When to use Hadoop in modern data stacks - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

When is Hadoop the best choice for data storage?

Which scenario best describes when Hadoop is the most suitable option for data storage in a modern data stack?

A Storing and processing very large volumes of unstructured data across many servers with fault tolerance.

B Running real-time analytics on small datasets with low latency requirements.

C Managing transactional data with strict ACID compliance and low latency queries.

D Hosting a small data warehouse for a single department with simple SQL queries.

💡 Hint

Think about Hadoop's strength in handling big data and fault tolerance.

Hadoop is designed to store and process huge amounts of unstructured data distributed across many machines, providing fault tolerance. It is not optimized for real-time or transactional workloads.

❓ Predict Output

intermediate

Output of Hadoop MapReduce job simulation

What is the output of this simplified Python simulation of a Hadoop MapReduce word count job?

Hadoop

data = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']

# Map step
mapped = [(word, 1) for word in data]

# Shuffle and sort step (group by word)
grouped = {}
for word, count in mapped:
    grouped[word] = grouped.get(word, 0) + count

# Reduce step
reduced = {word: count for word, count in grouped.items()}

print(reduced)

A {'apple': 3, 'banana': 2, 'orange': 1}

B {'apple': 1, 'banana': 1, 'orange': 1}

C {'apple': 2, 'banana': 2, 'orange': 1}

D SyntaxError

💡 Hint

Count how many times each word appears in the list.

The code counts the occurrences of each word: 'apple' appears 3 times, 'banana' 2 times, and 'orange' once.

❓ data_output

advanced

Result of combining Hadoop with Spark for data processing

Given a dataset stored in Hadoop HDFS, what is the main advantage of using Apache Spark on top of Hadoop for processing?

Choose the output that best describes the result of this combination.

A Only supports SQL queries without machine learning capabilities.

B Slower batch processing due to Hadoop's disk-based storage limiting Spark's speed.

C Removes the need for Hadoop's distributed storage.

D Faster in-memory data processing with support for complex analytics on large datasets stored in Hadoop.

💡 Hint

Think about Spark's in-memory processing and Hadoop's storage.

Spark can process data stored in Hadoop's HDFS much faster by using in-memory computation, enabling complex analytics and machine learning on big data.

🔧 Debug

advanced

Identify the error in this Hadoop streaming command

What error will this Hadoop streaming command produce?

hadoop jar /usr/lib/hadoop-mapreduce/hadoop-streaming.jar \
  -input /data/input \
  -output /data/output \
  -mapper 'python mapper.py' \
  -reducer 'python reducer.py' \
  -file mapper.py \
  -file reducer.py

A Error: Invalid Hadoop streaming jar path.

B Error: Mapper script not found.

C Error: Output directory /data/output already exists.

D No error, job runs successfully.

💡 Hint

Check if the output directory exists before running a Hadoop job.

Hadoop streaming jobs fail if the output directory already exists. You must delete or rename it before running the job again.

🚀 Application

expert

Choosing Hadoop for a modern data stack architecture

You are designing a modern data stack for a company with massive historical logs and need to store petabytes of data cost-effectively. The data is mostly unstructured and accessed for batch analytics. Which option best justifies using Hadoop in this architecture?

A Hadoop is ideal for transactional databases requiring ACID compliance.

B Hadoop provides scalable, distributed storage and batch processing that can handle petabytes of unstructured data cost-effectively.

C Hadoop replaces the need for cloud data warehouses and BI tools entirely.

D Hadoop is best for real-time streaming analytics and low-latency queries on small datasets.

💡 Hint

Consider Hadoop's strengths in storage and batch processing for large unstructured data.

Hadoop is designed for scalable, distributed storage and batch processing of very large unstructured datasets, making it cost-effective for petabyte-scale historical logs. It is not suited for real-time or transactional workloads.

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