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Create a copy of data for redundancy or distributed processing.

Data replication definition:

Data replication in the context of modern data pipelines involves creating multiple copies of data and distributing them across multiple nodes or clusters to ensure high availability and fault tolerance. This is typically done to minimize the risk of data loss due to hardware failure or other issues.

Data replication example using Python:

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

In Python, data replication can be achieved using various distributed computing frameworks such as Apache Hadoop, Apache Spark, or Dask. These frameworks provide mechanisms for replicating data across multiple nodes or clusters.

For example, using Apache Spark, we can create a replicated RDD (Resilient Distributed Dataset) by calling the `repartition()`` function with a desired number of partitions:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("Data Replication Example").getOrCreate()

# Load data from a CSV file
data ="data.csv", header=True, inferSchema=True)

# Replicate the data across two nodes
replicated_data = data.repartition(2)

# Do some processing on the replicated data
result = replicated_data.groupBy("column1").count()

# Save the result to a CSV file
result.write.csv("replicate_result", mode="overwrite", header=True)

In this example, we load data from a CSV file using Spark and then replicate the data across two nodes using the repartition() function. We then perform some processing on the replicated data and save the result to a CSV file in the folder replicate_result. The replicated data ensures high availability and fault tolerance in case of hardware failure or other issues.

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.


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.


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.