Memgraph
orchest
Memgraph | orchest | |
---|---|---|
44 | 44 | |
2,096 | 4,022 | |
2.5% | 0.1% | |
9.7 | 4.5 | |
2 days ago | 11 months ago | |
C++ | TypeScript | |
Business Source License (BSL) | Apache License 2.0 |
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.
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.
orchest
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Decent low code options for orchestration and building data flows?
You can check out our OSS https://github.com/orchest/orchest
- Build ML workflows with Jupyter notebooks
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Building container images in Kubernetes, how would you approach it?
The code example is part of our ELT/data pipeline tool called Orchest: https://github.com/orchest/orchest/
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Launch HN: Patterns (YC S21) – A much faster way to build and deploy data apps
First want to say congrats to the Patterns team for creating a gorgeous looking tool. Very minimal and approachable. Massive kudos!
Disclaimer: we're building something very similar and I'm curious about a couple of things.
One of the questions our users have asked us often is how to minimize the dependence on "product specific" components/nodes/steps. For example, if you write CI for GitHub Actions you may use a bunch of GitHub Action references.
Looking at the `graph.yml` in some of the examples you shared you use a similar approach (e.g. patterns/openai-completion@v4). That means that whenever you depend on such components your automation/data pipeline becomes more tied to the specific tool (GitHub Actions/Patterns), effectively locking in users.
How are you helping users feel comfortable with that problem (I don't want to invest in something that's not portable)? It's something we've struggled with ourselves as we're expanding the "out of the box" capabilities you get.
Furthermore, would have loved to see this as an open source project. But I guess the second best thing to open source is some open source contributions and `dcp` and `common-model` look quite interesting!
For those who are curious, I'm one of the authors of https://github.com/orchest/orchest
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Argo became a graduated CNCF project
Haven't tried it. In its favor, Argo is vendor neutral and is really easy to set up in a local k8s environment like docker for desktop or minikube. If you already use k8s for configuration, service discovery, secret management, etc, it's dead simple to set up and use (avoiding configuration having to learn a whole new workflow configuration language in addition to k8s). The big downside is that it doesn't have a visual DAG editor (although that might be a positive for engineers having to fix workflows written by non-programmers), but the relatively bare-metal nature of Argo means that it's fairly easy to use it as an underlying engine for a more opinionated or lower-code framework (orchest is a notable one out now).
- Ideas for infrastructure and tooling to use for frequent model retraining?
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Looking for a mentor in MLOps. I am a lead developer.
If you’d like to try something for you data workflows that’s vendor agnostic (k8s based) and open source you can check out our project: https://github.com/orchest/orchest
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Is there a good way to trigger data pipelines by event instead of cron?
You can find it here: https://github.com/orchest/orchest Convenience install script: https://github.com/orchest/orchest#installation
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How do you deal with parallelising parts of an ML pipeline especially on Python?
We automatically provide container level parallelism in Orchest: https://github.com/orchest/orchest
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Launch HN: Sematic (YC S22) – Open-source framework to build ML pipelines faster
For people in this thread interested in what this tool is an alternative to: Airflow, Luigi, Kubeflow, Kedro, Flyte, Metaflow, Sagemaker Pipelines, GCP Vertex Workbench, Azure Data Factory, Azure ML, Dagster, DVC, ClearML, Prefect, Pachyderm, and Orchest.
Disclaimer: author of Orchest https://github.com/orchest/orchest
What are some alternatives?
faust - Python Stream Processing. A Faust fork
docker-airflow - Docker Apache Airflow
kuzu - Embeddable property graph database management system built for query speed and scalability. Implements Cypher.
hookdeck-cli - Manage your Hookdeck workspaces, connections, transformations, filters, and more with the Hookdeck CLI
Apache AGE - Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
serverless-graphql - Serverless GraphQL Examples for AWS AppSync and Apollo
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
cugraph - cuGraph - RAPIDS Graph Analytics Library
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
demo-news-recommendation - Exploring News Recommendation With Neo4j GDS
Node RED - Low-code programming for event-driven applications