Data Modeling | Dagster Glossary

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Data Modeling

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:

feature_1,feature_2,target
1,2,10
2,4,20
3,6,30
4,8,40
5,10,50

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()
model.fit(X_train, 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:
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Append

Adding or attaching new records or data items to the end of an existing dataset, database table, file, or list.
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Archive

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

Add new data or information to an existing dataset to enhance its value.
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Auto-materialize

The automatic execution of computations and the persistence of their results.
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Backup

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

Process large volumes of data all at once in a single operation or batch.
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Cache

Store expensive computation results so they can be reused, not recomputed.
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Categorize

Organizing and classifying data into different categories, groups, or segments.
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Checkpointing

Saving the state of a process at certain points so that it can be restarted from that point in case of failure.
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Deduplicate

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

Deserialization is essentially the reverse process of serialization. See: 'Serialize'.
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Dimensionality

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

The bundling of data with the methods that operate on that data.
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Enrich

Enhance data with additional information from external sources.
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Export

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

A powerful tool to model and understand intricate relationships within our data systems.
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Idempotent

An operation that produces the same result each time it is performed.
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Index

Create an optimized data structure for fast search and retrieval.
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Integrate

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

Understand how data moves through a pipeline, including its origin, transformations, dependencies, and ultimate consumption.
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Linearizability

Ensure that each individual operation on a distributed system appear to occur instantaneously.
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Materialize

Executing a computation and persisting the results into storage.
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Memoize

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

Combine data from multiple datasets into a single dataset.
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Monitor

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

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

Interpret and convert data from one format to another.
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Partition

Data partitioning is a technique that data engineers and ML engineers use to divide data into smaller subsets for improved performance.
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Prep

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

Transform raw data before data analysis or machine learning modeling.
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Replicate

Create a copy of data for redundancy or distributed processing.

Scaling

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

Automatically identify the structure of a dataset.
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Schema Mapping

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

Improve the efficiency of data retrieval in a database or storage system.
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Software-defined Asset

A declarative design pattern that represents a data asset through code.
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Synchronize

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

Check data for completeness, accuracy, and consistency.
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Version

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