Handling Large Data - Data Chunking

In our previous article, we delved into data distribution using PySpark to effectively manage extensive datasets. Another method for handling large data sets is through Data Chunking.

Key Points:

- Memory Efficiency: Reading files in chunks prevents loading the entire dataset into memory simultaneously, a critical aspect for handling very large files.

- Flexibility: Each chunk allows for various operations like filtering, aggregation, or transformation to be performed independently.

- Progress Tracking: Monitoring processing progress is simplified through message printing or progress bars.

Additional Considerations:

- Chunk Size: Optimal chunk size varies based on available memory and task complexity. Experimenting with different sizes helps determine the best performance.

- Combining Results: When operations on the complete dataset are necessary, results from each chunk can be merged using methods like pd.concat().

- Dask: For highly parallel processing of extensive datasets, consider utilizing the Dask library, which enhances pandas by offering efficient distributed computing capabilities.

Program:

Laymen Understanding:

Data Chunking generally works in the principle of "GENERATOR" in python. It "YIELDS" each chunk of data instead of reading all at once.

Important Point to Remember:

Data Chunking is not serialization.