Top Tools / December 7, 2021
StartupStash Team

The world's biggest online directory of resources and tools for startups and the most upvoted product on ProductHunt History.

Top 14 Data Versioning Tools

Data versioning is a method of keeping track of code changes so that if something goes wrong, we can compare various code versions and go back to whatever prior version we desire. When numerous developers are constantly working on/changing the source code, data versioning tools are a must. Just like data analysis, data versioning should never be ignore.

In this top tools list, we have compiled the top 14 Data Versioning Tools for you to choose from.


1. LakeFS

LakeFS is an open-source data versioning tool that uses S3 or GCS for storage and provides a Git-like branching and committing paradigm that grows to Petabytes of data.

By enabling changes to happen in discrete branches that can be formed, merged, and rolled back atomically and immediately, this branching approach makes your data lake ACID-compliant.

Key Features:

  • Check for version control on an exabyte scale.

  • Branch, commit, merge, and revert check are Git-like actions.

  • For frictionless trials, use zero copy branching.

  • Data and code checks are fully reproducible.

  • Data CI/CD checks using pre-commit/merge hooks.

  • Revert data changes in real time.

Cost:

This is a free tool.


2. Idera DB Change Manager

Idera DB Change Manager can be used by DBAs and Developers to manage changes, compare schemas, produce software-generated synchronization scripts, and provide configurable reports. It evaluates schema differences across one or more archived or live databases. It also keeps data safe. It also creates modified scripts to sync or restore chosen items to a previous state.

Key Features:

  • Changes to the database can be rolled out and reconciled as soon as possible.

  • Database changes are revealed, tracked, and reported on.

  • Database auditing and reporting guidelines are followed.

  • Within the database context, ensure data privacy.

  • Changes from many main database platforms may be tracked.

Cost:

You can request a quote on their website.


3. DeltaLake

DeltaLake is an open-source storage layer that makes data lakes more reliable. Delta Lake integrates streaming and batch data processing while providing ACID transactions and scalable metadata management. It is completely compatible with Apache Spark APIs and operates on top of your existing data lake.

Key Features:

  • Delta Sharing is the first open protocol for safe data sharing in the business, making it easy to exchange data with other enterprises independent of their computing platforms.

  • Your data lakes will benefit from Delta Lake's ACID transactions. It offers serializability, which is the highest level of isolation.

  • Delta Lake treats information the same way it treats data, relying on Spark's distributed processing capability to manage it all. Delta Lake can now easily manage petabyte-scale tables with billions of partitions and files.

Cost:

This is a free tool.


4. Pachyderm

Pachyderm is a free and comprehensive data science version control system. Pachyderm Enterprise is a feature-rich data science platform built for large-scale cooperation in highly secure contexts.

Key Features:

  • Through commits, branches, and rollbacks, it has a Git-like structure that allows for successful team collaboration.

  • Petabytes of structured and unstructured data may be supported while storage expenses are kept to a minimum thanks to an optimized storage system.

  • File-based versioning offers a comprehensive audit trail for all data and artifacts, including intermediate outputs, throughout pipeline stages.

  • Versioning is automated and assured by storing native objects rather than metadata pointers.

Cost:

You can request a quote on their website.


5. AWS CodeCommit

AWS Code Commit is a managed source control service that hosts private Git repositories and is safe and highly scalable. It enables teams to work on code in a safe manner, with contributions encrypted in transit and at rest. CodeCommit may be used to save anything from code to binaries. It works with your existing Git-based tools since it supports Git's basic capabilities.

Key Features:

  • AWS CodeCommit eliminates the need for your own source control servers to be hosted, maintained, backed up, and scaled. The service automatically grows to match your project's expanding demands.

  • AWS Identity and Access Management (IAM) is integrated with CodeCommit, allowing you to define user-specific access to your repositories.

  • The design of AWS CodeCommit is extremely scalable, redundant, and long-lasting. Your repositories will be extremely available and accessible thanks to the service.

  • Pull requests, branching, and merging are all features of AWS CodeCommit that allow you to work on code with peers.

Cost:

$1 per month.


6. Sqitch

Sqitch is an application for managing database changes. Changes are made using scripts that are local to the database engine you've chosen. It uses a Merkle tree design, akin to Git and Blockchain, to manage modifications and dependencies, and to assure deployment integrity.

Key Features:

  • Sqitch is an effective data versioning tool that can be used for any database engine, application framework, or development environment.

  • Changes are made using scripts that are local to the database engine you've chosen.

  • Modifications to the database might create dependencies on other updates, including changes from other Sqitch projects. Even if you've committed changes to your VCS out-of-order, this maintains appropriate execution order.

  • Sqitch uses a plan file to handle changes and dependencies, and it uses a Merkle tree design to maintain deployment integrity, similar to Git and Blockchain.

Cost:

This is a free tool.


7. Dolt

Dolt is a SQL database that works similarly to a git repository in terms of forking, cloning, branching, merging, pushing, and pulling. Dolt allows data and structure to change in tandem to improve the user experience of a version control database. It's a fantastic tool for you and your team to work on together.

You may connect to Dolt exactly like any other MySQL database and use SQL commands to conduct queries or change the data.

