Table of Contents
What is Data Version Control (DVC)?
It is an open-source data versioning tool that users use for data reproducibility and several machine-learning experiments. With DVC, users need not manually track datasets and the different data models for particular results.
DVC users have the benefit where reconstruction of past data models is not needed to achieve similar results. It eliminates the time and effort to track each trained data model and the different data sets involved. DVC can work through extensive metrics and data sets to maintain distinguished reports rotationally.
DVC comprises tool bundles used to process the different changes in a data version and all past datasets. The tool eliminates the tedious task of digging through old files. Furthermore, its repositories are under the system’s direct influence, allowing it to track and categorize every change, such as additions or deletions.
The History of DVC
DVC came into creation and got released in 2017 as a command line tool. It has only recently published the latest update of 1.11.2. DVC has a vast community backing it, which mainly includes ML engineers, developers, and data scientists, who help guide discussions and decisions. Thousands of users have opted for DVC, and the system has also amassed over 150 contributors.
It was an entire year before DVC got released with stabilized file formats and data commands. An updated version is under work which can change the way machine learning operations take place.
DVC Vs. Pachyderm – What’s the Difference
When choosing a tool for all data needs, it is necessary to look at the different factors that influence the functioning of each.
DVC and Pachyderm are unique data versioning tools that can help streamline data processes to enhance machine learning experiments. They both have features that provide data reproducibility and scaling in different programming languages, which makes it easier to configure them for various needs.
DVC is an open-source tool compared to Pachyderm, each with pros and cons. One can outweigh the other depending on what is needed by data engineers. They are both suitable for achieving the required data versioning results. However, they may also fall short in areas where the infrastructure may not support the data requirements.
6 Best Practices for Data Versioning
Here are a few practices companies can follow to ensure the best implementation of data versioning so that users can achieve desired results irrespective of the tools used.
- It is necessary to treat all reference data available as regular code. Users must store them in the source control systems and their schemas.
- Storing every change made in a database and inside the reference data, it will be possible to create scripts highlighting all modifications. Users must showcase all changes made and reflected in a singular script.
- It is necessary to make all script files immutable once deployed to the production environment.
- It is beneficial to allow each member of the engineers’ team to have their instance in a database to avoid any collisions in modifications.
- Any change users make to the schema or reference data must be done by creating scripts, not manually.
- All versioned data should be stored in the relevant databases and named according to different settings for better tracking.
4 Real-Time Use cases of DVC
- Tracking and training various machine learning models.
- Understanding the process of data version and machine learning artifacts.
- Comparison of different machine learning model metrics.
- Adoption of the most suitable data science methods.
5 Main Features of DVC
DVC is a comprehensive tool that can benefit data science and machine learning in numerous ways. It has different features that help achieve the best results when integrated.
Storage agnostic:
DVC can be integrated with several cloud-based storage systems, making data flow more efficient and easily accessible.
Reproducible:
Consistent and efficient data reproducibility makes it convenient to configure and run machine learning experiments.
Metric tracking:
DVC has a command line for tracking all datasets, which helps save time and effort when needing access to specific data points.
Language agnostic:
DVC is compatible with various programming languages in use, which makes it easier for companies to integrate DVC into their data functions.
Track failures:
DVC has historical tracking, so it maintains the history of past attempts. With this data, users can influence future attempts to achieve more successful machine learning experiments.
6 Main Pros of DVC
The advantages of DVC when integrated with different tools and how it can help boost machine learning experiments.
Cloud sharing of models:
The centralized data storage makes it easier for team members to try various experiments using only a single machine. It makes for better source utilization and convenient management of a development server for all the data storage.
Visualization and tracking of ML models:
DVC has data science features that get versioned in its repositories. It allows for better tracking of all data models and future versioning. To achieve it, users need to take actions such as pull requests through Git workflows. DVC also uses an in-built cache to store each machine learning artifact, which users can sync to cloud storage.
Reproducibility:
Creating DVC data registries can be helpful when using machine learning models in cross-project experiments. They work as management systems that help boost reproducibility and reusability. The repositories can store the history of all data artifacts, thus keeping track of when and what changes users made. Furthermore, it allows users to organize all points with a single command line.
Better organization of ML data:
For machine learning engineers, data is crucial, so organizing and training data models become highly necessary. DVC uses data pipelines with the help of Git to version all data. These are lightweight and enable the organization and reproduction of each workflow – it provides for better data version control, and encourages automation and reproducibility for machine learning.
Improved pace of data science:
DVC has several modernized features that can improve the pace of growth for data science. Some mentionable ones include metafile versioning, metric tracking, centralized data sharing, and lightweight pipelines. The use of these extensive features encourages higher levels of innovation in the field of machine learning.
Compliance and auditing benefits:
DVC makes it easier to assist internal and external audit compliances by storing all data from specific timelines. By versioning data, there is higher convenience and time available to meet all regulations put down for data protection.
