HintsToday

Absolutely! Let’s break it down clearly by comparing:

💡 Traditional RDBMS (like Oracle, Postgres, MySQL) vs. Vanilla PySpark (with Parquet/ORC) vs. PySpark with Delta Lake

We’ll explain each feature, its purpose in traditional SQL DBs, and whether it’s supported in Vanilla PySpark, and how Delta Lake fills the gap.

🔁 1. ACID Transactions

What It Means: ACID = Atomicity, Consistency, Isolation, Durability. Ensures data integrity (e.g., all-or-nothing writes, rollback on error).

Why PySpark Can’t Do It: Parquet/ORC are just files on disk (HDFS/S3) – no transaction coordination.

Delta Lake adds a _delta_log that tracks atomic operations.

Use Case: Writing multiple updates – you want either all to succeed or none (e.g., financial transactions, ETL job failure rollback).

🕰️ 2. Time Travel

What It Means: You can query old versions of a table — useful for debugging, audits, rollback.

Delta Lake uses versioned snapshots stored in _delta_log.

-- Delta Lake: Read previous version
SELECT * FROM table VERSION AS OF 5

Use Case: Accidentally deleted data? Just roll back to version 4.

🔀 3. Concurrent Writes

What It Means: Multiple users or jobs can safely update the same table.

Why PySpark Fails: If two Spark jobs write the same file path, one may overwrite the other — no locking mechanism.

Delta uses optimistic concurrency control (OCC) — detects conflicting changes and throws errors.

📐 4. Schema Evolution

What It Means: You can evolve a schema — add/remove columns dynamically.

PySpark Issue: Manual reconciliation of schemas.

# Delta Lake schema evolution
df.write.option("mergeSchema", "true").format("delta").mode("append").save(...)

Use Case: JSON or streaming data where new fields get introduced over time.

🔄 5. MERGE / UPSERT (SCD2 Support)

What It Means: Efficiently update or insert based on conditions (Slowly Changing Dimensions, SCD2).

Vanilla Way:

# Manual Upsert: join → overwrite
existing = spark.read.parquet("path")
updated = new_data.join(existing, "id", "leftanti").union(new_data)
updated.write.mode("overwrite").parquet("path")

Delta Way:

MERGE INTO customers USING updates ON customers.id = updates.id
WHEN MATCHED THEN UPDATE SET ...
WHEN NOT MATCHED THEN INSERT ...

🧽 6. Deletes / Updates

Problem: Want to delete all records where status = 'inactive'.

PySpark:

• Read table
• Filter out records
• Overwrite entire dataset

Delta Lake:

DELETE FROM my_table WHERE status = 'inactive'

📋 7. Change Tracking / Audit Logs

Use Case: Who changed what and when?

🔍 8. Indexing

Problem: No B-Trees or hash indexes like in Oracle.

Delta Lake:

• Supports Z-Ordering — physically clusters data by column to improve query performance.

OPTIMIZE table_name ZORDER BY (user_id)

⚙️ 9. Stored Procedures / Triggers

Spark doesn’t support SQL procedural logic (loops, if/else). You have to do this in PySpark/Python/Scala code.

🔐 10. Access Control

Why This Matters: If you store data on S3, access control happens at bucket or object level — not at row or column level like in databases.

Databricks Unity Catalog:

• Table/Column/Row level access
• RBAC, Data Lineage

✅ Summary Table

📌 Conclusion

• Vanilla PySpark (Parquet, ORC) is not a database. It’s file-based: no transaction log, no versioning, no indexing.
• Delta Lake brings the power of databases (ACID, merge, delete, time travel) to data lakes using Parquet under the hood.
• Databricks with Unity Catalog adds governance, RBAC, lineage — making it a true enterprise Lakehouse.

Excellent — let’s break this down in-depth. We’ll explain key database features like Atomicity (ACID), SCD1/SCD2, and then compare Traditional RDBMS vs. Vanilla PySpark vs. Delta Lake/Databricks for each of them, with examples.