Key Features:

  • Import CSV files, commit your changes, publish them to a remote, and integrate your teammate's modifications using the command line interface.

  • All of the commands you're used to using with Git will function with Dolt as well. Dolt versions tables, Git versions files

  • To execute queries or change data using SQL commands, connect to Dolt as you would any other MySQL database.

Cost:

This is a free tool.


8. Perforce

Perforce offers DevOps solutions that help you gain a competitive edge by addressing quality, security, compliance, collaboration, and speed – all while addressing the whole technology lifecycle. Please contact the support to learn more about how the solutions may assist you in accelerating digital transformation, scaling innovation, and achieving DevOps success.

Key Features:

  • Scripts and procedures can be dealt with more quickly.

  • Deliver files and comments while avoiding costly squabbles.

  • Collaboration with your existing toolbox is frictionless.

  • Every modification is easily tracked, and IPs may be reused.

  • Secure your assets whether they're in the cloud or on-premises.

Cost:

You can request a quote on their website.


9. DBGeni

DBGeni makes it simple to handle database migrations, including applying and reverting changes to transfer your database from one version to the next. DBGeni understands where to locate your migration scripts, how to run them, and what still needs to be applied if you follow a few unwritten rules.

Key Features:

  • With only one command, you can create your database.

  • There is no new syntax or lockin because it uses ordinary SQL files.

  • Stored procedures are supported.

  • Changes may be easily applied to a variety of contexts.

  • If you need it, you can script the command line interface.

Cost:

This is a free tool.


10. Version SQL

Version SQL is a simple version control add-in for SQL Server. It's designed to perform one thing well: commit SQL to source control repositories such as Git and Subversion. By verifying stored procedures, views, table schema, and other objects into your source control repository, you can easily trace changes.

Key Features:

  • Over a secure HTTPS connection, VersionSQL works with any Git or Subversion server on your local network or in the cloud (GitHub, Bitbucket, Azure DevOps, and so on), as well as many more through the CLI.

  • For checking in a complete database, folder, or individual objects, VersionSQL adds contextual instructions to SSMS' Object Explorer panel.

  • The database code is exported to.sql script flat files, sorted into directories, and stored on a version control server.

Cost:

A one-time license costs $149.


11. Git LFS

Git LFS project is open-source. Large files, such as audio samples, films, databases, and images, are replaced with text pointers within Git, and the file contents are stored on a remote server, such as GitHub.com or GitHub Enterprise.

It enables you to use Git to version huge files—even those up to several GB in size—host more in your Git repositories using external storage, and clone and retrieve large file repositories quicker.

Key Features:

  • Git allows you to version big files, even ones that are a few GB in size.

  • More files should be stored in your Git repository. It's simple to keep your repository at a moderate size with external file storage.

  • Reduce the amount of data you download. This means quicker cloning and retrieval of huge files from repositories.

  • You don't need any additional commands, alternative storage systems, or toolsets to work on Git.

Cost:

This is a free tool.


12. Data Version Control

Data Version Control is an open-source data versioning tool specifically for data science and machine learning applications. The tool is created to make machine learning models shared and repeatable by handling big files, data sets, machine learning models, code, and so on.

Key Features:

  • Every ML model's whole evolution may be tracked with full code and data provenance.

  • Harness the full potential of Git branches to trial alternative ideas instead of messy file suffixes and comments in code. Instead of using paper and pencil, use automated metric-tracking to travel.

  • It's simple to compare and select the finest ideas. With intermediate artifact caching, iterations become quicker.

  • Use push/pull commands instead of ad-hoc scripts to transport consistent bundles of machine learning models, data, and code into production, distant machines, or a colleague's workstation.

Cost:

This is a free tool.


13. DB Ghost Change Manager

DB Ghost Change Manager contains tools for scripting out your databases into separate drop/create scripts, storing them under source control, modifying them there, and then building and deploying them. A database can be considered the "source database" if it has been properly scripted and brought under source control.

Key Features:

  • Make use of your source control system to keep track of all of their SQL code.

  • Incorporate SQL code into your recurring build process.

  • All other areas of the software development life cycle will receive SQL code.

  • Make a repeatable and dependable framework.

  • Use visual/manual database comparison and sync tools to save time.

Cost:

A one-time license costs $435.


14. Neptune

Neptune is a machine learning metadata repository designed for research and production teams that execute a lot of experiments. From hyperparameters and metrics to videos, interactive visualizations, and data versions, you can log and show almost any ML metadata. With a single line of code, Neptune artifacts allow you to version datasets, models, and other files from your local drive or any S3-compatible storage.

Key Features:

  • Experiment tracking: In one spot, you can log, display, organize, and compare machine learning experiments.

  • Version, store, manage, and query trained models, as well as model creation metadata, in the model registry.

  • Monitoring Machine Learning (ML) runs in real-time: Record and monitor model training, assessment, and production run in real-time.

Cost:

This is a free tool.


