Data warehousing Interview Quiestions ~ Data Warehousing

DATA WAREHOUSING BASICS INTERVIEW QUESTIONS & ANSWERS

1. What is hybrid slowly changing dimension?

Ans:- 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.
For such tables we implement Hybrid SCDs, where in some columns are Type 1 and some are Type 2.
You can add that it is not an intelligent key but similar to a sequence number and tied to a timestamp typically!

2. can a dimension table contains numeric values?

Ans:- Yes.But those datatype will be char (only the values can numeric/char).

yes,diemesions even contain numerical because these are disscriptive elements of

ur business

3. How to Create Surrogate Key using Ab Initio?

Ans:- There are many ways to create Surrogatekey but it depends on your business logic. Here you can try these ways.

1. use next_in_sequence() function in your transform.

2.use Assign key values component (if your gde is higher than 1.10)

3.write a stored proc to this and call this stor proc wherever you need.

Yes, dimension table contains numerics but not contain measures and facts

4. Differences between star and snowflake schemas ?

Ans:- Star schema

A single fact table with N number of Dimension

Snowflake schema

Any dimensions with extended dimensions are know as snowflake schema

STAR MODEL :Radical arrangement of the organisation with one fact table and more dimension tables SNOWFLAKE MODEL: The dimension table subdivide itself forming new dimension table and start acting as a fact table.

a datamart is subject oriented data base through which we can analyse the business in each department in an organisation.

data marts are called high query performance structures

5. What is a CUBE in datawarehousing concept?

Ans:- Cubes are logical representation of multidimensional data.The edge of the cube contains dimension members and the body of the cube contains data

values.

6. 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?

Ans:- Star schema contains the dimesion tables mapped around one or more fact tables.

It is a denormalised model.

No need to use complicated joins.

Queries results fastly.

Snowflake schema

It is the normalized form of Star schema.

Contains indepth joins ,bcas the tbales r splitted in to many pieces.We can easily do modification directly in the tables.

We hav to use comlicated joins ,since we hav more tables .

There will be some delay in processing the Query .

7. What is ER Diagram ?

Ans:- The Entity-Relationship (ER) model was originally proposed by Peter in 1976 [Chen76] as a way to unify the network and relational database views.

Simply stated the ER model is a conceptual data model that views the real world as entities and relationships. A basic component of the model is the Entity-Relationship diagram which is used to visually represents data objects.

Since Chen wrote his paper the model has been extended and today it is commonly used for database design For the database designer, the utility of the ER model is:

it maps well to the relational model. The constructs used in the ER model can easily be transformed into relational tables.

it is simple and easy to understand with a minimum of training. Therefore, the model can be used by the database designer to communicate the design to the end user.

In addition, the model can be used as a design plan by the database developer to implement a data model in a specific database management software.

8. What is degenerate dimension table?

Ans:- Degenerate Dimensions : If a table contains the values, which r neither dimesion nor measures is called degenerate dimensions.Ex : invoice id,empno

9. What is VLDB??

Ans:- The perception of what constitutes a VLDB continues to grow. A one terabyte database would normally be considered to be a VLDB.

degenerate dimension:it doesn't have any link with dimensions and i wont have any attribute.

10. What is meant by metadata in context of a Datawarehouse and how it is important?

Ans:- Meta data is the data about data; Business Analyst or data modeler usually capture information about data - the source (where and how the data is originated), nature of data (char, varchar, nullable, existance, valid values etc) and behavior of data (how it is modified / derived and the life cycle ) in data dictionary a.k.a metadata. Metadata is also presented at the Datamart level, subsets, fact and dimensions, ODS etc. For a DW user, metadata provides vital information for analysis / DSS.

11. what is incremintal loading?2.what is batch processing?3.what is crass reference table?4.what is aggregate fact table??

Ans:- Incremental loading means loading the ongoing changes in the OLTP.

Aggregate table contains the [measure] values ,aggregated /grouped/summed up to some level of hirarchy.

