interview questions of Latest Updates in Data Warehousing

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Implementing a referential integrity in DW environmentSupports analytical performance in...

A common capacity problem with Data Warehousing involvesData extract transfer of bulk fi...

Design module includes all of the following EXCEPTSpecify data integrity rulesSpec...

Latest Updates in Data Warehousing

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Top data warehousing interview questions importent books

1. What is the data type of the surrogate key?
2. What are the steps to build the data warehouse?
3. What are the different architectures of data warehouse?
4. What are the advantages data mining over traditional approaches?
5. What is the difference between view and materialized view?
6. What is the main difference between Inmon and Kimball philosophies of data warehousing?
7. What is junk dimension? what is the difference between junk dimension and degenerated dimension?
8. What is the definition of normalized and denormalized view and what are the differences between them
9. Why fact table is in normal form?
10. What is Difference between E-R Modeling and dimensional modeling.
11. What is conformed fact?
12. What are the methodologies of Data Warehousing.
13. What is BUS Schema?
14. What is Datawarehousing Hierarchy?
15. What is data validation strategies for data mart validation after loading process
16. What is a linked cube?
17. What is meant by metadata in context of a Datawarehouse and how is it important?
18. What is incremental loading?
19. What is batch processing?
20. What is cross-reference table?
21. What is aggregate fact table?
22. What are the possible data marts in Retail sales?
23. What is the main difference between schema in RDBMS and schemas in DataWarehouse?
24. What are the various ETL tools in the Market?
25. What is Dimensional Modelling?
26. What is VLDB?
27. What is degenerate dimension table?
28. What is ER Diagram?
29. Difference between Snow flake and Star Schema. What are situations where Snow flake Schema is better than Star Schema to use and when the opposite is true?
30. What is a CUBE in data warehousing concept?
31. Differences between star and snowflake schemas?
32. What are Datamarts?
33. Can a dimension table contains numeric values?
34. What is hybrid slowly changing dimension?

ETL Data Warehousing interview questions-11importent

explain why and where do we exactly use the lookup tranformations.

You can use the Lookup transformation to perform many tasks, including:

Get a related value. For example, your source includes employee ID, but you want to

include the employee name in your target table to make your summary data easier to read.

Perform a calculation. Many normalized tables include values used in a calculation, such as gross sales per invoice or sales tax, but not the calculated value (such as net sales).

Update slowly changing dimension tables. You can use a Lookup transformation to

determine whether rows already exist in the target.

how do you tell aggregator stage that input data is already sorted

By enablign sorted input property in Aggregator Properties

What are push and pull etl strategies?Push and Pull strategies determine how data comes from source system to ETL server.

Push : In this case the Source system pushes data i.e.(sends data) to the ETL server.

Pull : In this case the ETL server pulls data i.e.(gets data) from the source system.

What is the Difference between a ODS and Staging Area

ODS :-Operational Data Store which contains data .
ods comes after the staging area
eg:-
In our e.g lets consider that we have day level Granularity in the OLTP & Year level Granularity in the Data warehouse.
If the business(manager) asks for week level Granularity then we have to go to the oltp and summarize the day level to the week level which would be pain taking.So wat we do is that we maintain week level Granularity in the ods for the data,for abt 30 to 90 days.

Note : Ods information would contain cleansed data only. ie after staging area

Staging Area :-
It comes after the etl has finished.Staging Area consists of
1.Meta Data .
2.The work area where we apply our complex business rules.
3.Hold the data and do calculations.
In other words we can say that its a temp work area.

How you capture changes in data if the source system does not have option of storing date/time field in source table from where you need to extract the data?

The DW database can be Oracle or Teradata. The requirement here is to pull data from source system and ETL need to device a mechanism to identify the changes or new records. The source system can be a legacy system like AS400 application or Mainframe application. List out all such methods of data capture. The ETL can be Informatica, data stage or custom etl code.

If LKP on target table is taken, can we update the rows without update strategy transformation?

yes, by using dynamic lookup

In what scenario ETL coding is preferred than Database level SQL,PL/SQL coding?

Data scrubbing process is difficult. That is, file contains date column like 20070823 but data warehouse requires date as 08/23/2007 in that case it is difficult.

Data warehouse interview questions

1. What is source qualifier?
2. Difference between DSS & OLTP?
3. Explain grouped cross tab?
4. Hierarchy of DWH?
5. How many repositories can we create in Informatica?
6. What is surrogate key?
7. What is difference between Mapplet and reusable transformation?
8. What is aggregate awareness?
9. Explain reference cursor?
10. What are parallel querys and query hints?
11. DWH architecture?
12. What are cursors?
13. Advantages of de normalized data?
14. What is operational data source (ODS)?
15. What is meta data and system catalog?
16. What is factless fact schema?
17. What is confirmed dimension?
18. What is the capacity of power cube?
19. Difference between PowerPlay transformer and power play reports?
20. What is IQD file?
21. What is Cognos script editor?
22. What is difference macros and prompts?
23. What is power play plug in?
24. Which kind of index is preferred in DWH?
25. What is hash partition?
26. What is DTM session?
27. How can you define a transformation? What are different types of transformations in Informatica?
28. What is mapplet?
29. What is query panel?
30. What is a look up function? What is default transformation for the look up function?
31. What is difference between a connected look up and unconnected look up?
32. What is staging area?
33. What is data merging, data cleansing and sampling?
34. What is up date strategy and what are th options for update strategy?
35. OLAP architecture?
36. What is subject area?
37. Why do we use DSS database for OLAP tools?

SQL SERVER - Data Warehousing Interview Questions and Answers - Part 1

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 (Ref:Wikipedia). 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.
(Reference Wikipedia)

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 OLAP?
OLAP is abbreviation of Online Analytical Processing. This system is an application that collects, manages, processes and presents multidimensional data for analysis and management purposes.

What is the difference between OLTP and OLAP?
Data Source
OLTP: Operational data is from original data source of the data
OLAP: Consolidation data is from various source.

Process Goal
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.

Database Design
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.