Basics of Transaction Management

9 important questions on Basics of Transaction Management

What are the decision making levels? And its systems?

Operational level
- day to day business decisions
- short time frame
- OLTP: online transaction processing

Tactical level
- middle management decisions
- medium term focus

Strategic level
- senior management
- long term focus

Operational systems
- operational level
- focus on simple insert, update, delete, select statements
- transaction throughput

Decision support systems
- tactical + strategic level
- focus on data retrieval by answering complex ad hoc queries
- represent data in a multidimensional way
- trend analysis

Difference transactional system and data warehouse?

Usage: day to day - decision support tactical/strategic
Latency: real time - periodic snapshots
Design: application oriented - subject oriented
Normalization: normalized - denormalized
Manipulation: Insert/Update/Delete/Select - Insert/Select
Transaction management: Important - Less of concern
Type of queries: many simple - fewer but complex

What are the different data warehouse schemas?

Star schema
- fact table: one tuple per transaction or event and also measurement data
- dimension table: further info about each of the facts. Criteria for aggregating the data. Often denormalized

Snowflake schema
- in case dimension tables grow too large
- in case most queries dont make use of the outer level dimension tables

Fact constellation
- more fact tables and innser dimension tables that link to both.

MAYBE CHECK VID AGAIN
  • Higher grades + faster learning
  • Never study anything twice
  • 100% sure, 100% understanding
Discover Study Smart

What are the specific schema issues?

Surrogate keys
Granularity of the fact table
Factless fact tables
Optimizing the dimensions tables
Defining junk dimensions
Defining outrigger tables
Slowly changing dimensions
Rapidly changing dimensions

What are surrogate keys?

Meaningless integers. Cannot use business keys since they usually have a business meaning. Essentially buffer the data warehouse from the operational environment.

What are junk dimensions?

Deals with low cardinality attribute types such as flags or indicators. Junk dimension is a dimension that simply enumerates all feasible combinations of values of the low cardinality attribute types

What are outrigger tables?

Stores a set of attribute types of a dimension table which are highly correlated, low in cardinality and updated simultaneously.

What are slowly and rapidly changing dimensions?

Slowly
- dimensions that change slowly and irregularly over a period of time. Check 4 approaches on slides

Rapidly
- change rapidly and regularly over period of time. Some approaches result into a lot of rows. Split customer information into stable and rapidly changing information which is put in mini-dimension table

What is the ETL process?

The extraction transformation and loading process
- can consume upto 80% of all efforts needed to set up a data warehouse
- dump the data in a so-called staging area where the ETL activities can be executed

The question on the page originate from the summary of the following study material:

  • A unique study and practice tool
  • Never study anything twice again
  • Get the grades you hope for
  • 100% sure, 100% understanding
Remember faster, study better. Scientifically proven.
Trustpilot Logo