6 Cons of DVC
The following are the challenges users might face with DVC when trying to integrate it for various use cases.
Redundancy:
Using separate pipelines may lead to redundancy as users can integrate DVC with its management.
Risk of incorrect configuration:
There is a risk of the wrong configuration in the data pipelines used in DVC. One cannot assume that a versioned project created a year ago will work similarly under the same circumstances. It is also difficult to find missing dependencies as data faults aren’t easily visible in the instance of an error.
Chance of poor performance:
If there is no proper definition of datasets or metrics, it will be difficult for teams to achieve the best results from DVC. Instead, they may be required to develop the needed features manually to meet the different machine learning demands.
Not suitable for big data:
The DVC architecture may not be highly appropriate to meet the machine learning requirements of big data. It can lead to redundancy, slower workflows, and errors that may slow down machine learning teams.
Git integration may not always be beneficial:
DVC works along with Git, but its integration may not help all use cases. Configuring DVC in such instances will be challenging as it may not serve its best benefits.
Single failure points:
If an error occurs at a single data point, it prevents access or any new changes to versioned data.
What is Pachyderm?
Pachyderm is a cost-efficient tool that allows data engineers to automate data pipeline processes with the help of sophisticated transformations of data.
List of Real-Time Pachyderm Use Cases
Users can use Pachyderm in different industries to meet numerous data needs:
- MLOps
- Unstructured data
- Data warehouse
- Bio and life sciences
- Financial industry operations
- Natural language processing (NLP)
5 Key Features of Pachyderm
The different characteristics of the tool that can influence data processes and functions in machine learning experiments.
Cost-effective scaling:
Pachyderm works with optimizing all resources, which enables efficiency in development. It has deduplication of data features which can help save infrastructure costs.
Automation of data pipelines:
Pachyderm has a feature that triggers data pipelines when users make changes to the data. It is also possible to orchestrate real-time data pipelines with the help of various data sources.
Data processing and duplication:
Pachyderm deduplicates all versioned data as it processes only modified sets and dependencies.
Autoscaled parallel processing:
It helps autoscale jobs depending on current resource demand and works on processing large data sets.
Composability:
Pachyderm makes sharing data sets between teams for various use cases easier. It boosts collaboration and the reusability of microservices.
6 Main Benefits of Pachyderm
Listed below are the advantages of using Pachyderm for different data science needs and results:
Scaling in native Python:
Many data versioning tools miss the factor of allowing scalability in Python. Pachyderm fills this gap in the industry which can further help reduce overheads when utilizing Spark or Dask.
Infrastructure agnostic:
One of the best benefits of Pachyderm is that it can run well with other cloud providers or on-premises data spaces. It integrates with tools such as CI/CD and standard machine learning and data processing systems.
Cost-effective:
Pachyderm is highly reliable in optimizing resource utilization, which can further help reduce the complexity involved with data pipelines and minimize infrastructure spending.
Flexible:
Pachyderm provides flexibility and can run with different cloud-based data services. It can run and scale batch or real-time data from various sources.
Reproducibility:
Pachyderm ensures reproducibility of data which is vital for improving the efficiency of teams and boosting collaboration.
Immutable data lineage:
Pachyderm versions all data and pipeline codes and provides an immutable history of data activities. It makes tracking a specific data point easier without digging through heavy loads, making it highly effective when building an immutable database.
6 Cons of Pachyderm
Listed below are the different challenges Pachyderm users may face when integrating it for data versioning:
Difficult learning curve:
While Pachyderm may be beneficial for data versioning, it isn’t easy to learn all the features and processes, which can slow down work. Learning will be time-consuming, and engineers may not have it in hand to focus entirely.
Chances of weak performance:
The events of debugging and pipeline failures can prove to be challenging and may need extra tools to fix any issues. Without it, there are chances that engineers may not be able to make the most of all the other benefits that Pachyderm can provide.
Difficult application:
While Pachyderm has comprehensive features, it is also challenging to apply as it may not match all use cases. Some uses may lead to pipeline crashes, hindering data flows and disrupting machine learning experiments.
Hard to diagnose error points:
It may be challenging to figure out exact error points within pipelines that cause complete data transformations to stop functioning. It can interrupt workflow for teams and come in the way of achieving necessary results.
Can produce high overheads:
Pachyderm might produce high overheads when teams need to process small data. It can slow down the process as this may make it unsuitable for such use.
Automation can take time:
Due to a steep learning need and tricky application, the automation features from Pachyderm can take time to be implemented or may run into challenges to ensure that it provides the most efficient results.
Endnote
DVC and Pachyderm are two highly beneficial data versioning tools in the market. They come with extensive features and capabilities that can shape the way machine learning experiments run in the future. For data science engineers, data versioning can be an advantageous task that these tools can aid. DVC and Pachyderm can provide solutions for all machine learning and data control needs by analyzing benefits and challenges.
