delta VS dvc

Compare delta vs dvc and see what are their differences.


An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs (by delta-io)
InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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delta dvc
69 113
7,163 13,311
3.7% 1.5%
9.9 9.6
5 days ago 8 days ago
Scala Python
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.


Posts with mentions or reviews of delta. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-19.
  • Delta Lake vs. Parquet: A Comparison
    2 projects | | 19 Jan 2024
    Delta is pretty great, let's you do upserts into tables in DataBricks much easier than without it.

    I think the website is here:

  • Understanding Parquet, Iceberg and Data Lakehouses
    4 projects | | 29 Dec 2023
    I often hear references to Apache Iceberg and Delta Lake as if they’re two peas in the Open Table Formats pod. Yet…

    Here’s the Apache Iceberg table format specification:

    As they like to say in patent law, anyone “skilled in the art” of database systems could use this to build and query Iceberg tables without too much difficulty.

    This is nominally the Delta Lake equivalent:

    I defy anyone to even scope out what level of effort would be required to fully implement the current spec, let alone what would be involved in keeping up to date as this beast evolves.

    Frankly, the Delta Lake spec reads like a reverse engineering of whatever implementation tradeoffs Databricks is making as they race to build out a lakehouse for every Fortune 1000 company burned by Hadoop (which is to say, most of them).

    My point is that I’ve yet to be convinced that buying into Delta Lake is actually buying into an open ecosystem. Would appreciate any reassurance on this front!

  • Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog
    4 projects | | 18 Dec 2023
    Apache Iceberg is one of the three types of lakehouse, the other two are Apache Hudi and Delta Lake.
  • [D] Is there other better data format for LLM to generate structured data?
    1 project | /r/MachineLearning | 10 Dec 2023
    The Apache Spark / Databricks community prefers Apache parquet or Linux Fundation's over json.
  • Delta vs Iceberg: make love not war
    1 project | /r/MicrosoftFabric | 30 Jun 2023
    Delta 3.0 extends an olive branch.
  • Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML
    4 projects | | 26 Jun 2023
    Databricks provides Jupyter lab like notebooks for analysis and ETL pipelines using spark through pyspark, sparkql or scala. I think R is supported as well but it doesn't interop as well with their newer features as well as python and SQL do. It interfaces with cloud storage backend like S3 and offers some improvements to the parquet format of data querying that allows for updating, ordering and merged through . They integrate pretty seamlessly to other data visualisation tooling if you want to use it for that but their built in graphs are fine for most cases. They also have ML on rails type through menus and models if I recall but I typically don't use it for that. I've typically used it for ETL or ELT type workflows for data that's too big or isn't stored in a database.
  • The "Big Three's" Data Storage Offerings
    2 projects | /r/dataengineering | 15 Jun 2023
    Structured, Semi-structured and Unstructured can be stored in one single format, a lakehouse storage format like Delta, Iceberg or Hudi (assuming those don't require low-latency SLAs like subsecond).
  • Ideas/Suggestions around setting up a data pipeline from scratch
    3 projects | /r/dataengineering | 9 Jun 2023
    As the data source, what I have is a gRPC stream. I get data in protobuf encoded format from it. This is a fixed part in the overall system, there is no other way to extract the data. We plan to ingest this data in delta lake, but before we do that there are a few problems.
  • Medallion/lakehouse architecture data modelling
    1 project | /r/dataengineering | 3 Jun 2023
    Take a look at Delta Lake, it enables a lot of database-like actions on files
  • CSV or Parquet File Format
    3 projects | /r/Python | 1 Jun 2023
    I prefer parquet (or delta for larger datasets. CSV for very small datasets, or the ones that will be later used/edited in Excel or Googke sheets.


Posts with mentions or reviews of dvc. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-06-17.
  • Essential Deep Learning Checklist: Best Practices Unveiled
    20 projects | | 17 Jun 2024
    Tool: Consider using Data Version Control (DVC) to manage your datasets, models, and their respective versions. DVC integrates with Git, allowing you to handle large data files and model binaries without cluttering your repository. It also makes it easy to version your training datasets and models, ensuring you can always match a model back to its exact training environment.
  • 10 Open Source Tools for Building MLOps Pipelines
    9 projects | | 6 Jun 2024
    As Git helps you with code versions and the ability to roll back to previous versions for code repositories, DVC has built-in support for tracking your data and model. This helps machine learning teams reproduce the experiments run by your fellows and facilitates collaboration. DVC is based on the principles of Git and is easy to learn since the commands are similar to those of Git. Other benefits of using DVC include:
  • A step-by-step guide to building an MLOps pipeline
    7 projects | | 4 Jun 2024
    The meta-data and model artifacts from experiment tracking can contain large amounts of data, such as the training model files, data files, metrics and logs, visualizations, configuration files, checkpoints, etc. In cases where the experiment tool doesn't support data storage, an alternative option is to track the training and validation data versions per experiment. They use remote data storage systems such as S3 buckets, MINIO, Google Cloud Storage, etc., or data versioning tools like data version control (DVC) or Git LFS (Large File Storage) to version and persist the data. These options facilitate collaboration but have artifact-model traceability, storage costs, and data privacy implications.
  • AI Strategy Guide: How to Scale AI Across Your Business
    4 projects | | 11 May 2024
    Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities.
  • My Favorite DevTools to Build AI/ML Applications!
    9 projects | | 23 Apr 2024
    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.
  • Why bad scientific code beats code following "best practices"
    3 projects | | 6 Jan 2024
    What you’re describing sounds like DVC (at a higher-ish—80%-solution level).

    See pachyderm too.

  • First 15 Open Source Advent projects
    16 projects | | 15 Dec 2023
    10. DVC by Iterative | Github | tutorial
  • Exploring Open-Source Alternatives to Landing AI for Robust MLOps
    18 projects | | 13 Dec 2023
    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
    4 projects | | 2 Nov 2023
  • Git Version Controlled Datasets in S3
    1 project | | 25 Oct 2023
    I was using DVC ( 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.

What are some alternatives?

When comparing delta and dvc you can also consider the following projects:

Apache Cassandra - Mirror of Apache Cassandra

MLflow - Open source platform for the machine learning lifecycle

lakeFS - lakeFS - Data version control for your data lake | Git for data

hudi - Upserts, Deletes And Incremental Processing on Big Data.

Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. [Moved to:]

delta-rs - A native Rust library for Delta Lake, with bindings into Python

ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️

iceberg - Apache Iceberg

aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

Apache Avro - Apache Avro is a data serialization system.

git-submodules - Git Submodule alternative with equivalent features, but easier to use and maintain.

InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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