Data Merging | Dagster Glossary

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Data Merging

Combine data from multiple datasets into a single dataset.

Data merging definition:

Data merging is the process of combining two or more datasets into a single dataset. It is a critical step in modern data pipelines when working with data from multiple sources or with different formats that need to be merged for analysis.

Data normalization example using Python:

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

In Python, one of the most popular libraries for merging data are Pandas and Polars. The Pandas library provides several functions for merging data, including merge() , concat() , and join() .

The merge() function

The merge() function allows you to merge two data frames on one or more common columns. For example, let's say we have two data frames:

import pandas as pd

df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'value': [1, 2, 3, 4]})
df2 = pd.DataFrame({'key': ['B', 'D', 'E', 'F'], 'value': [5, 6, 7, 8]})

We can merge these two data frames on the 'key' column like this:

merged_df = pd.merge(df1, df2, on='key', how='inner')
print(merged_df)

For an output of:

  key  value_x  value_y
0   B        2        5
1   D        4        6

The concat() function

The concat() function allows you to concatenate two or more data frames vertically or horizontally. For example, let's say we have two data frames:

df1 = pd.DataFrame({'col1': ['A', 'B', 'C', 'D'], 'col2': [1, 2, 3, 4]})
df2 = pd.DataFrame({'col1': ['E', 'F', 'G', 'H'], 'col2': [5, 6, 7, 8]})

We can concatenate these two data frames horizontally like this:

concatenated_df = pd.concat([df1, df2], axis=1)
print(concatenated_df)

This will yield an output of:

  key  value key  value
0   A      1   B      5
1   B      2   D      6
2   C      3   E      7
3   D      4   F      8

The join() function

The join() function is similar to merge() , but it joins two data frames on their index rather than on a column. For example, let's say we have two data frames:

df1 = pd.DataFrame({'value1': [1, 2, 3, 4]}, index=['A', 'B', 'C', 'D'])
df2 = pd.DataFrame({'value2': [5, 6, 7, 8]}, index=['B', 'D', 'E', 'F'])

We can join these two data frames like this:

joined_df = df1.join(df2, how='inner')
print(joined_df)

In this example, we join df1 and df2 on their index and keep only the rows that are present in both data frames (specified by how='inner' ).

This will yield an output of:

   value1  value2
B       2       5
D       4       6

Data merge vs. data join vs. data matching

It's worth disambiguating a few terms here as these refer to different ways of combining datasets:

  • Data merging combines datasets by simply appending columns from different datasets
  • Data join combines datasets based on a common key or column.
  • Data matching combines records or data elements from different datasets based on their similarity or matching criteria.

Data merge vs. data join

As opposed to data merging discussed above, data join is a specific type of data merging that involves combining two or more datasets based on a common column or key. In a data join, the resulting dataset will only include rows where the key matches in both datasets. This means that any rows that do not have a matching key in both datasets will not be included in the final output. Data join is often used in relational databases, where tables can be joined based on a common key.

Data join is more selective in its output than data merging. Data join only includes rows where the key matches in both datasets, while data merging includes all rows from both datasets.

Data merge vs. data match

Data matching involves comparing two or more datasets to identify and extract common records or data elements. The goal of data matching is to find matching records or data elements in different datasets and combine them into a single, unified record or dataset. Data matching can be done using a variety of methods, including fuzzy matching, exact matching, and probabilistic matching.

While data merging is a relatively straightforward process that involves identifying a common variable or column and combining datasets based on that variable, data matching can be a more complex process that involves comparing multiple datasets and identifying common records or data elements based on complex algorithms and rules.


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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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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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 of 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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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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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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