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Create a conceptual representation of data objects.

Data modeling definition:

Data modeling is the process of creating a conceptual representation of data objects, their relationships, and the rules that govern them. In the context of modern data pipelines, data modeling is crucial to organizing and structuring data in a way that enables efficient data processing, analysis, and visualization.

There are various data modeling techniques such as Entity-Relationship (ER) modeling, UML modeling, and Data Flow Diagrams (DFDs). In addition, modern data pipelines rely heavily on the use of machine learning algorithms for predictive modeling and pattern recognition.

Data modeling example using Python:

Please note that you need to have the necessary Python libraries installed in your Python environment to run the following code examples.

In Python, there are several libraries and frameworks for data modeling, including:

  • Scikit-learn: a popular machine-learning library that includes a wide range of tools for classification, regression, and clustering.
  • TensorFlow: an open-source machine learning framework developed by Google that is widely used for building and training deep neural networks.
  • PyTorch: another popular open-source machine learning framework that is known for its ease of use and flexibility.
  • Pandas: a data manipulation library that provides data structures and functions for working with structured data.

Here is an example of using scikit-learn for data modeling:

Given an input file data.csv as follows:


The code:

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import pandas as pd

# load data
data = pd.read_csv('data.csv')

# split data into training and testing sets
train_data, test_data = train_test_split(data, test_size=0.2)

# prepare training data
X_train = train_data.drop('target', axis=1)
y_train = train_data['target']

# create and train the model
model = LinearRegression(), y_train)

# prepare testing data
X_test = test_data.drop('target', axis=1)
y_test = test_data['target']

# make predictions on test data
predictions = model.predict(X_test)

# calculate mean squared error
mse = mean_squared_error(y_test, predictions)
print('Mean squared error:', mse)

Will yield an output of:

Mean squared error: 1.262177448353619e-29

In this example, we are using the scikit-learn library to create a simple linear regression model. We load our data from a CSV file, split it into training and testing sets, and prepare the training and testing data. We then create and train the linear regression model on the training data and use it to make predictions on the testing data. Finally, we calculate the mean squared error to evaluate the performance of the model.

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.


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.


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