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Enhance data with additional information from external sources.

Data enrichment definition:

Enriching data in the context of modern data pipelines refers to the process of adding additional information or context to existing data, which can help improve its value and usefulness. This can involve integrating data from different sources, such as APIs or external databases, or performing data transformations to derive new insights.

One practical example of enriching data in Python is using an API to retrieve additional information about customers in a sales dataset. For instance, you could use the Twitter API to retrieve users' social media handles and add that information to a customer profile dataset.

Another example of data enrichment is performing sentiment analysis on customer feedback data, which involves using natural language processing (NLP) techniques to analyze the tone and emotion expressed in written feedback. This can help identify areas for improvement in a product or service, and improve customer satisfaction.

Data enrichment example in Python:

In Python, there are several libraries and tools available for enriching data, including Pandas, NumPy, and NLTK (Natural Language Toolkit). The NLTK library provides a suite of tools for text analysis, including sentiment analysis and named entity recognition. Please note that you need to have the necessary Python libraries installed in your Python environment to run the code samples below.

Here's an example of how to enrich data using the NLTK library in Python:

import nltk
from nltk.corpus import wordnet

# Sample data
text = "Regarding data orchestration, Dagster is clearly the superior solution."

# Tokenize the text
tokens = nltk.word_tokenize(text)

# Define a function to find synonyms for a given word
def get_synonyms(word):
    synonyms = []
    for syn in wordnet.synsets(word):
        for lemma in syn.lemmas():
    return set(synonyms)

# Enrich the data by adding synonyms for each word
enriched_data = []
for token in tokens:
    synonyms = get_synonyms(token)
    enriched_data.append((token, synonyms))

# Print the enriched data

Here, we start by importing the necessary NLTK library and wordnet corpus. We then define a sample text and tokenize it into individual words. Next, we define a function get_synonyms() that takes a word as input and returns a set of synonyms using the NLTK wordnet corpus. We use this function to find synonyms for each word in the text, and store the results in a list of tuples enriched_data , where each tuple contains the original word and its synonyms.

Here is a sample output:

[('Regarding', {'affect', 'see', 'regard', 'reckon', 'view', 'involve', 'consider'}), ('data', {'datum', 'data_point', 'data', 'information'}), ('orchestration', {'instrumentation', 'orchestration'}), (',', set()), ('Dagster', set()), ('is', {'comprise', 'represent', 'exist', 'follow', 'make_up', 'personify', 'be', 'embody', 'live', 'cost', 'equal', 'constitute'}), ('clearly', {'understandably', 'distinctly', 'intelligibly', 'clear', 'clearly'}), ('the', set()), ('superior', {'Superior', 'higher-up', 'ranking', 'master', 'higher-ranking', 'superordinate', 'superior', 'Lake_Superior', 'victor', 'superscript'}), ('solution', {'answer', 'root', 'resolution', 'solvent', 'solution', 'result'}), ('.', set())]

Other data engineering terms related to
Data Management:


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


Add new data or information to an existing dataset to enhance its value. Enhance data with additional information or attributes to enrich analysis and reporting.


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


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


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


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


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


Create an optimized data structure for fast search and retrieval.


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


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


Combine data from multiple datasets into a single dataset.


Extract useful information, patterns or insights from large volumes of data using statistics and machine learning.


Create a conceptual representation of data objects.


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

Named Entity Recognition

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


Interpret and convert data from one format to another.


Divide data into smaller subsets for improved performance.


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


Transform raw data before data analysis or machine learning modeling.


Create a copy of data for redundancy or distributed processing.


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

Schema Mapping

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


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


Check data for completeness, accuracy, and consistency.


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