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

Data integration definition:

Data integration refers to the process of combining data from multiple sources into a single, unified dataset. This is an important step in many data analysis and machine learning projects, as data is often stored in different formats and locations.

Integration involves several steps, including data cleaning, transformation, and normalization to ensure that the data from different sources can be combined. This can involve resolving naming conflicts, converting data types, and removing duplicate data.

Data integration example using Python and PySpark:

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

Python provides several libraries and tools for integrating data, such as pandas, NumPy, and Apache Spark. These tools can help with data cleaning, transformation, and normalization, as well as providing a range of functions and algorithms for analyzing and visualizing the integrated dataset.

For example, let's say you are working on a project to analyze customer behavior for an e-commerce site. You might need to integrate data from several sources, including website logs, customer transaction data, and social media activity. Using Python, you can use tools such as pandas and NumPy to clean and transform the data, and then integrate it into a single dataset for analysis.

Here's an example of data integration using Apache Spark:

Let's say we have two data sources, customers and orders, and we want to integrate them based on the customer_id field. We can load both data sources into Spark DataFrames, and then join them on the customer_id field:

from pyspark.sql import SparkSession

# Create a SparkSession
spark = SparkSession.builder.appName("DataIntegration").getOrCreate()

# Load the customers data into a DataFrame
customers_df ="customers.csv", header=True, inferSchema=True)

# Load the orders data into a DataFrame
orders_df ="orders.csv", header=True, inferSchema=True)

# Join the two DataFrames on the customer_id field
joined_df = customers_df.join(orders_df, "customer_id")

# Show the resulting integrated data

In this example, customers.csv might look like this:


And orders.csv might look like this:


The resulting joined_df DataFrame would look like this:

|customer_id|  name|            email|order_id|  product|
|          1| Alice||   1001|   Widget|
|          1| Alice||   1002|   Gadget|
|          2|   Bob||   1003|   Widget|
|          3|Charlie||   1004|Thingy|

This integrated DataFrame combines the data from both sources into a single dataframe based on the customer_id field. We are now set up to perform further analysis or transformations on this integrated data using Apache Spark.

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.


Enhance data with additional information from external sources.


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


Create an optimized data structure for fast search and retrieval.


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.