Data Shredding | Dagster Glossary

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Data Shredding

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

Data shredding definition:

Data shredding, also known as data partitioning or data fragmentation, is the process of breaking down large datasets into smaller, more manageable pieces for easier processing and analysis. Shredding data can improve performance by allowing distributed processing of the smaller pieces, which can be performed in parallel, reducing the amount of time required to process the entire dataset. This approach is particularly useful for large datasets that cannot be processed on a single machine or within a single process.

As a side note, data shredding may, in a different context, refer to the process of securely erasing or destroying sensitive data that is no longer needed.

Data shredding example using Python:

In Python, data shredding can be achieved using various libraries, such as PySpark or Dask, which allow for distributed computing across multiple nodes or machines. For example, PySpark's repartition() function can be used to repartition a DataFrame into a specified number of partitions, enabling more efficient processing of large datasets. Please note that you need to have the necessary Python libraries installed in your Python environment to run this code.

Here's an example:

from pyspark.sql import SparkSession

# create a SparkSession object
spark = SparkSession.builder.appName("DataShreddingExample").getOrCreate()

# read in a large dataset
df = spark.read.format("csv").option("header", "true").load("large_dataset.csv")

# repartition the data into 10 partitions
df = df.repartition(10)

# perform some analysis on the data
result = df.groupBy("Col A","Col B").count().show()

# stop the SparkSession
spark.stop()

In this example, we start by creating a SparkSession object, then reading in a large dataset. We then repartition the data into 10 partitions using the repartition() function, which creates 10 smaller, more manageable pieces of the data. We can then perform some analysis on the data, such as grouping by a column and counting the occurrences, before stopping the SparkSession.

For example, if the input file large_dataset.csv contained two columns of 1000 random integers between 0 and 10, your results might look like this:

|Col A|Col B|count|
+-----+-----+-----+
|    9|    4|   11|
|   10|    5|    2|
|    9|    5|    8|
|    8|    2|    8|
|    7|    1|   11|
|    4|    1|   11|
|    4|    2|   12|
|    0|    6|    6|
|    5|    4|    8|
|    1|    4|   14|
|    0|    4|    5|
|    3|    8|   11|
|    4|    6|    8|
|    5|    1|   12|
|    5|    5|    8|
|    2|    8|   13|
|    1|    1|   13|
|    0|    1|    6|
|   10|    0|    2|
|    0|    5|    4|
+-----+-----+-----+
only showing top 20 rows

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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Denoise

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