Is OLTP database is design optimal for Data Warehouse?
No. OLTP database tables are normalized and it will add additional time to queries to return results. Additionally OLTP database is smaller and it does not contain longer period (many years) data, which needs to be analyzed. A OLTP system is basically ER model and not Dimensional Model. If a complex query is executed on a OLTP system, it may cause a heavy overhead on the OLTP server that will affect the normal business processes.
If de-normalized is improves data warehouse processes, why fact table is in normal form?
Foreign keys of facts tables are primary keys of Dimension tables. It is clear that fact table contains columns which are primary key to other table that itself make normal form table.
What are lookup tables?
A lookup table is the table placed on the target table based upon the primary key of the target, it just updates the table by allowing only modified (new or updated) records based on thelookup condition.
What are Aggregate tables?
Aggregate table contains the summary of existing warehouse data which is grouped to certain levels of dimensions. It is always easy to retrieve data from aggregated tables than visiting original table which has million records. Aggregate tables reduces the load in the database server and increases the performance of the query and can retrieve the result quickly.
What is real time data-warehousing?
Data warehousing captures business activity data. Real-time data warehousing captures business activity data as it occurs. As soon as the business activity is complete and there is data about it, the completed activity data flows into the data warehouse and becomes available instantly.
What are conformed dimensions?
Conformed dimensions mean the exact same thing with every possible fact table to which they are joined. They are common to the cubes.
What is conformed fact?
Conformed dimensions are the dimensions which can be used across multiple Data Marts in combination with multiple facts tables accordingly.
How do you load the time dimension?
Time dimensions are usually loaded by a program that loops through all possible dates that may appear in the data. 100 years may be represented in a time dimension, with one row per day.
What is a level of Granularity of a fact table?
Level of granularity means level of detail that you put into the fact table in a data warehouse. Level of granularity would mean what detail are you willing to put for each transactional fact.
What are non-additive facts?
Non-additive facts are facts that cannot be summed up for any of the dimensions present in the fact table. However they are not considered as useless. If there is changes in dimensions the same facts can be useful.
What is factless facts table?
A fact table which does not contain numeric fact columns it is called factless facts table.
What are slowly changing dimensions (SCD)?
SCD is abbreviation of Slowly changing dimensions. SCD applies to cases where the attribute for a record varies over time.
There are three different types of SCD.
1) SCD1 : The new record replaces the original record. Only one record exist in database – current data.
2) SCD2 : A new record is added into the customer dimension table. Two records exist in database – current data and previous history data.
3) SCD3 : The original data is modified to include new data. One record exist in database – new information are attached with old information in same row.
What is hybrid slowly changing dimension?
Hybrid SCDs are combination of both SCD 1 and SCD 2. It may happen that in a table, some columns are important and we need to track changes for them i.e capture the historical data for them whereas in some columns even if the data changes, we don’t care.
What is BUS Schema?
BUS Schema is composed of a master suite of confirmed dimension and standardized definition if facts.
What is a Star Schema?
Star schema is a type of organizing the tables such that we can retrieve the result from the database quickly in the warehouse environment.
What Snow Flake Schema?
Snowflake Schema, each dimension has a primary dimension table, to which one or more additional dimensions can join. The primary dimension table is the only table that can join to the fact table.
Differences between star and snowflake schema?
Star schema – A single fact table with N number of Dimension, all dimensions will be linked directly with a fact table. This schema is de-normalized and results in simple join and less complex query as well as faster results.
Snow schema – Any dimensions with extended dimensions are know as snowflake schema, dimensions maybe interlinked or may have one to many relationship with other tables. This schema is normalized and results in complex join and very complex query as well as slower results.
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