Delta Lake vs Parquet: Preventing Data Loss During Write Operations

Traditional data storage methods, such as plain Parquet files, are susceptible to partial failures during write operations. This can result in incomplete data files and a lack of clear recovery options in the event of a system crash.

Delta Lake’s write operation with ACID transactions helps solve this by:

• Ensuring either all data is written successfully or none of it is
• Maintaining a transaction log that tracks all changes
• Providing time travel capabilities to recover from failures

Here’s an example showing Delta Lake’s reliable write operation:

from deltalake import write_deltalake, DeltaTable
import pandas as pd

initial_data = pd.DataFrame({
    "id": [1, 2],
    "value": ["a", "b"]
})

write_deltalake("customers", initial_data)

If the append operation fails halfway, Delta Lake’s transaction log ensures that the table remains in its last valid state.

try:
    # Simulate a large append that fails halfway
    new_data = pd.DataFrame({
        "id": range(3, 1003),  # 1000 new rows
        "value": ["error"] * 1000
    })

    # Simulate system crash during append
    raise Exception("System crash during append!")
    write_deltalake("customers", new_data, mode="append")

except Exception as e:
    print(f"Write failed: {e}")

    # Check table state - still contains only initial data
    dt = DeltaTable("customers")
    print("\nTable state after failed append:")
    print(dt.to_pandas())

    # Verify version history
    print(f"\nCurrent version: {dt.version()}")

Output:

Write failed: System crash during append!

Table state after failed append:
   id value
0   1     a
1   2     b

Current version: 0

Link to Delta Lake.