Advantages of Using Delta Tables Over CSV for Machine Learning Models - ivinnyaraujo/dataengineer-datascience-python GitHub Wiki

• Better Performance: Delta tables are optimised for fast reads and writes, which makes them more efficient for handling large datasets compared to CSV files that can be slow and memory-heavy.
• Scalability: Delta tables are designed to handle large-scale data and can easily scale with distributed systems like Apache Spark, making them a better choice for big data workloads.
• Data Integrity: Delta tables support ACID transactions, ensuring that data is consistent and reliable, unlike CSV files which can become corrupted when modified by multiple users at once.
• Schema Flexibility: Delta tables allow schema evolution, meaning you can safely add or change columns without breaking your workflow. In contrast, updating a CSV file can be cumbersome and error-prone.
• Version Control: Delta supports time travel, so you can easily access previous versions of your data. This is not possible with CSV files, which lack versioning capabilities.
• Data Quality: Delta tables support data validation and automatic deduplication during writes, reducing the need for manual data cleaning that often comes with working with CSV files.

In short, Delta tables provide improved performance, better scalability, and greater data integrity than CSV files, making them a more practical and robust choice for machine learning projects.