differences between Parquet and Delta Lake (Delta) in the context of data storage and processing in systems like Databricks:
is a set of properties that ensure reliable processing of database transactions. It stands for:
A – Atomicity
-
"All or nothing"
-
A transaction is treated as a single unit, which either completes entirely or does not happen at all.
-
Example: If you're buying products from an Ecommerce domain and while making payment to vendor from one account to another, the debit and credit must both succeed. If one fails, the whole transaction is rolled back.
-
Ensures that a transaction brings the database from one valid state to another.
-
All rules (constraints, cascades, etc.) are maintained before and after the transaction.
-
Prevents invalid data or corruption.
-
I – Isolation
Ensures that concurrent transactions do not interfere with each other.
Each transaction should appear as if it's the only one running, even when others are executing at the same time.
Prevents issues like dirty reads, non-repeatable reads, or phantom reads (depending on isolation level).
D – Durability
Once a transaction is committed, the changes are permanent, even in the event of a system crash or power failure.
Typically achieved through write-ahead logs, checkpoints, or journaling.
Conclusion:
·
Use Parquet when you need a fast,
compact, read-optimized format without transactional support.
· Use Delta when you need reliability, auditability, and flexibility (like schema evolution, versioning, upserts, deletes).
