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Maintain a history of changes to data for auditing and tracking purposes.

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.

Other data engineering terms related to
Data Management:


Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. store data for long-term retention and compliance.


Add new data or information to an existing dataset to enhance its value. Enhance data with additional information or attributes to enrich analysis and reporting.


Create a copy of data to protect against loss or corruption.


Select, organize and annotate data to make it more useful for analysis and modeling.


Identify and remove duplicate records or entries to improve data quality.


Analyzing the number of features or attributes in the data to improve performance.


Enhance data with additional information from external sources.


Extract data from a system for use in another system or application.


Create an optimized data structure for fast search and retrieval.


combine data from different sources to create a unified view for analysis or reporting.


Store the results of expensive function calls and reusing them when the same inputs occur again.


Combine data from multiple datasets into a single dataset.


Extract useful information, patterns or insights from large volumes of data using statistics and machine learning.


Create a conceptual representation of data objects.


Track data processing metrics and system health to ensure high availability and performance.

Named Entity Recognition

Locate and classify named entities in text into pre-defined categories.


Interpret and convert data from one format to another.


Divide data into smaller subsets for improved performance.


Transform your data so it is fit-for-purpose.


Transform raw data before data analysis or machine learning modeling.


Create a copy of data for redundancy or distributed processing.


Increasing the capacity or performance of a system to handle more data or traffic.

Schema Mapping

Translate data from one schema or structure to another to facilitate data integration.


Ensure that data in different systems or databases are in sync and up-to-date.


Check data for completeness, accuracy, and consistency.