Databricks platform overview in Apache Spark - Step-by-Step Execution

Concept Flow - Databricks platform overview

User logs into Databricks

↓

Create or open Workspace

↓

Create Notebook or Job

↓

Write Spark code

↓

Submit code to Cluster

↓

Cluster runs Spark jobs

↓

Results returned to Notebook

↓

Visualize or export results

↓

Manage data and resources

↓

Collaborate with team

↓

End

This flow shows how a user interacts with Databricks: logging in, creating notebooks, running Spark code on clusters, getting results, and collaborating.

Execution Sample

Apache Spark

# Sample Spark code in Databricks notebook
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
data = [(1, 'Alice'), (2, 'Bob'), (3, 'Cathy')]
df = spark.createDataFrame(data, ['id', 'name'])
df.show()

This code creates a Spark DataFrame with sample data and shows it in the notebook.

Execution Table

Step	Action	Evaluation	Result
1	Import SparkSession	from pyspark.sql import SparkSession	SparkSession class available
2	Create SparkSession	spark = SparkSession.builder.getOrCreate()	SparkSession object created
3	Prepare data list	data = [(1, 'Alice'), (2, 'Bob'), (3, 'Cathy')]	List of tuples created
4	Create DataFrame	df = spark.createDataFrame(data, ['id', 'name'])	DataFrame with 3 rows and 2 columns created
5	Show DataFrame	df.show()	Table displayed: id | name 1 | Alice 2 | Bob 3 | Cathy
6	End of code execution	No more code	DataFrame displayed in notebook

💡 Code execution ends after displaying the DataFrame in the notebook.

Variable Tracker

Variable	Start	After Step 3	After Step 4	Final
spark	None	SparkSession object created	SparkSession object created	SparkSession object created
data	None	[(1, 'Alice'), (2, 'Bob'), (3, 'Cathy')]	[(1, 'Alice'), (2, 'Bob'), (3, 'Cathy')]	[(1, 'Alice'), (2, 'Bob'), (3, 'Cathy')]
df	None	None	DataFrame with 3 rows and 2 columns	DataFrame with 3 rows and 2 columns

Key Moments - 3 Insights

Why do we need to create a SparkSession before running Spark code?

The SparkSession is the entry point to use Spark features. Without it, Spark cannot run code or create DataFrames. See execution_table step 2 where SparkSession is created.

What does df.show() do in the notebook?

df.show() displays the DataFrame content as a table in the notebook output. It does not change the data, just shows it. See execution_table step 5.

Why is the data variable created before the DataFrame?

The data variable holds the raw data as a list. We need it first to pass into spark.createDataFrame to make the DataFrame. See execution_table steps 3 and 4.

Visual Quiz - 3 Questions

Look at the execution_table, what is the value of 'df' after step 4?

A None

B A list of tuples

C A Spark DataFrame with 3 rows and 2 columns

D A SparkSession object

💡 Hint

Check the 'Result' column in execution_table row for step 4.

At which step is the SparkSession object created?

A Step 2

B Step 1

C Step 3

D Step 4

💡 Hint

Look at the 'Action' and 'Result' columns in execution_table for SparkSession creation.

If we skip creating the 'data' list, what will happen at step 4?

A DataFrame will be created with empty data

B Error because data is not defined

C SparkSession will fail to create

D df.show() will display an empty table

💡 Hint

Refer to variable_tracker and execution_table steps 3 and 4 about 'data' variable.

Concept Snapshot

Databricks lets you write and run Spark code in notebooks.
You start by creating a SparkSession.
Load or create data, then make DataFrames.
Run code on clusters and see results instantly.
Use notebooks to visualize and share your work.

Full Transcript

Databricks is a platform where users log in and create notebooks to write Spark code. The user starts by creating a SparkSession, which is needed to run Spark commands. Then, data is prepared as a list of tuples. This data is converted into a Spark DataFrame. The DataFrame is shown in the notebook output. The platform runs the code on clusters and returns results quickly. Users can visualize data and collaborate with others in the workspace.

