Chapter 7

How does Hadoop work?

It breaks up Big Data into multiple parts so each part can be processed and analyzed at the same time on multiple computers.

Big Data uses commodity hardware, which is expensive, specialized hardware that is custom built for a client or application.

False

Hadoop was designed to handle petabytes and exabytes of data distributed over multiple nodes in parallel.

False

Big Data is being driven by the exponential growth, availability, and use of information.

True

Hadoop was designed to handle petabytes and exabytes of data distributed over multiple nodes in parallel.

True

Which Big Data approach promotes efficiency, lower cost, and better performance by processing jobs in a shared, centrally managed pool of IT resources?

grid computing

Allowing Big Data to be processed in memory and distributed across a dedicated set of nodes can solve complex problems in near-real time with highly accurate insights. What is this process called?

in-memory analytics

Traditional data warehouses have not been able to keep up with

the variety and complexity of data

What is the Hadoop Distributed File System (HDFS) designed to handle?

unstructured and semistructured non-relational data

Data flows can be highly inconsistent, with periodic peaks, making data loads hard to manage. What is this feature of Big Data called?

variability