main types of NoSQL databases - document, graph, key-value, and column-oriented databases. NoSQL databases generally are well-suited for unstructured data,... - Source: dev.to / 11 months ago
Whether it's about identifying similar user profiles in a social network, detecting similar patterns in a communication network, or classifying nodes in a semantic network, cosine similarity contributes valuable insights. Combined with a powerful graph database system, such as Memgraph, it gives a better understanding of complex networks. Memgraph is an open-source in-memory graph database built to handle... - Source: dev.to / about 1 year ago
Take a look at this blog post about choosing the optimal index. It focuses on Memgraph graph database but it offers a theoretical background that is not vendor related. Source: about 1 year ago
Dgraph: A distributed and scalable graph database known for high performance. It's a good fit for large-scale graph processing, offering a GraphQL-like query language and gRPC API support. - Source: dev.to / 4 months ago
DGraph – A distributed GraphQL database with a graph backend. - Source: dev.to / over 1 year ago
How does it compare to, say grakn (renamed https://vaticle.com/, I think?), or draph (https://dgraph.io/), or Ontotext's GraphDB (https://www.ontotext.com/products/graphdb/), or Datomic? Source: over 1 year ago
Consul Connect service mesh has a higher memory footprint, so on a small cluster with e5-medium nodes (2 vCPUs, 4 GB memory), you will only be able to support a maximum of 6 side-car proxies. In order to get an application like Dgraph working, which will have 6 nodes (3 Dgraph Alpha pods and 3 Dgraph Zero pods) for high availability along with at least one client, a larger footprint with more robust Kubernetes... - Source: dev.to / over 1 year ago
Looking forward comparison with Dgraph ( https://dgraph.io/ ) — I mentioned Dgraph in other, older, posts. I'm not a shill, just a Dgraph user who's looking for alternative. - Source: Hacker News / over 1 year ago
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