When to use Hadoop in modern data stacks - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Hadoop primarily used for in data processing?

Hadoop is used for storing and processing very large data sets across clusters of computers using simple programming models.

intermediate

Why might Hadoop still be relevant in modern data stacks?

Hadoop is relevant when you need to handle huge volumes of data that do not fit into memory or single machines, especially for batch processing and cost-effective storage.

beginner

What type of data processing is Hadoop best suited for?

Hadoop is best suited for batch processing large data sets rather than real-time or streaming data.

intermediate

When should you consider alternatives to Hadoop in modern data stacks?

Consider alternatives when you need real-time analytics, low-latency queries, or simpler cloud-managed services that scale automatically.

advanced

How does Hadoop compare with cloud data warehouses in modern data stacks?

Hadoop offers more control and can be cheaper for very large data volumes but requires more management, while cloud warehouses offer ease of use and fast queries but can be more costly at scale.

What is a key strength of Hadoop in modern data stacks?

A Low-latency interactive queries

B Real-time data streaming

C Handling very large batch data processing

D Automatic cloud scaling

Hadoop excels at processing very large data sets in batch mode, not real-time or low-latency queries.

When is Hadoop less suitable compared to modern cloud services?

A When you need to store petabytes of data

B When you want easy-to-manage, real-time analytics

C When you want to process data in batches

D When you want to use open-source tools

Hadoop is less suitable for real-time analytics compared to cloud services designed for low-latency queries.

Which of these is a common use case for Hadoop today?

A Streaming video processing

B Mobile app backend

C Interactive dashboard queries

D Batch processing large logs

Hadoop is commonly used for batch processing large log files and similar big data workloads.

What is a disadvantage of using Hadoop in modern data stacks?

A Requires complex management and setup

B Cannot handle large data volumes

C Does not support batch processing

D Is only available as a cloud service

Hadoop requires more management and setup compared to managed cloud services.

Which scenario favors using Hadoop over cloud data warehouses?

A Massive datasets with cost-sensitive storage

B Small datasets with fast queries

C Real-time user analytics

D Mobile app data sync

Hadoop is cost-effective for storing and processing massive datasets where cost is a concern.

Explain when and why you would choose Hadoop in a modern data stack.

Think about data size, processing type, and cost.

Describe the main differences between Hadoop and modern cloud data warehouses.

Compare control versus convenience.

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