Data Versioning

Data versioning definition:

Versioning of data is a process of tracking and managing changes made to data over time. It is important in modern data pipelines because it enables data engineers to keep track of changes made to the data and revert to a previous version if necessary.

Data versioning example using Python:

Here are some practical examples of data versioning in data engineering using dvc. Please note that you need to have the dvc library installed in your Python environment to run this code:

One way to implement versioning of data in Python is by using Git, a version control system. Data can be stored in a Git repository, with each commit representing a new version of the data. Git provides tools for comparing different versions of the data and merging changes made to different versions.

Another way to implement versioning of data is by using a data version control system (DVCS) like DVC (Data Version Control). DVC allows data scientists and engineers to track and version datasets, models, and experiments. DVC stores data in remote repositories and provides features like tracking data lineage, enabling reproducibility, and sharing data across teams.

Here is an example of how to use DVC for versioning data in Python:

Install DVC using pip:

$ pip install dvc

Initialize a DVC repository- for this example we wil not be using an SCM tool (e.g. Git).:

$ dvc init --no-scm

Add data to the DVC repository:

$ dvc add data.csv

Commit the data to the DVC repository:

$ git add data.csv.dvc
$ git commit -m "Add data.csv"

[main (root-commit) 750be23] Add data.csv
 1 file changed, 4 insertions(+)
 create mode 100644 data.csv.dvc

Make changes to the data and commit them to the DVC repository:

dvc add data.csv
git add data.csv.dvc
git commit -m "Update data.csv"

Realpython provides a complete tutorial on DVC, that you can find here.