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Based on our record, Luigi should be more popular than ArangoDB. It has been mentiond 9 times since March 2021. 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.
ArangoDB is a multi-model database that supports document, key-value, and graph data models with a unified query language. - Source: dev.to / about 11 hours ago
In modern databases, efficient data serialization and deserialization are paramount to achieving high performance. ArangoDB, a multi-model database, addresses this need with its innovative binary data format, VelocyPack. This article delves into the intricacies of VelocyPack, demonstrating its advantages, usage, and how it enhances the performance of ArangoDB with code examples in Java and Rust. - Source: dev.to / 27 days ago
ArangoDB: A native multi-model database, it offers flexibility for documents, graphs, and key-values. This versatility makes it suitable for applications requiring a combination of these data models. - Source: dev.to / 4 months ago
ArangoDB, a "multi-modal" database engine that stores arbitrary JSON documents like MongoDB, key/value data like Redis, and graph relationships like Neo4j — and lets you leverage all three kinds of data in a single query. Source: over 1 year ago
I have a bash script and a part of it is installation of arangodb.com. I want a script to install Arangodb in such a way a user won't have select any options during installation, not even specify a password. I want it to have all the default settings, default password such as 1234, even an empty password will do, default config... Everything by default and without any questions to a user. Source: over 3 years ago
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... - Source: Hacker News / 9 months ago
Maybe if your use case is “smallish” and doesn’t require the whole studio suite you could check out apscheduler for doing python “tasks” on a schedule and luigi to build pipelines. Source: almost 2 years ago
What are you trying to do? Distributed scheduler with a single instance? No database? Are you sure you don't just mean "a scheduler" ala Luigi? https://github.com/spotify/luigi. - Source: Hacker News / about 2 years ago
It's good to know what Airflow is not the only one on the market. There are Dagster and Spotify Luigi and others. But they have different pros and cons, be sure that you did a good investigation on the market to choose the best suitable tool for your tasks. - Source: dev.to / over 2 years ago
MLOps is a HUGE area to explore, and not surprisingly, there are many startups showing up in this space. If you want to get it on the latest trends, then I would look at workflow orchestration frameworks such as Metaflow (started off at Netflix, is now spinning off into its own enterprise business, https://metaflow.org/), Kubeflow (used at Google, https://www.kubeflow.org/), Airflow (used at Airbnb,... Source: over 2 years ago
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
Kestra.io - Infinitely scalable, event-driven, language-agnostic orchestration and scheduling platform to manage millions of workflows declaratively in code.
neo4j - Meet Neo4j: The graph database platform powering today's mission-critical enterprise applications, including artificial intelligence, fraud detection and recommendations.
Dagster - The cloud-native open source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability.