Informationslogistik Unit 8: OLTP, OLAP, SAP, and Data ...institute.unileoben.ac.at/infotech/lehre/infolog/SS2012/unterlagen/... · Reminder: Normalization Transactions, OLTP, OLAP,

Reminder: Normalization Transactions, OLTP, OLAP, SAP, and Data Warehouse

InformationslogistikUnit 8: OLTP, OLAP, SAP, and Data Warehouse

Ronald Ortner and David Pietzka

11. V. 2012



Outline

1 Reminder: NormalizationAnother Example for Normalization

2 Transactions, OLTP, OLAP, SAP, and Data WarehouseTransactionsOLTP and OLAPSAPData Warehouse & OLAP



Another Example for Normalization

Outline







Link:http://wikis.gm.fh-koeln.de/wiki_db/Category/Normalformen

Human resources database:

pers_no name salary dept boss project course

FDs:{pers_no} → {name, salary, dept, boss}

{dept} → {boss}

{boss} → {dept}

MVDs:{pers_no} →→ {project}

{pers_no} →→ {course}


http://wikis.gm.fh-koeln.de/wiki_db/Category/Normalformen

http://wikis.gm.fh-koeln.de/wiki_db/Category/Normalformen



Another Example

Apply decomposition algorithm:

FD {pers_no} → {name, salary, dept, boss} givesdecomposition

pers_no name salary dept boss

pers_no project course




Another Example


FD {dept} → {boss} gives decomposition

pers_no name salary dept dept boss

pers_no project course




Another Example


MVDs {pers_no} →→ {project} and {pers_no} →→ {course}give decomposition


pers_no project pers_no course




Another Example


MVDs {pers_no} →→ {project} and {pers_no} →→ {course}give decomposition


pers_no project pers_no course

is in 4NF




Another Example

When there is more information about projects and courses to store:


pers_no project_no pers_no course_no

project_no name ... course_no name ...



Transactions

Outline





Transactions

Transactions

ACID: atomic, consistent, isolated and durableBegin transactionExecute manipulations (Updates, Inserts, Delets, ...)Commit or Rollback



OLTP and OLAP

Outline





OLTP and OLAP

OLTP vs. OLAP

OLTP: online transaction processingDatabase applications for ongoing workExamples: orders, bookings, etc.current data is important→ many updates and changes in database

OLAP: online analytical processingDatabase applications for analysis and decision supportExample: analysis of trendshistorical data is important→ lots of data, need information in aggregated form



OLTP and OLAP

OLTP vs. OLAP





SAP

Outline





SAP

SAP

SAP: software system, mainly for OLTP

SAP has three levels:big relational database system in the backgroundapplications that work on the database systemgraphical user interface

Access to underlying database system:Some tables can be accessed also outside SAP (using SQL).Usually only read access is sensible.Some other tables can be accessed only via SAP.



SAP

SAP


SAP has three levels:big relational database system in the backgroundapplications that work on the database systemgraphical user interface

Access to underlying database system:Some tables can be accessed also outside SAP (using SQL).Usually only read access is sensible.Some other tables can be accessed only via SAP.



SAP

SAP


SAP RFC:Remote Function Callsaccess for third party applicationskind of interface

Writing applications with ABAP/4 – access to databases withNative SQL (using special interface)Open SQL (direct access to databases)



Data Warehouse & OLAP

Outline






OLTP vs. OLAP



→ no good idea to do OLTP and OLAP on the same database system




OLTP vs. OLAP



→ no good idea to do OLTP and OLAP on the same database system




Data Warehouse

Idea of Data Warehouse:Do OLTP on operational databasesStore information from operational databases regularly(but not online!) in data warehouse

Database Scheme for Data Warehouse:Star Scheme:

Central ‘fact’ tableother tables not normalized

Snowflake Scheme:Central ‘fact’ tableother tables normalized (→ more joins necessary)




Data Warehouse








Data Warehouse








Roll Up and Drill Down

Queries on Data Warehouse for analysis usually aggregate data(→ GROUP BY)

Drill down: more attributes in GROUP BY

Roll up: fewer attributes in GROUP BY

Data can be summarized in a cross table (data cube)




Relations for Aggregation & the Cube Operator

Creating the data cube:

expensive to execute all queries for creating cube

can store relation for data cube(using NULL values where aggregated)still elaborate and uncomfortable→ idea: new SQL operator CUBEUsage: GROUP BY CUBE( attr1, attr2, . . . )Other possibility:

storing maximally drilled-down tableaggregate this table(cheaper than doing each aggregation from scratch)






expensive to execute all queries for creating cubecan store relation for data cube(using NULL values where aggregated)

still elaborate and uncomfortable→ idea: new SQL operator CUBEUsage: GROUP BY CUBE( attr1, attr2, . . . )Other possibility:







expensive to execute all queries for creating cubecan store relation for data cube(using NULL values where aggregated)still elaborate and uncomfortable

→ idea: new SQL operator CUBEUsage: GROUP BY CUBE( attr1, attr2, . . . )Other possibility:







expensive to execute all queries for creating cubecan store relation for data cube(using NULL values where aggregated)still elaborate and uncomfortable→ idea: new SQL operator CUBEUsage: GROUP BY CUBE( attr1, attr2, . . . )

Other possibility:storing maximally drilled-down tableaggregate this table(cheaper than doing each aggregation from scratch)






expensive to execute all queries for creating cubecan store relation for data cube(using NULL values where aggregated)still elaborate and uncomfortable→ idea: new SQL operator CUBEUsage: GROUP BY CUBE( attr1, attr2, . . . )Other possibility:



Documents

Informationslogistik Unit 8: OLTP, OLAP, SAP, and Data ...institute.unileoben.ac.at/infotech/lehre/infolog/SS2012/unterlagen/... · Reminder: Normalization Transactions, OLTP, OLAP,