Databricks & PySpark Essentials for Data Engineering Interviews

Preparing for a Data Engineer interview can be daunting. You need to know the theory, master the code, and understand how it all fits together to solve real-world problems. This guide is designed to give you a comprehensive understanding of Databricks and PySpark, tailored for data engineering roles at large-scale organizations.

The “What & Why”: Setting the Scene

Before we write any code, let’s understand the tools of the trade and why they are the industry standard for modern data engineering.

What is Apache Spark & PySpark?

Apache Spark is a powerful, open-source distributed computing engine for processing big data. Its key advantage is in-memory processing, making it orders of magnitude faster than older systems like Hadoop MapReduce. PySpark is simply the Python API for Spark, allowing us to command this powerful engine using the friendly and versatile Python language.

What is Databricks?

Databricks is a cloud-based, unified analytics platform built by the creators of Spark. Think of it as “Spark on steroids.” While you *can* set up your own Spark cluster, Databricks simplifies everything:

• Managed Infrastructure: No more complex cluster setup. You click a button, and you have a highly optimized Spark cluster ready to go.
• Collaborative Notebooks: A shared, interactive environment where engineers and analysts can work together.
• Performance Boosts: The Databricks Runtime includes proprietary optimizations (like the Photon engine) that make your Spark jobs run significantly faster.

The “How”: A Day in the Life of a Databricks Notebook

A Databricks Notebook is your canvas. It’s an interactive web-based interface composed of cells, where you can mix code, text, and visualizations.

Key Functions & Notebook Structure

Your workflow will often involve chaining transformations on DataFrames, the core data structure in Spark. Let’s imagine a common data engineering task: creating a clean “Student Dimension” table for a large university.

# In Cell 1: Use a Markdown cell to document your work.
%md
### **ETL Pipeline: Create Dim_Student**
This notebook reads raw student data from the data lake, cleans it, applies transformations, and saves it as a reliable dimension table in Delta Lake format.
---
# In Cell 2: Read raw data using PySpark.
# The path would point to your cloud data lake (e.g., ADLS Gen2, S3).
raw_student_path = "/mnt/datalake/raw/students.csv"

df_raw_students = spark.read.format("csv") \
    .option("header", "true") \
    .option("inferSchema", "true") \
    .load(raw_student_path)

display(df_raw_students) # `display()` is a powerful Databricks command for rich visualization.
---
# In Cell 3: Apply transformations.
from pyspark.sql import functions as F

df_transformed = df_raw_students \
    .withColumn("full_name", F.concat_ws(" ", F.col("given_name"), F.col("family_name"))) \
    .withColumn("ingestion_date", F.current_timestamp()) \
    .withColumnRenamed("student_id", "student_key") \
    .select("student_key", "full_name", "faculty", "ingestion_date")
---
# In Cell 4: Use SQL for quick validation.
# First, create a temporary view.
df_transformed.createOrReplaceTempView("students_temp_view")
    

-- In Cell 5: Now switch the cell language to SQL.
%sql
SELECT
  faculty,
  COUNT(1) AS student_count
FROM students_temp_view
GROUP BY faculty
ORDER BY student_count DESC;
    

PySpark Core Concepts: The Engine Room

To master the interview, you must understand the theory behind the code.

Transformations vs. Actions (And Lazy Evaluation)

This is the single most important concept in Spark.

• Transformations: Operations that create a *new* DataFrame from an existing one (e.g., select(), filter(), withColumn(), join()).
• Actions: Operations that trigger the computation and return a result (e.g., show(), count(), collect()) or write data to disk (write()).

Lazy Evaluation means that Spark doesn’t execute transformations immediately. It builds up a plan (a Directed Acyclic Graph or DAG) of all the steps. The work only starts when you call an action. This allows Spark’s Catalyst Optimizer to find the most efficient way to execute your entire plan.

Narrow vs. Wide Transformations

An interviewer might ask this to gauge your understanding of performance.

• Narrow Transformation (Efficient): The data needed to compute a partition in the output DataFrame exists in a single partition of the input DataFrame. There’s no need to move data between executors. Examples: select, filter, withColumn.
• Wide Transformation (Expensive): Data from multiple input partitions is “shuffled” across the network to be combined into a single output partition. The data shuffle is a major performance bottleneck. Examples: groupBy, join, orderBy.

The Databricks Advantage: Delta Lake

This is Databricks’ secret weapon and a key component in building a “reliable and robust data” platform, a common requirement in job descriptions.

Delta Lake is an open-source storage layer that brings ACID (Atomicity, Consistency, Isolation, Durability) transactions to your data lake. It essentially turns your data lake into a “Lakehouse.”

Why is Delta Lake a Game-Changer?

• ACID Transactions: No more corrupted data from failed jobs. Writes are all-or-nothing.
• Time Travel (Data Versioning): You can query older versions of your data. This is incredible for debugging, auditing, and rolling back bad writes.
• Schema Enforcement: Prevents bad data (e.g., a string in an integer column) from being written to your tables, ensuring data quality.

# Writing a DataFrame to Delta Lake is simple.
# This creates a reliable, transactional table for the data warehouse.
delta_path = "/mnt/datalake/curated/dim_student"

df_transformed.write.format("delta").mode("overwrite").save(delta_path)

# Reading is just as easy.
df_student_dim = spark.read.format("delta").load(delta_path)
    

Top Interview Questions & FAQs

Here are some of the most likely questions you’ll face, with tailored answers.