🔥 1. Atomicity (from ACID)

Atomicity = All or Nothing

✅ Traditional RDBMS (e.g., Oracle, Postgres)

• Uses transactions:
  Example: BEGIN; UPDATE accounts SET balance = balance - 500 WHERE id = 1; UPDATE accounts SET balance = balance + 500 WHERE id = 2; COMMIT;
• If any step fails, entire block rolls back.
• Supported by built-in transaction logs + locks.

❌ Vanilla PySpark (Parquet/CSV/ORC)

• Filesystem-based (HDFS, S3): No concept of transaction logs or rollback.
• Example: df1.write.mode('overwrite').parquet("s3://data/step1/") df2.write.mode('overwrite').parquet("s3://data/step2/") # If this fails, step1 still written.
• If one write fails, others remain — not atomic.

✅ Delta Lake (on Databricks or OSS)

• Maintains _delta_log/ directory for transaction logs.
• Atomic actions: from delta.tables import DeltaTable spark.sql("BEGIN") spark.sql(""" MERGE INTO target USING updates ON target.id = updates.id WHEN MATCHED THEN UPDATE SET ... WHEN NOT MATCHED THEN INSERT ... """) spark.sql("COMMIT")
• If any step fails: rollback using Delta transaction logs.

📘 2. Slowly Changing Dimensions (SCD)

SCD1 (overwrite old data with new)

Use Case: Keep only the latest customer address.

✅ Traditional SQL:

UPDATE customers SET address = 'New York' WHERE customer_id = 101;

❌ Vanilla PySpark:

Manual steps:

df_existing = spark.read.parquet("s3://customers/")
df_updates = spark.read.parquet("s3://new_updates/")

df_merged = df_existing.join(df_updates, "customer_id", "left_outer")\
    .withColumn("address", F.coalesce(df_updates.address, df_existing.address))

df_merged.write.mode('overwrite').parquet("s3://customers/")

• No native MERGE ⇒ must read → join → overwrite.

✅ Delta Lake (Databricks):

MERGE INTO customers USING updates
ON customers.customer_id = updates.customer_id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *

OR (Python API):

DeltaTable.forPath(spark, "s3://delta/customers").alias("t") \
    .merge(df_updates.alias("u"), "t.customer_id = u.customer_id") \
    .whenMatchedUpdateAll() \
    .whenNotMatchedInsertAll() \
    .execute()

SCD2 (history tracking)

Use Case: Track every change with start_date, end_date, and is_active.

✅ Traditional SQL:

-- Close old record
UPDATE customer_dim SET end_date = CURRENT_DATE, is_active = 0
WHERE customer_id = 101 AND is_active = 1;

-- Insert new version
INSERT INTO customer_dim (id, address, start_date, is_active)
VALUES (101, 'New York', CURRENT_DATE, 1);

❌ Vanilla PySpark:

Manual logic:

# Read old + new
existing = spark.read.parquet("s3://customer_dim/")
incoming = spark.read.parquet("s3://incoming/")

# Close old
closed = existing.join(incoming, "id")\
    .withColumn("end_date", F.current_date())\
    .withColumn("is_active", F.lit(0))

# Add new rows
new_rows = incoming.withColumn("start_date", F.current_date())\
    .withColumn("end_date", F.lit(None))\
    .withColumn("is_active", F.lit(1))

# Combine and write
final = closed.union(new_rows)
final.write.mode('overwrite').parquet("s3://customer_dim/")

✅ Delta Lake:

from delta.tables import DeltaTable

delta = DeltaTable.forPath(spark, "s3://delta/customer_dim")

# Step 1: Mark old records inactive
delta.alias("target").merge(
    updates.alias("source"),
    "target.id = source.id AND target.is_active = 1"
).whenMatchedUpdate(set={
    "end_date": "current_date()",
    "is_active": "0"
}).execute()

# Step 2: Insert new versions
updates.withColumn("start_date", F.current_date())\
    .withColumn("is_active", F.lit(1))\
    .write.format("delta").mode("append").save("s3://delta/customer_dim")

🔍 Feature-by-Feature Comparison Table

💡 Summary: Key Takeaways

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