awesome-kafka
Memgraph
awesome-kafka | Memgraph | |
---|---|---|
1 | 44 | |
565 | 2,096 | |
- | 2.5% | |
4.7 | 9.7 | |
3 months ago | 4 days ago | |
C++ | ||
- | Business Source License (BSL) |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-kafka
Memgraph
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Ask HN: Who is hiring? (March 2024)
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 some very difficult problems.
Either DM me (the hiring manager) or apply here: https://join.com/companies/memgraph/10684850-staff-software-...
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Ask HN: Were Graph Databases a Mirage?
It's not possible to escape tradeoffs. To deal with tradeoffs, focus is important. API to tradeoffs is also important.
I bet somebody will raise a similar question in a few years time when the list under https://db-engines.com/en/ranking/graph+dbms will be bigger.
DISCLAIMER: Coming from https://github.com/memgraph/memgraph
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In-memory vs. disk-based databases: Why do you need a larger than memory architecture?
Albeit the significant engineering endeavor, the larger-than-memory architecture is a super valuable asset to Memgraph users since it allows them to store large amounts of data cheaply on disk without sacrificing the performance of in-memory computation. We are actively working on resolving issues introduced with the new storage mode, so feel free to ask, open an issue, or pull a request. We will be more than happy to help. Until next time 🫡
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When to Use a NoSQL Database
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, large-scale applications, and agile development processes. The most popular examples of NoSQL databases are MongoDB (document), Memgraph (graph), Redis (key-value store) and Apache HBase (column-oriented).
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Understanding Cosine Similarity in Python with Scikit-Learn
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 real-time use cases at an enterprise scale. Memgraph supports strongly-consistent ACID transactions and uses the standardized Cypher query language for structuring, manipulating, and exploring data.
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History of Open-Source Licenses: What License to Choose?
It should be noted this article is on the blog of a project which advertises itself as open source, under a BSL license that puts limitations on distribution and use.
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Introduction to Benchgraph and its Architecture
At the moment, benchgraph is a project under Memgraph repository (previously Mgbench). It consists of Python scripts and a C++ client. Python scripts are used to manage the benchmark execution by preparing the workload, configurations, and so on, while the C++ client actually executes the benchmark.
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How to Benchmark Memgraph [or Neo4j] with Benchgraph?
These five steps will result in something similar to this simplified version of demo.py example:
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Are indices used as much in Graph databases like Neo4j as in SQL databases?
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.
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How to Identify Essential Proteins Using Betweenness Centrality
In this tutorial, we will utilize betweenness centrality for identifying essential proteins. For this task, we are using Memgraph, a graph analytics platform, which can perform complex graph analysis on all sorts of networks. Even though we will use betweenness centrality, other graph algorithms can also be applied to the protein-protein interaction network, such as other centrality measures or the PageRank algorithm.
What are some alternatives?
cribl-syslog-input - This Pack enables a variety of functions when LogStream is used to receive data from Syslog senders.
faust - Python Stream Processing. A Faust fork
kattlo-cli - Kattlo CLI Project
kuzu - Embeddable property graph database management system built for query speed and scalability. Implements Cypher.
Benthos - Fancy stream processing made operationally mundane
Apache AGE - Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL.
go-streams - A lightweight stream processing library for Go
serverless-graphql - Serverless GraphQL Examples for AWS AppSync and Apollo
kafka-ui - Open-Source Web UI for Apache Kafka Management
cugraph - cuGraph - RAPIDS Graph Analytics Library
demo-news-recommendation - Exploring News Recommendation With Neo4j GDS
gqlalchemy - GQLAlchemy is a library developed with the purpose of assisting in writing and running queries on Memgraph. GQLAlchemy supports high-level connection to Memgraph as well as modular query builder.