Why Hadoop was created for big data - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why Hadoop uses distributed storage

Hadoop stores data across many machines instead of one. Why is this important for big data?

A It ensures data is only stored in one place for security.

B It makes the data easier to delete quickly.

C It reduces the need for data backups.

D It allows handling very large data sets that do not fit on a single machine.

💡 Hint

Think about the size of big data and storage limits of one computer.

Big data is often too large to fit on one machine. Hadoop splits data across many machines to store and process it efficiently.

🧠 Conceptual

intermediate

Why Hadoop processes data in parallel

Hadoop processes data on many machines at the same time. What is the main benefit of this?

A It speeds up processing by dividing work across machines.

B It makes the data more secure by hiding it.

C It reduces the total amount of data stored.

D It allows only one user to access data at a time.

💡 Hint

Think about how doing many small tasks at once compares to doing one big task alone.

Parallel processing lets Hadoop handle large data sets faster by splitting tasks across many machines.

🧠 Conceptual

advanced

How Hadoop handles hardware failures

Big data systems often run on many machines that can fail. How does Hadoop handle this problem?

A By deleting data from failed machines immediately.

B By shutting down the system when one machine fails.

C By replicating data across multiple machines to avoid data loss.

D By storing all data on a single, very reliable machine.

💡 Hint

Think about how to keep data safe even if some machines stop working.

Hadoop copies data to several machines so if one fails, data is still available elsewhere.

🧠 Conceptual

advanced

Why Hadoop uses a simple programming model

Hadoop uses MapReduce, a simple way to write programs for big data. Why is this helpful?

A It lets programmers focus on data tasks without managing complex details of distributed systems.

B It only works for small data sets.

C It requires programmers to write very complex code for each machine.

D It prevents any data from being processed in parallel.

💡 Hint

Think about how simple tools help people work faster and avoid mistakes.

MapReduce hides the complexity of distributed computing, making it easier to write big data programs.

🧠 Conceptual

expert

What problem did Hadoop solve that traditional databases could not?

Traditional databases struggle with big data. What key problem did Hadoop solve to handle big data better?

A It limited data size to fit in memory only.

B It allowed storage and processing of huge, unstructured data across many cheap machines.

C It made databases run only on expensive, high-end servers.

D It removed the need for any data processing.

💡 Hint

Think about data size, structure, and cost of hardware.

Hadoop was created to store and process very large, varied data using many low-cost machines, unlike traditional databases.