dvc
Flyway
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dvc | Flyway | |
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109 | 81 | |
13,116 | 7,775 | |
1.4% | 1.0% | |
9.7 | 7.2 | |
4 days ago | 7 days ago | |
Python | Java | |
Apache License 2.0 | 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.
dvc
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My Favorite DevTools to Build AI/ML Applications!
Collaboration and version control are crucial in AI/ML development projects due to the iterative nature of model development and the need for reproducibility. GitHub is the leading platform for source code management, allowing teams to collaborate on code, track issues, and manage project milestones. DVC (Data Version Control) complements Git by handling large data files, data sets, and machine learning models that Git can't manage effectively, enabling version control for the data and model files used in AI projects.
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Why bad scientific code beats code following "best practices"
What you’re describing sounds like DVC (at a higher-ish—80%-solution level).
https://dvc.org/
See pachyderm too.
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First 15 Open Source Advent projects
10. DVC by Iterative | Github | tutorial
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
- ML Experiments Management with Git
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Git Version Controlled Datasets in S3
I was using DVC (https://dvc.org/) for some time to help solve this but it was getting hard to manage the storage connections and I would run into cache issues a lot, but this solves it using git-lfs itself.
- Ask HN: How do your ML teams version datasets and models?
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Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
DVC (Data Version Control):
- Evaluate and Track Your LLM Experiments: Introducing TruLens for LLMs
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[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
Flyway
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Let's write a simple microservice in Clojure
The session logs show that the application loads configurations and establishes a connection with a PostgreSQL database. This involves initializing a HikariCP connection pool and Flyway for database migrations. The logs confirm that the database schema validation and migration checks were successful. The startup of the Jetty HTTP server follows, and the server becomes operational and ready to accept requests on the specified port.
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Ask HN: What tool(s) do you use to code review and deploy SQL scripts?
Also RedGate, but Flyway has some reasons to recommend it over RedGate Deploy depending on your DBAs/workflows: https://flywaydb.org/
(Though I don't think it is "complete" or "perfect", either.)
EF Migrations are in a really good place now if you like/don't mind C# as a language (and you can easily embed SQL inside the C#, too, but there are benefits to being able to also run high level C# code). With today's tooling you can package your migration "runner application" as a single deployable executable for most platforms. You can build the executable once and run it in all your environments. (The same tool that updates your QA and Staging updates your Prod, testably running the same migrations.) Given the single executable deployable I might even consider using it for projects not themselves written in C#.
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PostgreSQL Is Enough
There is a bit of tooling needed but is already around. For Java for example I had very good experience with a combination of flyway [1] for migrations, testcontainers [2] for making integration tests as easy as unit tests and querydsl [3] for a query and mapping layer.
[1] https://github.com/flyway/flyway
[2] https://java.testcontainers.org/modules/databases/postgres/
[3] https://github.com/querydsl/querydsl
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Using Flyway to version your database
When software starts using a database, it's advisable to have version control, just as we have Github to control our source code. This is all to be sure about what was executed for that specific version. For Java and Spring boot, we have the Flyway framework that aims to resolve this situation, free of charge.
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CI/CD for Databricks
If you're looking for tools, like https://www.liquibase.com/ or https://flywaydb.org/, which are database-state-based schema migration toolkits - it might be relatively straightforward to build similar ones using Databricks SQL drivers.
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Working with jOOQ and Flyway using Testcontainers
Honestly I kind of wish there was a Lukas Eder database migration library. Call it whatever jooq-migration. At least I would have more insight of what is going on (<-- seriously look at the commit history).
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Strategy to run database scripts on Kubernetes
This is a 4th option, which should play nice with ArgoCD. The following example runs flyway as a k8s job. The desired migration changes are recorded as files within the chart. This helm chart can be integrated with your application (Using hooks to determine when the migration job is run) or run manually.
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How do your teams run DB migrations?
By using an opinionated framework within the app/service (like Flyway, Migrate, Diesel, etc). Schema migrations happen on app/service start-up.
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I've never created a production database from scratch and am wondering how much trouble it would be to transition a one-to-one relationship to a one-to-many relationship if I determine at some point that the latter is required.
Depending on the language or platform there are libraries you can use to manage this, such as Prisma on node and Flyway for Java/JVM.
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How should I document and/or automate schema changes?
It's probably overkill but I've used github plus flyway at a couple places in the past which is pretty nice tool for tracking changes to a variety of db's, it's also very helpful if you ever need to replicate a db in a new region/environment.
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
alembic - A database migrations tool for SQLAlchemy.
lakeFS - lakeFS - Data version control for your data lake | Git for data
HikariCP - 光 HikariCP・A solid, high-performance, JDBC connection pool at last.
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
roundhouse - RoundhousE is a Database Migration Utility for .NET using sql files and versioning based on source control
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
H2 - H2 is an embeddable RDBMS written in Java.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
dbmate - :rocket: A lightweight, framework-agnostic database migration tool.
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
Hibernate - Hibernate's core Object/Relational Mapping functionality