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.