Things To Consider When Choosing A Data Versioning Tool

Collaboration

Data versioning is based on collaboration. When selecting a version control system, the ability to support team communication should be a top priority. You may have observed that cooperation may be enabled in a variety of ways as we covered the many types of version control software. Regardless of the other considerations, we'll go over later, how a version control system adapts to the expertise of your team is a critical topic that will help you properly estimate costs.

Security

Some data versioning tools are highly secure when compared with others. The dispute over distributed vs centralized version control systems is a typical example of this issue. We'll go through this in further detail later. For certain teams, having access control down to the file level, rather than simply the repository or space, is essential. Depending on the version control system, the amount of granularity with which you may regulate these aspects varies.

Data Size And File Type

Some version control systems are better than others at managing huge binary files. If the projects you work on need a lot of binary files (e.g., visual assets and text files), you'll want to be sure your version control system can handle them.


Conclusion

We spoke about the finest data versioning tools in this post. As we've seen, each tool has its own unique set of features. Some of them were free, while others were for a fee. Some are well suited to the small business model, while others are better suited to the large business model.

As a result, you must pick the best software for your needs after evaluating the benefits and drawbacks. Before you buy a premium tool, we recommend that you try out the free trial version first.


FAQs

What Is Data Versioning?

Version control, also known as revision control or source control, is the management of changes to documents, websites, basically any collection of data. It is a component of software configuration management. A number or letter code referred to as the "revision number," "revision level," or simply "revision," is used to identify changes. "Revision level 1" is an example of a starting collection of files.

The resulting set is called "revision level 2" after the subsequent alteration, and so on. Each revision has a timestamp and the name of the person who made the modification. Revisions can be compared, restored, and merged with various file formats.

When Should You Consider Using Data Versioning Tools?

Why do we need Version Control in the first place? I'm working on a job on my local computer/cloud and will deploy it to my server once the model is complete and tested. So, what's the big deal about version control?

Now consider the following scenario: I work at a firm called Botsupply, and I have customers. I'm an AI expert. Using a TF-IDF-based model, I created a question-answering search. It was installed on my server. In the following step, I tweaked it a bit, and my accuracy on dummy data improved. It was installed on the server. The performance is now degraded owing to the complexity of the test data. I'd want to revert to the prior version now.

One option is to re-deploy the prior version. Version control and reverting to a prior version is the second, or superior, method.

How Can You Version Control?

Git is one of the most common methods for version control. It's incredibly popular nowadays, and almost everyone knows how to use it. (At the very least, every programmer and data scientist).

Now, Git is fantastic, but maintaining all of your files synchronized in Git is a difficult effort for a data scientist. All of the superfluous space is taken up by the model checkpoints and data size. So, one option is to keep all of the data on a cloud server such as Amazon S3 and all of the replicable code in Git, and then create the models on the fly. Although it appears to be a sensible decision, using different data sets in the same code can cause confusion and, if not adequately documented, can lead to data set mixing in the long term.

In addition, if data changes/upgrades and all contributions are not adequately documented, the model may lose context.

How Does Data Versioning Help Workflows?

Data scientists usually spend weeks or months on time-consuming testing to assure project correctness. They are in charge of determining a model to train with which dataset. This process is influenced by DVC in the following ways:

If models are not finished during training, comparing them might be expensive. DVC can aid in the management of ML pipeline complexity, allowing you to train the same model repeatedly.

It's tough for teams to remember or maintain track of which model was trained with which sort of data when they're training a huge number of models. DVC assists teams in maintaining version files and referencing ML models and their outcomes.

Because data is viewed by a large number of people in machine learning, it can cause confusion among team members if datasets are not correctly labeled using standard protocols. DVC, on the other hand, makes correct labeling easier, allowing for more experimentation.

What Are The Advantages Of Data Versioning?

Managing, storing, and reusing models and algorithms is a major difficulty in deep learning studies. Some advantages of DVC for data scientists are outlined below to reduce the complexity of these challenges:

  1. Models may be shared via cloud storage.

Teams find it simpler to conduct experiments utilizing a shared single computer after centralizing data storage, which leads to improved resource use. DVC enables groups to maintain a development server for the use of shared data.

In this example, the servers might be any sort of cloud server (Microsoft Azure, Amazon S3, Google SSH, etc.). We can do the same for our data models in DVC as we do for our code since it begins rapid switching and workspace restoration for all users to exchange models over the cloud.

  1. Track and Visualize Machine Learning Models

In DVC, data science features are versioned and stored in data repositories. Regular Git procedures, such as pull requests, are used to achieve versioning. DVC employs a built-in cache to store all ML artifacts, which is then synced with distant cloud storage. DVC enables the recording of data and models for future versioning in this fashion. Writing a dvc.yaml file is a basic step in creating artifacts by tracking ML models.

  1. Reproducibility

DVC data registries might be useful for applying ML models in cross-project studies. These work in a similar way to a package management system in terms of increasing reproducibility and reusability. DVC repositories can employ no-code pulls to update requests with a single commit and maintain the history for all artifacts, including what was modified and when. With DVC get and DVC import commits, users may recreate and arrange feature stores using a simple command-line interface.

Top 14 Data Versioning Tools
StartupStash Team

The world's biggest online directory of resources and tools for startups and the most upvoted product on ProductHunt History.