Practice

1. What is the main purpose of the Databricks platform?

easy

A. To only store data without processing

B. To combine data storage, processing, and analysis in one platform

C. To replace Apache Spark with a new engine

D. To provide a simple text editor for coding

Solution

1. Step 1: Understand Databricks platform features

   Databricks integrates data storage, processing, and analysis in one place.

2. Step 2: Compare options with platform purpose

   Only To combine data storage, processing, and analysis in one platform correctly describes this combined purpose.

3. Final Answer:

   To combine data storage, processing, and analysis in one platform -> Option B

4. Quick Check:

   Databricks = Unified data platform [OK]

Hint: Remember Databricks is an all-in-one data platform [OK]

Common Mistakes:

• Thinking Databricks only stores data
• Confusing Databricks with just a code editor
• Believing it replaces Apache Spark

2. Which of the following is the correct way to start a cluster in Databricks?

easy

A. Click the 'Start Cluster' button in the Clusters UI

B. Run start_cluster() in a notebook cell

C. Use the command line to type cluster start

D. Clusters start automatically without any action

Solution

1. Step 1: Recall Databricks cluster management

   Clusters are started via the UI by clicking the 'Start Cluster' button.

2. Step 2: Evaluate other options

   Running start_cluster() or command line commands is not standard; clusters do not start automatically.

3. Final Answer:

   Click the 'Start Cluster' button in the Clusters UI -> Option A

4. Quick Check:

   Start clusters via UI button [OK]

Hint: Use the UI button to start clusters, not code commands [OK]

Common Mistakes:

• Trying to start clusters with code commands
• Assuming clusters start automatically
• Confusing cluster start with notebook run

3. Given the following code in a Databricks notebook:

df = spark.range(5)
df.show()

What will be the output?

medium

A. A table showing numbers from 0 to 4

B. An error because spark.range is not valid

C. An empty table with no rows

D. A table showing numbers from 1 to 5

Solution

1. Step 1: Understand spark.range(5)

   This creates a DataFrame with numbers starting at 0 up to 4 (5 numbers total).

2. Step 2: Analyze df.show() output

   It displays the DataFrame rows, showing numbers 0,1,2,3,4.

3. Final Answer:

   A table showing numbers from 0 to 4 -> Option A

4. Quick Check:

   spark.range(5) = 0 to 4 [OK]

Hint: spark.range(n) creates numbers from 0 to n-1 [OK]

Common Mistakes:

• Thinking range starts at 1
• Expecting an error from spark.range
• Assuming empty output

4. You run this code in a Databricks notebook:

df = spark.range(3)
df.show(5)

But it only shows 3 rows. Why?

medium

A. The cluster is too small to show 5 rows

B. show(5) is invalid syntax and ignored

C. The DataFrame only has 3 rows, so show(5) shows all available rows

D. You must call collect() before show()

Solution

1. Step 1: Check DataFrame size

   spark.range(3) creates 3 rows: 0,1,2.

2. Step 2: Understand show(5) behavior

   show(5) tries to show 5 rows but only 3 exist, so it shows all 3.

3. Final Answer:

   The DataFrame only has 3 rows, so show(5) shows all available rows -> Option C

4. Quick Check:

   show(n) shows up to n rows [OK]

Hint: show(n) displays up to n rows, not more than exist [OK]

Common Mistakes:

• Thinking show(5) causes error
• Believing cluster size limits show output
• Forgetting DataFrame row count

5. You want to analyze a large dataset using Databricks. Which combination of features helps you write code and run it on powerful machines easily?

hard

A. Manual data download and local scripts

B. Only notebooks without clusters

C. Clusters without notebooks

D. Notebooks and clusters

Solution

1. Step 1: Identify tools for coding and execution

   Notebooks let you write code interactively; clusters provide powerful machines to run code.

2. Step 2: Evaluate options

   Only Notebooks and clusters combines both notebooks and clusters, enabling easy coding and execution on powerful resources.

3. Final Answer:

   Notebooks and clusters -> Option D

4. Quick Check:

   Notebooks + clusters = easy code + power [OK]

Hint: Use notebooks for code and clusters for power [OK]

Common Mistakes:

• Ignoring clusters and using only notebooks
• Trying to run code without clusters
• Downloading data manually instead of using platform