Batch Data Ingestion

Batch data ingestion is the process of ingesting a large amount of data in a scheduled manner. It involves extracting data from various sources, transforming it if necessary, and then loading it into the target system in batches.

One commonly used tool for batch data ingestion is Python. Python provides several libraries and frameworks, such as Pandas and SQLAlchemy, that make it easy to perform batch data ingestion.

Here's an example of how to perform batch data ingestion using Python and Pandas:

1{{code}}

In this example, we first import the pandas library to work with data frames. Then, we use the read_csv function to load data from a CSV file named data.csv. Next, we iterate over each row in the data frame using a for loop and perform any necessary data processing. Finally, we use the to_sql function from the SQLAlchemy library to load the processed data into a target table in a database.

Batch data ingestion is suitable for scenarios where data updates are not time-sensitive and can be processed in batches. It is commonly used for periodic data updates, such as daily or weekly data feeds.

xxxxxxxxxx

import pandas as pd

​

# Load data from CSV file

data = pd.read_csv('data.csv')

​

# Perform batch data ingestion

# Python logic here

for i in range(len(data)):

    # Process data

    processed_data = process_data(data[i])

    # Load data into target system

data.to_sql('target_table', con=engine, if_exists='append', index=False)