When to use Hadoop in modern data stacks - Mini Project: Build & Apply

When to use Hadoop in modern data stacks

📖 Scenario: You work in a company that collects a lot of data from different sources every day. Your team wants to decide if Hadoop is the right tool to store and process this data alongside other modern tools.

🎯 Goal: Learn how to identify situations where Hadoop is useful in modern data stacks by creating a simple checklist and applying it to example data scenarios.

📋 What You'll Learn

Create a dictionary with example data scenarios and their data sizes

Add a threshold variable for big data size

Use a comprehension to select scenarios where Hadoop is recommended

Print the selected scenarios

💡 Why This Matters

🌍 Real World

Companies collect data from many sources like logs, databases, and sensors. Hadoop helps store and process very large data sets efficiently.

💼 Career

Knowing when to use Hadoop helps data engineers and analysts choose the right tools for big data projects, improving performance and cost.

1

Create example data scenarios

Create a dictionary called data_scenarios with these exact entries: 'small_logs': 10, 'medium_db': 500, 'large_clickstream': 5000, 'huge_sensor_data': 20000. The numbers represent data size in gigabytes.

Hadoop

# Create the dictionary data_scenarios with given entries
# Your code here

Hint

Use curly braces to create a dictionary and separate each key-value pair with a comma.

2

Set the big data size threshold

Create a variable called big_data_threshold and set it to 1000 to represent the minimum data size in gigabytes where Hadoop is recommended.

Hadoop

data_scenarios = {'small_logs': 10, 'medium_db': 500, 'large_clickstream': 5000, 'huge_sensor_data': 20000}
# Set big_data_threshold to 1000
# Your code here

Hint

Just assign the number 1000 to the variable big_data_threshold.

3

Select scenarios suitable for Hadoop

Use a dictionary comprehension to create a new dictionary called hadoop_recommended that includes only the entries from data_scenarios where the data size is greater than big_data_threshold.

Hadoop

data_scenarios = {'small_logs': 10, 'medium_db': 500, 'large_clickstream': 5000, 'huge_sensor_data': 20000}
big_data_threshold = 1000
# Create hadoop_recommended using dictionary comprehension
# Your code here

Hint

Use a dictionary comprehension with for scenario, size in data_scenarios.items() and an if condition.

4

Print the Hadoop recommended scenarios

Write a print statement to display the hadoop_recommended dictionary.

Hadoop

data_scenarios = {'small_logs': 10, 'medium_db': 500, 'large_clickstream': 5000, 'huge_sensor_data': 20000}
big_data_threshold = 1000
hadoop_recommended = {scenario: size for scenario, size in data_scenarios.items() if size > big_data_threshold}
# Print the hadoop_recommended dictionary
# Your code here

Hint

Use print(hadoop_recommended) to show the dictionary.

Practice

1. Which scenario is best suited for using Hadoop in a modern data stack?

easy

A. Running quick, real-time analytics on small data

B. Performing basic calculations in a spreadsheet

C. Storing simple, small CSV files on a single machine

D. Processing very large datasets that don't fit on one computer

Solution

1. Step 1: Understand Hadoop's strength

   Hadoop is designed to handle very large datasets that require distributed storage and parallel processing.

2. Step 2: Match scenario to Hadoop's use case

   Processing data too big for one computer fits Hadoop's design, unlike small or real-time data tasks.

3. Final Answer:

   Processing very large datasets that don't fit on one computer -> Option D

4. Quick Check:

   Big data needs Hadoop [OK]

Hint: Choose Hadoop for huge data, not small or fast tasks [OK]

Common Mistakes:

• Thinking Hadoop is good for real-time analytics
• Using Hadoop for small datasets
• Confusing Hadoop with simple file storage

2. Which of the following is the correct way to describe Hadoop's role in data processing?

easy

A. Hadoop stores and processes data in a distributed way across many machines

B. Hadoop processes data only on a single machine

C. Hadoop is mainly used for real-time data streaming

D. Hadoop is a tool for creating small databases on laptops

Solution

1. Step 1: Recall Hadoop architecture

   Hadoop uses distributed storage (HDFS) and parallel processing (MapReduce) across many machines.

2. Step 2: Eliminate incorrect options

   Options B, C, and D describe Hadoop incorrectly; it is not single-machine, real-time streaming, or for small databases.

3. Final Answer:

   Hadoop stores and processes data in a distributed way across many machines -> Option A

4. Quick Check:

   Distributed storage and processing = Hadoop [OK]

Hint: Hadoop = distributed storage + processing [OK]

Common Mistakes:

• Thinking Hadoop runs on one machine only
• Confusing Hadoop with streaming tools like Kafka
• Assuming Hadoop is for small-scale data

3. Consider this code snippet that checks data size before choosing processing method:

data_size_gb = 500
if data_size_gb > 100:
    method = 'Hadoop'
else:
    method = 'Pandas'
print(method)

What will be the output?

medium

A. Error

B. Pandas

C. Hadoop

D. null

Solution

1. Step 1: Evaluate the condition

   data_size_gb is 500, which is greater than 100, so the if condition is true.

2. Step 2: Determine assigned method

   Since condition is true, method is set to 'Hadoop'.

3. Final Answer:

   Hadoop -> Option C

4. Quick Check:

   500 > 100 -> Hadoop [OK]

Hint: Check if data size > 100GB to pick Hadoop [OK]

Common Mistakes:

• Choosing Pandas for large data
• Confusing > with < in condition
• Expecting syntax error due to print

4. This code tries to decide when to use Hadoop but has a bug:

data_size = 50
if data_size >= 100:
    tool = 'Hadoop'
else:
    tool = 'Spark'
print(tool)

What is the bug if the goal is to use Hadoop only for data larger than 100GB?

medium

A. The condition uses >= instead of >, so Hadoop runs at 100GB

B. The else block should assign 'Hadoop' instead of 'Spark'

C. The print statement is missing parentheses

D. Variable data_size should be a string, not integer

Solution

1. Step 1: Analyze the condition

   The condition uses >= 100, so Hadoop runs when data_size is exactly 100, but goal is strictly greater than 100.

2. Step 2: Identify correct condition

   Changing >= to > fixes the bug to run Hadoop only for data sizes greater than 100.

3. Final Answer:

   The condition uses >= instead of >, so Hadoop runs at 100GB -> Option A

4. Quick Check:

   Use > for strictly greater than 100 [OK]

Hint: Use >, not >=, for strictly greater than checks [OK]

Common Mistakes:

• Confusing >= with > in conditions
• Thinking print() is wrong syntax
• Assuming data_size must be string

5. You have a data pipeline that processes 10TB of logs daily. The logs are unstructured and need batch processing overnight. Which modern data stack choice fits best?

hard

A. Use a real-time streaming tool only

B. Use Hadoop for distributed storage and batch processing

C. Use a single SQL database on one server

D. Use Excel to analyze the logs

Solution

1. Step 1: Understand data size and type

   10TB is very large and logs are unstructured, needing batch processing overnight.

2. Step 2: Match tool to requirements

   Hadoop handles large, unstructured data with batch processing well; others are unsuitable for scale or type.

3. Final Answer:

   Use Hadoop for distributed storage and batch processing -> Option B

4. Quick Check:

   Big unstructured batch data -> Hadoop [OK]

Hint: Big, unstructured, batch data? Pick Hadoop [OK]

Common Mistakes:

• Choosing single server for huge data
• Using real-time tools for batch needs
• Trying Excel for terabytes of data