Chapter 4 Test Deck Flashcards by cesar martinez

Data Warehouse

A subject-oriented, integrated, time-variant, and nonvoliatile collection of data in support of manament’s decision-making process.

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Data warehousing

The process of constructing and using data warehouses

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Subject-Oriented

organized around Major subjects such as customer, product, sales
Focusing on modeling and analysis of data for decision makers
(OLTP - Online Transaction Processing)

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Integrated

Constructed by integrated multiple, heterogeneous data sources
relational databases, flat files, online transaction records
data cleaning and data integration techniques are applied

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TIme variant

Time horizon significantly longer than operational systems: current value data
data warehouse data: provide info from historical perspective 5-10 years
summarized and historical records, patterns
Contains an element of time, explicitly or implicitly

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Nonvolatile

A physically separate store of data from the operational environment
Update of data does not occur in the data warehouse environment
not require transaction processing, recovery, and concurrence control mechanisms
requires initial loading of data and access of data
OLAP- Online Analytical Processing

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OLTP

Clerk, IT professional
day to day operations
application-oriented E-R model
current, up-to-date detailed
repetitive
read/write
short simple transaction
tens
thousands
100MB-GB
transaction throughput

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Olap

knowledge worker
decision support
subject-oriented star schema
historical multidimensional
ad-hoc
lots of scans
complex query
millions
hundreds
100Gb-TB
query throughput, response

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DBMS

Tuned for OLTP access methods, indexing, concurrency control, recovery

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warehouse

tuned for OLAP: complex OLAP queries, multidimensional view, consolidation

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Enterprise warehouse

collects all of the information about subjects spanning the entire organization

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Data Mart

A subset of corporate-wide data that is of value to a specific group of users. Scope confined to specific, selected groups, such as marketing data market. Independent vs dependent data marts

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virtual warehouse

A set of views over operational databases

only some of the possible summary views may be materialized

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Data extractiom

Get data from multiple, heterogenous, and external sources

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Data cleaning

detect errors in the data and rectify them when possible

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Data Transformation

Convert data from legacy or host format to warehouse format

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Load

sort summarize consolidate, compute views, check integrity, and build indices and partitions

Refresh

propagate the updates from the data sources to the warehouse

Meta data

the data defining warehouse objects
stores: description of the structure of the data warehouse: schema, view, dimensions etc.
The algorithms used for summarization
the mapping from operational environment to the data warehouse
data related to system performance
operational meta-data
business data, terms, ownership of data charging policies

operational meta-data

data lineage, history of migrated data and transformation path,
currency of data, active, archived, or purged,
monitoring information, warehouse usage statistics, error reports, audit trails

A data cube

allows data to be modled and viewd in multiple dimensions
dimension tables and fact tables
it is n-dimensional, 4 d cubes can be a series of 3-d cubes
physical storage may differ from its logical representation

Cuboid

data cube often referred as a cuboid

The lattice of cuboids forms a data cube

base cuboid

n-d base cube

apex cuboid

the top most 0-D cuboid which holds the highest-level of summarization

Star schema

A fact table in the middle connected, via foreign key to primary key relationship, to a ser of dimension tables

Snowflake schema

a refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables forming a shape similar to snowflake Reduce redundancy

Fact Constellations

multiple fact tables share dimension tables, viewed as a collection of stars, therefore, called galaxy schema pr fact constellation

Data warehouses vs datamart

data wearhouse collects information about subjects that span the entire organization, it is enterprise-wide for warehouse, the fact constellation schema is commonly used a data mart is a departmental subset of the data warehouse, it is department-wide the star or snowflake is commonly used star schema is more popular and efficient, less joins

Roll up or drill up

summarize data by clibing up hierarchy or by dimension reduction

drill down or roll down

the reverse of roll up | from higher-level summary to lower level summary or detailed data or introducing new dimensions

slice and dice

slice: select form 1 D Dice: select 2 or more Ds from a subcube

pivot or rotate

reorient the cube, visualization 3d series of 2d blanes

drill across

involving more than one fact table

drill through

through the bottom level of the cube to its back-end related tables using SQl

Information processing

supports querying, basic statistical analysis, and reproting using crosstabs, tables, charts and graphs

analytical processing

multidimensional analysis of data warehouse data | support basic olap operations, slice-dice, drilling,pivoting

Data mining

knowledge discovery from hidden patterns supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools

Relational OLAP or ROLAP

use relational or extended relaional DBMS to store and manage waehosue dat and client front end using OLAP middleware greater scalability than MOLAP technology

Multidimensional OLAP or MOLAP

sparse array-based multidimensional storage engine | fast indexing to precomputed summarized data

Hybrid OLAP or HOLAP

e.g. microsoft SQLServer combines ROLAP and MOLAP technology flexible e.g. low level relational, high level array

Specialized SQL servers

e.g. Redbricks | specialized support for SQL queries over star.snowflake schemas