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PySparkSnowflakeDatabricks

Must-Know Databricks Interview Questions & Answers (Real Company Scenarios) – Part 1

1. What is Databricks, and how is it different from Apache Spark?​

Story-Driven Explanation​

Imagine you have a huge library of books and you want to quickly find insights from them. Apache Spark is like the powerful search engine in the library—it can process large data efficiently. Databricks is like a smart librarian who not only knows how to use Spark but also gives you a beautiful workspace, dashboards, and collaboration tools so your whole team can work together easily. Spark is the engine; Databricks is the platform that makes the engine usable at scale.

Professional / Hands-On Explanation​

Databricks is a unified data analytics platform built on top of Apache Spark. It provides:

  • A collaborative workspace for data scientists and engineers.
  • Managed clusters to run Spark jobs without manual setup.
  • Integration with cloud storage, data visualization, and ML tools.

Difference: Apache Spark is just the processing engine. Databricks adds management, collaboration, security, and scaling features on top of Spark.

2. What is a Databricks workspace?​

Story-Driven​

Think of a Databricks workspace like your personal office in the cloud. It’s where you can organize your projects, notebooks, data, and dashboards in one place, just like your desk, drawers, and bulletin board all combined.

Professional / Hands-On​

A Databricks workspace is a collaborative environment where you can:

  • Create notebooks.
  • Organize folders, libraries, and dashboards.
  • Manage users, permissions, and workflows. It’s the entry point for all Databricks operations.

3. What is a Databricks cluster?​

Story-Driven​

Imagine needing several chefs to prepare a huge feast quickly. A Databricks cluster is like your team of chefs. Each chef works in parallel, chopping, cooking, and serving simultaneously. The bigger the feast, the more chefs (nodes) you can add.

Professional / Hands-On​

A cluster in Databricks is a set of virtual machines configured to run Spark jobs. Clusters can be:

  • Interactive: For ad-hoc queries and development.
  • Job clusters: For scheduled jobs. You can scale clusters up or down based on workload.

4. How do you create a cluster in Databricks?​

Story-Driven​

Creating a cluster is like hiring your team of chefs. You decide how many you need, what tools they have, and when they start working. Databricks does the rest automatically.

Professional / Hands-On​

  1. Go to Clusters in the Databricks workspace.
  2. Click Create Cluster.
  3. Choose:
    • Cluster name
    • Spark version
    • Node type and size
    • Number of workers (min/max)
  4. Click Create Cluster.
    The cluster spins up and is ready to run notebooks or jobs.

5. What are Databricks notebooks?​

Story-Driven​

Think of a notebook as your magical journal. You can write code, add charts, jot down notes, and share it with your team. Everything runs live, and you can see the results instantly.

Professional / Hands-On​

A Databricks notebook is an interactive document for writing and executing:

  • Python, SQL, Scala, or R code.
  • Markdown for documentation.
  • Visualizations inline with code results.

Notebooks are used for data exploration, analysis, and ETL pipelines.

6. Explain Databricks jobs and how they differ from notebooks.​

Story-Driven​

If a notebook is like cooking a recipe in your kitchen, a job is scheduling the recipe to automatically cook every day at 8 AM. You don’t have to manually start it; Databricks does it for you.

Professional / Hands-On​

Databricks Jobs:

  • Automate notebook execution or other workflows.
  • Support scheduling (daily, hourly) or triggers. Difference: Notebooks are interactive; jobs are automated and production-oriented.

7. What is DBFS (Databricks File System)?​

Story-Driven​

DBFS is like your cloud backpack. You can store files, datasets, and libraries there, and access them anywhere in Databricks, just like opening your backpack and finding your tools instantly.

Professional / Hands-On​

Databricks File System (DBFS):

  • Layer over cloud storage (S3, ADLS, etc.).
  • Accessible via paths like /dbfs/FileStore/....
  • Supports reading/writing files from notebooks or jobs.

8. How do you read and write data in Databricks?​

Story-Driven​

Reading and writing data in Databricks is like opening a notebook and writing your notes, then saving them in your backpack (DBFS) for later use. You can also share them with others easily.

Professional / Hands-On​

# Read data
df = spark.read.csv("/path/to/data.csv", header=True, inferSchema=True)

# Write data
df.write.format("parquet").save("/path/to/output.parquet")

Databricks supports formats like CSV, Parquet, Delta, JSON, and more.

9. What languages are supported in Databricks notebooks?​

Story-Driven​

Think of Databricks as a multilingual friend. You can talk to it in Python, SQL, Scala, or R, and it will understand and process your requests flawlessly.

Professional / Hands-On​

Databricks notebooks support:

  • Python
  • SQL
  • Scala
  • R Use magic commands (%python, %sql, %scala, %r) to switch between languages in the same notebook.

10. What is a Databricks table?​

Story-Driven​

A table is like a spreadsheet inside your Databricks library. You can store, query, and share it, and it keeps your data organized neatly in rows and columns.

Professional / Hands-On​

A Databricks table is a structured dataset stored in DBFS or external storage. Tables can be queried using SQL or Spark APIs.

11. How do you create a table in Databricks?​

Story-Driven​

Creating a table is like creating a new spreadsheet. You decide the columns and types, then start filling in data.

Professional / Hands-On​

-- SQL example
CREATE TABLE employees (
    id INT,
    name STRING,
    salary DOUBLE
);

Or, create from a DataFrame in Python:

df.write.saveAsTable("employees")

12. Difference between managed and unmanaged tables?​

Story-Driven​

A managed table is like a filing cabinet Databricks fully manages—you just use it. An unmanaged table is like a personal folder—you manage the files yourself.

Professional / Hands-On​

  • Managed table: Databricks owns the data. Dropping the table deletes both metadata and data.
  • Unmanaged table: Only metadata is managed by Databricks. Dropping the table keeps the data in storage.

13. Databricks notebooks’ cell commands (%sql, %python)​

Story-Driven​

Magic commands are like telling Databricks which language you want to speak. %python for Python, %sql for SQL, and so on.

Professional / Hands-On​

  • %python – Execute Python code.
  • %sql – Execute SQL queries.
  • %scala – Execute Scala code.
  • %r – Execute R code.

Example:

%sql
SELECT * FROM employees

14. What is a Delta table?​

Story-Driven​

A Delta table is like a supercharged notebook. It keeps track of changes, lets you undo mistakes, and makes your data safe and fast to read.

Professional / Hands-On​

Delta Lake tables are an enhanced version of Parquet tables:

  • ACID transactions
  • Time travel
  • Schema enforcement
  • Optimized reads/writes
df.write.format("delta").save("/delta/employees")