Neo4j
Airflow
Neo4j | Airflow | |
---|---|---|
50 | 169 | |
12,486 | 34,570 | |
1.1% | 1.4% | |
9.9 | 10.0 | |
12 days ago | 3 days ago | |
Java | Python | |
GNU General Public License v3.0 only | 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.
Neo4j
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System Design: Databases and DBMS
Neo4j
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How to choose the right type of database
Neo4j: An ACID-compliant graph database with a high-performance distributed architecture. Ideal for complex relationship and pattern analysis in domains like social networks.
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Looks Like the Free Software Foundation Forced Neo4j's Hand
After spending millions fighting the committer of ONgDB who removed the commons clause from the AGPL branded license, it looks like the Free Software Foundation got involved and forced them to remove the commons clause or change the license to their own proprietary license.
https://github.com/neo4j/neo4j/commit/b6237ca4e31706b1efbd0f...
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Getting Started with GenAI Stack powered with Docker, LangChain, Neo4j and Ollama
The GenAI Stack came about through a collaboration between Docker, Neo4j, LangChain, and Ollama. The goal of the collaboration was to create a pre-built GenAI stack of best-in-class technologies that are well integrated, come with sample applications, and make it easy for developers to get up and running. The goal of the collaboration was to create a pre-built GenAI stack of best-in-class technologies that are well integrated, come with sample applications, and make it easy for developers to get up and running.
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Database Review: Top Five Missing Features from Database APIs
Neo4j (GraphQL)
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How to Choose the Right Document-Oriented NoSQL Database for Your Application
NoSQL is a term that we have become very familiar with in recent times and it is used to describe a set of databases that don't make use of SQL when writing & composing queries. There are loads of different types of NoSQL databases ranging from key-value databases like the Reddis to document-oriented databases like MongoDB and Firestore to graph databases like Neo4J to multi-paradigm databases like FaunaDB and Cassandra.
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Loading data
this thread on this github issue could be useful.
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[For Hire] Senior Developer with 14 years experience. Canadian expat in a low cost of living country | From 500 EUR per project/month
Recently I have taken an interest in big data. https://neo4j.com/ , https://cassandra.apache.org/ , https://clickhouse.com/, https://www.elastic.co/ - are all databases I have experience with. Neo4j and Cassandra only as a hobby, but Clickhouse I have used in production, and Elasticsearch I have used for some 7 years now.
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SQL Versus NoSQL Databases: Which to Use, When, and Why
For organizations and their applications that are designed to detect fraud, like International Consortium of Investigative Journalists, or try to improve customer experience via personalization, as in the case of Tourism Media, a NoSQL graph database like Neo4j is a good match. In these kinds of use cases, the quantity of data we're dealing with is enormous, and the pattern we're searching for in the data is often complex.
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Graph Databases vs Relational Databases: What and why?
First, you need to choose a specific graph database platform to work with, such as Neo4j, OrientDB, JanusGraph, Arangodb or Amazon Neptune. Once you have selected a platform, you can then start working with graph data using the platform's query language.
Airflow
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Building in Public: Leveraging Tublian's AI Copilot for My Open Source Contributions
Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.
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Navigating Week Two: Insights and Experiences from My Tublian Internship Journey
In week Two, I contributed to the Apache Airflow repository.
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Airflow VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Best ETL Tools And Why To Choose
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.
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Simplifying Data Transformation in Redshift: An Approach with DBT and Airflow
Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.
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Share Your favorite python related software!
AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic!
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Ask HN: What is the correct way to deal with pipelines?
I agree there are many options in this space. Two others to consider:
- https://airflow.apache.org/
- https://github.com/spotify/luigi
There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…
- "Você veio protestar para ter acesso ao código fonte da urnas. O que é o código fonte?" "Não sei" 🤡
- Cómo construir tu propia data platform. From zero to hero.
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Is it impossible to contribute to open source as a data engineer?
You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte
What are some alternatives?
Apache AGE - Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL. [Moved to: https://github.com/apache/age]
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
Hasura - Blazing fast, instant realtime GraphQL APIs on your DB with fine grained access control, also trigger webhooks on database events.
dagster - An orchestration platform for the development, production, and observation of data assets.
FlockDB - A distributed, fault-tolerant graph database
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
RedisGraph - A graph database as a Redis module
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
ArangoDB - 🥑 ArangoDB is a native multi-model database with flexible data models for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
janusgraph - JanusGraph: an open-source, distributed graph database
Dask - Parallel computing with task scheduling