Distributed Computing with Spark SQL

Which of the following are true when it comes to the business value of big data? (Select all that apply.)

• The size of the data businesses collect is growing

• Businesses are increasingly making data-driven decisions

• Automated technologies mean that data scientists and data analysts are no longer needed

Spark uses... (Select all that apply.)

• A distributed cluster of networked computers made of a driver node and many executor nodes

• A driver node to distribute work across a number of executor nodes

• One very large computer that is able to run computation against large databases

• Your database technology (e.g., Postgres or SQL Server) to run Spark queries

• A distributed cluster of networked computers made of many driver nodes and many executor nodes

How does Spark execute code backed by DataFrames? (Select all that apply.)

• It optimizes your query by figuring out the best "how" to execute what you want

• It separates the "logical plan" of what you want to accomplish from the "physical plan" of how to do it so it can optimize the query

• It executes code determined in advance

• It iterates over all of the source data to exhaustively evaluate queries

What are the properties of Spark DataFrames? (Select all that apply.)

• Resilient: Fault-tolerant

• Dataset: Collection of partitioned data

• Distributed: Computed across multiple nodes

• Tables: Operates as any table in SQL environments

What is the difference between Spark and database technologies? (Select all that apply.)

• Spark is a highly optimized compute engine and is not a database

• Spark is a computation engine and is not for data storage

• Spark operates for both data storage and computation

• Spark does not interact with databases but uses its proprietary DataFrame technology instead

• Spark in an alternative to traditional databases

What is Amdahl's law of scalability? (Select all that apply.)

• A formula that gives the theoretical speedup as a function of the percentage of a computation that can be parallelized

• Amdahl's law states that the speedup of a task is a function of how much of that task can be parallelized

• A formula that gives the expected speed of a single processor performing a computation

• A formula that gives the theoretical speedup as a function of the size of a partition (or subset) of data

• A formula that gives the number of processors (or other unit of parallelism) needed to complete a task

Spark offers a unified approach to analytics. What does this include? (Select all that apply.)

• Spark allows analysts, data scientists, and data engineers to all use the same core technology

• Spark is able to connect to data where it lives in any number of sources, unifying the components of a data application

• Spark unifies applications such as SQL queries, streaming, and machine learning

• Spark code can be written in the following languages: SQL, Scala, Java, Python, and R

• Spark unifies databases with optimized computation allowing for faster computation against the data it stores

What is a Databricks notebook?

• A collaborative, interactive workspace that allows you to execute Spark queries at scale

• A Spark instance that executes queries

• A single Spark query

• A cluster that executes Spark code

How can you get data into Databricks? (Select all that apply.)

• By uploading it through the user interface

• By registering the data as a table

• By "mounting" data backed by cloud storage

• By connecting to Dropbox or Google Drive

What are the qualities of big data? (Select all that apply.)

• Velocity: the speed of data

• Variety: the diversity of data

• Veracity: the reliability of data

• Volume: the amount of data

• Valorous: the positives impact of data