Data Deduplication | Dagster Glossary

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Data Deduplication

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

Data deduplication definition:

The presence of duplicates in your data can lead to incorrect analysis results and cause storage and processing inefficiencies, leading to increased costs.

There are several Python functions and techniques for de-duplicating data in regular programming, such as using hash functions, using set data structure, and comparing columns to identify duplicates. While traditional Python object lists and sets can be used for deduplicating data, they may not be the most efficient choice for large-scale data processing. This is because lists and sets require storing all data in memory, which can quickly become a bottleneck and slow down the processing.

For deduplicating large datasets, it's recommended to use specialized libraries and data structures designed for efficient processing, such as the pandas library in Python. Pandas has built-in functions like drop_duplicates() that can easily handle large datasets. Additionally, distributed processing frameworks like Apache Spark and Dask can be used to process large datasets in a distributed and parallelized manner, which can further improve the performance of data deduplication.

Deduplicating data in Python

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

Python provides several libraries and functions for de-duplicating data, such as:

Pandas: Pandas provides a drop_duplicates() function that can be used to remove duplicate rows from a Pandas DataFrame. Given an input file data.csv with 56 rows of which 10 are duplicates, the following code…

import pandas as pd

df = pd.read_csv('data.csv')
print(f"This dataframe has {len(df)} rows.")
df = df.drop_duplicates()
print(f"This dataframe now has {len(df)} rows.")

… might yield this output:

This dataframe has 56 rows.
This dataframe now has 44 rows.

Dask: Dask is a parallel computing library in Python that can be used for big data processing. Dask provides a drop_duplicates() function that can be used to remove duplicates from a Dask DataFrame.

In the following example, the first line imports the dask.dataframe module as dd.

The second line reads in CSV files using the dd.read_csv() function, which returns a Dask DataFrame. The in the filename parameter data.csv is a wildcard character that matches any file with a name starting with "data" and ending with ".csv". If there are multiple files that match this pattern, Dask will concatenate them into a single dataframe.

import dask.dataframe as dd

df = dd.read_csv('data*.csv')
df = df.drop_duplicates()

The resulting dataframe will be distributed across multiple workers, which can perform computations in parallel.

PySpark: PySpark is the Python API for Apache Spark, a big data processing framework. PySpark provides a dropDuplicates() function that can be used to remove duplicates from a PySpark DataFrame.

Given the input data.csv file:

name,zip,amount
James,12345,99
Bob,19876,23
Claire,212565,124
Bob,19876,23
Claire,212565,123
James,12345,99

We can use PySpark to deduplicate the data:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("De-duplication").getOrCreate()
df = spark.read.csv("data.csv", header=True, inferSchema=True)
df = df.dropDuplicates()

df.show()

The df.show() command will provide us with the output:

+------+------+------+
|  name|   zip|amount|
+------+------+------+
| James| 12345|    99|
|   Bob| 19876|    23|
|Claire|212565|   124|
|Claire|212565|   123|
+------+------+------+

Note that the lines have to be identical across all columns to be considered a duplicate, which is why the last two lines remain in the dataframe.


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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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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Model

Create a conceptual representation of data objects.
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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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