Data Denoising | Dagster Glossary

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Data Denoising

Remove noise or artifacts from data to improve its accuracy and quality.

Data denoising - a definition:

In the context of data pipelines, denoising refers to the process of removing noise from the data, which could be caused by various factors such as measurement errors, faulty sensors, or even human error during data entry. The goal is to extract useful information from the data and remove any unwanted signals that could interfere with the analysis.

Data denoising in Python:

Here are some practical examples of denoising techniques in Python:

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

Moving Average Filter: This is a simple denoising technique that involves taking the average of a sliding window of data points to smooth out the noise. It can be implemented using the rolling function in pandas or convolve function in numpy.

import numpy as np

def moving_average_filter(data, window_size):
    window = np.ones(window_size)/float(window_size)
    convolved_data = np.convolve(data, window, 'same')
    print("Original data: {}".format(data))
    print("Moving average filtered data with window size {}: {}".format(window_size, convolved_data))
    return convolved_data

# example usage
data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
window_size = 4
filtered_data = moving_average_filter(data, window_size)

This example will put yield the following output:

Original data: [ 1  2  3  4  5  6  7  8  9 10]
Moving average filtered data with window size 4: [0.75 1.5  2.5  3.5  4.5  5.5  6.5  7.5  8.5  6.75]

Median Filter: This technique involves replacing each data point with the median of its neighboring points. It can be implemented using the signal.medfilt function in scipy.

from scipy import signal

def median_filter(data, window_size):
    filtered_data = signal.medfilt(data, kernel_size=window_size)
    print("Original data: {}".format(data))
    print("Median filtered data with window size {}: {}".format(window_size, filtered_data))
    return filtered_data

# example usage
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
window_size = 5
filtered_data = median_filter(data, window_size)

Which will output:

Original data: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Median filtered data with window size 5: [1 2 3 4 5 6 7 8 8 8]

Try changing the window_size value to 4 for an interesting result.

Since we are applying a median filter to an input signal using the medfilt() function from the scipy.signal module. The medfilt() function applies a median filter to the input signal using a sliding window with a specified size, and returns the filtered signal.

When the window size is even, such as when window_size = 4, the function raises an error because the medfilt() function requires an odd number for the window size. This is because the median filter operates by taking the median value of the data points within the window, and when the window size is even, the median value is not well-defined.

Wavelet Transform: This is a more advanced denoising technique that involves decomposing the signal into different frequency bands using wavelets and then thresholding the coefficients to remove the noise. It can be implemented using the pywt library (which may require installation using pip install PyWavelets.

import numpy as np
import pywt

def wavelet_denoising(data, wavelet='db4', level=1):
    coeffs = pywt.wavedec(data, wavelet, level=level)
    threshold = np.std(coeffs[-level])
    coeffs = [pywt.threshold(c, threshold) for c in coeffs]
    denoised_data = pywt.waverec(coeffs, wavelet)
    print("Original data: {}".format(data))
    print("Denoised data using wavelet {}: {}".format(wavelet, denoised_data))
    return denoised_data

# example usage
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
wavelet = 'db4'
level = 1
denoised_data = wavelet_denoising(data, wavelet, level)

Which will yield the following output:

Original data: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Denoised data using wavelet db4: [0.95707075 2.00990332 2.94189799 3.95561577 5.00005885 5.96165328
 6.97197144 7.97478225 8.97266171 9.96551716]

These are just a few examples of denoising techniques that can be used in data pipelines. The choice of technique depends on the nature of the noise and the requirements of the analysis.


Other data engineering terms related to
Data Transformation:
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Align

Aligning data can mean one of three things: aligning datasets, meeting business rules, or arranging data elements in memory.
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Clean or Cleanse

Remove invalid or inconsistent data values, such as empty fields or outliers.
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Cluster

Group data points based on similarities or patterns to facilitate analysis and modeling.
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Curate

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

Optimize data for faster read access by reducing the number of joins needed to retrieve related data.
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Derive

Extracting, transforming, and generating new data from existing datasets.
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Discretize

Transform continuous data into discrete categories or bins to simplify analysis.
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ETL

Extract, transform, and load data between different systems.
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Encode

Convert categorical variables into numerical representations for ML algorithms.
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Filter

Extract a subset of data based on specific criteria or conditions.
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Fragment

Break data down into smaller chunks for storage and management purposes.
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Homogenize

Make data uniform, consistent, and comparable.
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Impute

Fill in missing data values with estimated or imputed values to facilitate analysis.
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Linearize

Transforming the relationship between variables to make datasets approximately linear.
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Munge

See 'wrangle'.
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Normalize

Standardize data values to facilitate comparison and analysis. Organize data into a consistent format.
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Reduce

Convert a large set of data into a smaller, more manageable form without significant loss of information.
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Reshape

Change the structure of data to better fit specific analysis or modeling requirements.
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Serialize

Convert data into a linear format for efficient storage and processing.
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Shred

Break down large datasets into smaller, more manageable pieces for easier processing and analysis.
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Skew

An imbalance in the distribution or representation of data.
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Split

Divide a dataset into training, validation, and testing sets for machine learning model training.
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Standardize

Transform data to a common unit or format to facilitate comparison and analysis.
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Tokenize

Convert data into tokens or smaller units to simplify analysis or processing.
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Transform

Convert data from one format or structure to another.
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Wrangle

Convert unstructured data into a structured format.
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