Programming Essentials Python - Batch Operations - Batch Loading of Data

In this article, we will explore the significance of loading data in batches and compare multiple approaches to understand which one is more efficient. We will demonstrate how different batch loading strategies can impact the performance when dealing with large datasets.

Key Concepts Explanation

Approach 1

When data is loaded by inserting and committing each record individually, there is a significant overhead due to frequent commit operations in the database.

# Inserting and committing one row in each iteration

Approach 2

Inserting one record at a time but committing all records at the end is a slightly faster approach compared to Approach 1 as it reduces the number of commit operations.

# Inserting one row at a time but committing at the end

Approach 3

Inserting all records in a single batch improves efficiency by transferring all data at once. However, this could lead to memory issues and data loss if the operation fails midway.

Approach 4

Inserting records in manageable chunks or batches and committing per batch ensures better restartability and recoverability, making it the preferred approach for handling large datasets.

Hands-On Tasks

  1. Follow Approach 4 for batch loading of data to ensure optimal performance.
  2. Implement the batch loading strategy with a manageable batch size for improved efficiency.


Batch loading of data is a critical consideration when dealing with large datasets. By following the recommended batch loading approach, users can enhance the performance and reliability of data loading processes, especially in scenarios where data integrity and efficiency are paramount.

Watch the video tutorial here

Watch the video tutorial here