Book - Chapter 1 intro to big data analytics

Question 1

What are the vs of big data

Answer 1

Volume. Velocity. Variety.

Question 2

What is meta data

Answer 2

The minimum you should know about the data

Question 3

What is paraders

Answer 3

How has the data been processed. What are the artefacts left in the data

Question 4

What is velocity

Answer 4

It is speed

Question 5

What are the three attributes that stand out of defining big data characteristics

Answer 5

Huge volume of data
Complexity of data types and structures
Speed of new date of creation and growth

Question 6

What is huge volume of data

Answer 6

Rather than thousands of rows, big data can be billions of rows and millions of columns

Question 7

What is complexity of data types and structures

Answer 7

It reflects the variety of new data sources, formats and structures, including digital traces been left on the web and other digital repositories for subsequent analysis

Question 8

What is speed of new data creation and growth

Answer 8

If you describe high velocity data, the rapid data ingestion in near real-time analysis

Question 9

What way is big data sometimes described as having

Answer 9

The big free v’s

Question 10

What are the big three Vs

Answer 10

Volume, variety and velocity

Question 11

Can big data be Efficiently analysed using only traditional database or methods

Answer 11

No it requires new tools and technologies to store, manage and realise the business benefits

Question 12

What main two forms can big data come from

Answer 12

Structured and nonstructured

Question 13

How is most of the big data formed

Answer 13

Usually unstructured or semistructured in nature Which requires different techniques and tools to process and analyse

Question 14

Where does 80 to 90% of future data growth come from

Answer 14

Non-structured data types

Question 15

What sort of data in addition could the RDBMS have

Answer 15

Quasi-or semistructured data, such as three form cell log information taking from an email ticket of the problem, customer chat history

Question 16

What are the four parts of big data characteristics: data structures

Answer 16

Bottom: unstructured
Third: “is the structured
Second: semistructured
Top: structured

Question 17

What is quasi structured

Answer 17

Erratic structure, Webb click

Question 18

What is semistructured

Answer 18

Structure definition is embedded in the data

Question 19

What is structured

Answer 19

External definition of structure

Question 20

What does structured data consist of

Answer 20

A defined data type, format, and structure (transaction data online analytical processing data cubes, traditional RDBMS, CSV files and even simple spreadsheet) Excel

Question 21

What does semistructured data consist of

Answer 21

Textual data files with a discernible pattern that enables passing (such as extensible markup language XML data files that are self describing and find by an XML schema)

Scripts

Question 22

What does quasi-structured data consist of

Answer 22

Textual data with erratic data formats that can be formatted with effort, and time, and tools (for instance, web clckstreams data that may contain inconsistencies in data values and format)

Question 23

What does unstructured data consist of

Answer 23

Text documents, PDFs, images and video i.e. data has no inherent structure

Question 24

How can a clickstream be used

Answer 24

It can be passed in mind by data scientist to discover usage patterns I don’t have a relationship someone clicks and areas of interest on the website a group of sites

Question 25

How does big data describe data

Answer 25

It describes new kinds of data with which most organisations may not be used to working

Question 26

Is database administration training required to create spreadsheets

Answer 26

No

Question 27

What are EDW

Answer 27

Enterprise data warehouse

Question 28

What are enterprise data warehouse is critical for

Answer 28

Reporting and B I tasks and solve many other problems that proliferating spreadsheets introduce such as which of multiple versions of a spreadsheet is correct

Question 29

Despite the benefits of EDW and PI what do these systems tend to restrict

Answer 29

The flexibility need to perform robust or exploratory data analysis

Question 30

With the EDW model who is the data managed and controlled by

Answer 30

IT groups and database administrators (DBA) And data analysts who depend on IT for access and changes to the data of schemas

Question 31

What new problems do EDW and B I introduce

Answer 31

Flexibility and agility which were less pronounced when dealing with spreadsheets

Question 32

What is the solution to the problems faced with EDW and PI when dealing with spreadsheets

Answer 32

The analytic sandbox

Question 33

What does the analytic sandbox attempt to resolve

Answer 33

The conflict for analysis and data scientists with EDw and more formally managed corporate data

Question 34

How are analytic sandboxes purposely designed

Answer 34

To enable robust analytics well being centrally managed and secured

Question 35

How are analytic sandboxes often referred to as

Answer 35

Work spaces as they are designed to enable teams to explore more data set in a controlled fashion and are not typically use the enterprise level financial reporting and sales databases

Question 36

What do Analytic sandboxes enable

Answer 36

High-performance Computering using in database processing