What is Data Warehousing?
A data warehouse is the main repository of an organization’s historical data, its corporate memory. It contains the raw material for management’s decision support system. The critical factor leading to the use of a data warehouse is that a data analyst can perform complex queries and analysis, such as data mining, on the information without slowing down the operational systems. Data warehousing collection of data designed to support management decision making. Data warehouses contain a wide variety of data that present a coherent picture of business conditions at a single point in time. It is a repository of integrated information, available for queries and analysis.
What are fundamental stages of Data Warehousing?
Offline Operational Databases – Data warehouses in this initial stage are developed by simply copying the database of an operational system to an off-line server where the processing load of reporting does not impact on the operational system’s performance.
Offline Data Warehouse – Data warehouses in this stage of evolution are updated on a regular time cycle (usually daily, weekly or monthly) from the operational systems and the data is stored in an integrated reporting-oriented data structure.
Real Time Data Warehouse – Data warehouses at this stage are updated on a transaction or event basis, every time an operational system performs a transaction (e.g. an order or a delivery or a booking etc.)
Integrated Data Warehouse – Data warehouses at this stage are used to generate activity or transactions that are passed back into the operational systems for use in the daily activity of the organization.
What is Dimensional Modeling?
Dimensional data model concept involves two types of tables and it is different from the 3rd normal form. This concepts uses Facts table which contains the measurements of the business and Dimension table which contains the context(dimension of calculation) of the measurements.
What is Fact table?
Fact table contains measurements of business processes also fact table contains the foreign keys for the dimension tables. For example, if your business process is “paper production” then “average production of paper by one machine” or “weekly production of paper” would be considered as measurement of business process.
What is Dimension table?
Dimensional table contains textual attributes of measurements stored in the facts tables. Dimensional table is a collection of hierarchies, categories and logic which can be used for user to traverse in hierarchy nodes.
What are the Different methods of loading Dimension tables?
There are two different ways to load data in dimension tables.
Conventional (Slow) :
All the constraints and keys are validated against the data before, it is loaded, this way data integrity is maintained.
Direct (Fast) :
All the constraints and keys are disabled before the data is loaded. Once data is loaded, it is validated against all the constraints and keys. If data is found invalid or dirty it is not included in index and all future processes are skipped on this data.
What is OLTP?
OLTP is abbreviation of On-Line Transaction Processing. This system is an application that modifies data the instance it receives and has a large number of concurrent users.
What is the difference between OLTP and OLAP?
OLTP: Operational data is from original data source of the data
OLAP: Consolidation data is from various source.
OLTP: Snapshot of business processes which does fundamental business tasks
OLAP: Multi-dimensional views of business activities of planning and decision making
Queries and Process Scripts
OLTP: Simple quick running queries ran by users.
OLAP: Complex long running queries by system to update the aggregated data.
OLTP: Normalized small database. Speed will be not an issue due to smaller database and normalization will not degrade performance. This adopts entity relationship(ER) model and an application-oriented database design.
OLAP: De-normalized large database. Speed is issue due to larger database and de-normalizing will improve performance as there will be lesser tables to scan while performing tasks. This adopts star, snowflake or fact constellation mode of subject-oriented database design.
Describes the foreign key columns in fact table and dimension table?
Foreign keys of dimension tables are primary keys of entity tables.
Foreign keys of facts tables are primary keys of Dimension tables.
What is Data Mining?
Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information.
What is the difference between view and materialized view?
A view takes the output of a query and makes it appear like a virtual table and it can be used in place of tables.
A materialized view provides indirect access to table data by storing the results of a query in a separate schema object.
What is ER Diagram?
Entity Relationship Diagrams are a major data modelling tool and will help organize the data in your project into entities and define the relationships between the entities. This process has proved to enable the analyst to produce a good database structure so that the data can be stored and retrieved in a most efficient manner.
An entity-relationship (ER) diagram is a specialized graphic that illustrates the interrelationships between entities in a database. A type of diagram used in data modeling for relational data bases. These diagrams show the structure of each table and the links between tables.
What is ODS?
ODS is abbreviation of Operational Data Store. A database structure that is a repository for near real-time operational data rather than long term trend data. The ODS may further become the enterprise shared operational database, allowing operational systems that are being re-engineered to use the ODS as there operation databases.
What is ETL?
ETL is abbreviation of extract, transform, and load. ETL is software that enables businesses to consolidate their disparate data while moving it from place to place, and it doesn’t really matter that that data is in different forms or formats. The data can come from any source.ETL is powerful enough to handle such data disparities. First, the extract function reads data from a specified source database and extracts a desired subset of data. Next, the transform function works with the acquired data – using rules orlookup tables, or creating combinations with other data – to convert it to the desired state. Finally, the load function is used to write the resulting data to a target database.
What is VLDB?
VLDB is abbreviation of Very Large DataBase. A one terabyte database would normally be considered to be a VLDB. Typically, these are decision support systems or transaction processing applications serving large numbers of users.
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.