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PySparkSnowflakeDatabricks

Must-Know Databricks Interview Questions & Answers (Real Company Scenarios) – Part 2

16. What is Delta Lake? Why is it used?​

Story-Driven​

Imagine your data is a magical diary where entries can be added, updated, or even undone safely. Delta Lake is like that diary—it keeps your data organized, safe, and fast to read, even when multiple people are writing at the same time.

Professional / Hands-On​

Delta Lake is an open-source storage layer on top of cloud storage (S3, ADLS, etc.) that provides:

  • ACID transactions for reliability.
  • Schema enforcement to maintain consistent data.
  • Time travel to access historical data versions.
  • Used to make Spark jobs more reliable, maintainable, and performant.

17. Explain ACID transactions in Delta Lake​

Story-Driven​

Think of ACID transactions like a safety net: if something goes wrong while writing data, everything rolls back so you don’t end up with half-baked results. It ensures your data is Accurate and Reliable.

Professional / Hands-On​

ACID stands for:

  • Atomicity: Operations are all-or-nothing.
  • Consistency: Data always remains valid.
  • Isolation: Concurrent writes do not interfere.
  • Durability: Once committed, data is never lost. Delta Lake guarantees ACID compliance using transaction logs.

18. How do you handle schema evolution in Delta Lake?​

Story-Driven​

Your data sometimes grows new columns, like adding extra pages to a diary. Schema evolution allows Delta Lake to automatically accept those new columns without breaking old data.

Professional / Hands-On​

# Enable schema evolution while writing
df.write.format("delta").mode("append").option("mergeSchema", "true").save("/delta/employees")
  • New columns are automatically added to the Delta table.
  • Prevents job failures when the schema changes.

19. What are MERGE, UPDATE, DELETE operations in Delta Lake?​

Story-Driven​

Delta Lake is like a smart notebook where you can:

  • MERGE: Update or insert new entries in one go.
  • UPDATE: Correct existing entries.
  • DELETE: Remove unwanted entries.

Professional / Hands-On​

  • MERGE: Combines insert/update operations based on a condition.
MERGE INTO target_table t
USING source_table s
ON t.id = s.id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *
  • UPDATE: Change existing data.
  • DELETE: Remove rows based on a condition.

20. What is Databricks Autoloader?​

Story-Driven​

Imagine a magical conveyor belt that automatically picks up new files and loads them into your notebook. That’s Autoloader—it keeps your data pipeline fresh without manual intervention.

Professional / Hands-On​

Databricks Autoloader:

  • Incrementally loads new files from cloud storage.
  • Handles schema inference and evolution.
  • Supports streaming ingestion for large datasets.
(spark.readStream.format("cloudFiles")
  .option("cloudFiles.format", "csv")
  .load("/mnt/data"))

21. Types of Databricks clusters (Interactive vs Job)​

Story-Driven​

  • Interactive clusters are like a playground—you explore, experiment, and play with data.
  • Job clusters are like scheduled workers—they automatically run tasks on a schedule and go away once the job is done.

Professional / Hands-On​

  • Interactive clusters: Used for development, debugging, and notebooks.
  • Job clusters: Created for production jobs, automatically terminated after job completion.

22. What is caching in Databricks, and how is it done?​

Story-Driven​

Caching is like putting your most-used cookbook on the counter for quick access instead of going to the shelf every time. It makes data retrieval faster.

Professional / Hands-On​

  • Caching: Keeps frequently accessed data in memory.
df.cache()          # Cache the DataFrame
df.unpersist()      # Remove from cache when done
  • Improves query performance by reducing disk I/O.

23. Explain partitioning in Databricks and its benefits​

Story-Driven​

Partitioning is like organizing a library by categories. Instead of scanning all books every time, you only check the relevant shelf. Queries run faster, and storage is more organized.

Professional / Hands-On​

  • Partitioning: Divides large datasets into smaller chunks based on column values.

  • Benefits:

    • Faster queries via partition pruning.
    • Efficient storage and I/O.
    • Better parallel processing.
df.write.partitionBy("year", "month").format("delta").save("/delta/sales")

24. How do you optimize performance in Spark jobs on Databricks?​

Story-Driven​

Optimizing Spark jobs is like making your kitchen more efficient: pre-chop veggies, organize utensils, and assign tasks smartly. In Databricks, similar techniques make data processing faster and cheaper.

Professional / Hands-On​

  • Use Delta tables for ACID and performance.
  • Partition and cache frequently used data.
  • Optimize joins with broadcast joins.
  • Use Databricks Runtime optimizations.
  • Avoid unnecessary shuffles and expensive transformations.

25. What is Databricks Runtime?​

Story-Driven​

Databricks Runtime is like the engine of a sports car—it makes everything go faster and smoother. It comes with Spark and all the tools pre-installed for data engineering, analytics, and ML.

Professional / Hands-On​

  • Databricks Runtime (DBR): Pre-configured Spark environment optimized for Databricks.

  • Includes:

    • Spark, Delta Lake, ML libraries.
    • Performance improvements and security fixes.

  • Versions: Standard, ML, GPU-enabled, or Light versions depending on workload.