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.