12. What are the possible data marts in Retail sales.?

Ans:- Product information,sales information

13. What is the main differnce between schema in RDBMS and schemas in Data Warehouse....?

Ans:- RDBMS Schema

* Used for OLTP systems,* Traditional and old schema,* Normalized

* Difficult to understand and navigate.,* Cannot solve extract and complex problems

* Poorly modelled

DWH Schema

* Used for OLAP systems.,* New generation schema.,* De Normalized

* Easy to understand and navigate., * Extract and complex problems can be easily solved .,* Very good model

14. What are the vaious ETL tools in the Market?

Ans:- Various ETL tools used in market are:

1)Informatica.2)Data Stage 3) Oracle Warehouse Bulider 4)Ab Initio.,Data Junction

Apart from the above Humming Bird Genio, Business Objects Data Integrator

15. What is Dimensional Modelling?

Ans:- Dimensional Modelling is a design concept used by many data warehouse desginers to build thier datawarehouse. In this design model all the data is stored in two types of tables - Facts table and Dimension table. Fact table contains the facts/measurements of the business and the dimension table contains the context of measuremnets ie, the dimensions on which the facts are calculated.

16. What is Data warehosuing Hierarchy?

Ans:- Hierarchies

Hierarchies are logical structures that use ordered levels as a means of organizing data. A hierarchy can be used to define data aggregation. For example, in a time dimension, a hierarchy might aggregate data from the month level to the quarter level to the year level. A hierarchy can also be used to define a navigational drill path and to establish a family structure.

Within a hierarchy, each level is logically connected to the levels above and below it. Data values at lower levels aggregate into the data values at higher levels. A dimension can be composed of more than one hierarchy. For example, in the product dimension, there might be two hierarchies--one for product categories and one for product suppliers.

Dimension hierarchies also group levels from general to granular. Query tools use hierarchies to enable you to drill down into your data to view different levels of granularity. This is one of the key benefits of a data warehouse.

When designing hierarchies, you must consider the relationships in business structures. For example, a divisional multilevel sales organization.

Hierarchies impose a family structure on dimension values. For a particular level value, a value at the next higher level is its parent, and values at the next lower level are its children. These familial relationships enable analysts to access data quickly.

Levels

A level represents a position in a hierarchy. For example, a time dimension might have a hierarchy that represents data at the month, quarter, and year levels. Levels range from general to specific, with the root level as the highest or most general level. The levels in a dimension are organized into one or more hierarchies.

Level Relationships

Level relationships specify top-to-bottom ordering of levels from most general (the root) to most specific information. They define the parent-child relationship between the levels in a hierarchy.

Hierarchies are also essential components in enabling more complex rewrites. For example, the database can aggregate an existing sales revenue on a quarterly base to a yearly aggregation when the dimensional dependencies between quarter and year are known.

17. What is data validation strategies for data mart validation after loading process?

Ans:- Data validation is to make sure that the loaded data is accurate and meets the business requriments.

Strategies are different methods followed to meet the validation requriments

18. What r the data types present in bo? n wht happens if we implement view in the designer n report

Ans:- Three different data types: Dimensions,Measure and Detail.

View is nothing but an alias and it can be used to resolve the loops in the universe.

19. What is surrogate key ? where we use it expalin with examples???

Ans:- surrogate key is a substitution for the natural primary key.

It is just a unique identifier or number for each row that can be used for the primary key to the table. The only requirement for a surrogate primary key is that it is unique for each row in the table.

Data warehouses typically use a surrogate, (also known as artificial or identity key), key for the dimension tables primary keys. They can use Infa sequence generator, or Oracle sequence, or SQL Server Identity values for the surrogate key.

It is useful because the natural primary key (i.e. Customer Number in Customer table) can change and this makes updates more difficult.

Some tables have columns such as AIRPORT_NAME or CITY_NAME which are stated as the primary keys (according to the business users) but ,not only can these change, indexing on a numerical value is probably better and you could consider creating a surrogate key called, say, AIRPORT_ID. This would be internal to the system and as far as the client is concerned you may display only the AIRPORT_NAME.

2. Adapted from response by Vincent on Thursday, March 13, 2003

Another benefit you can get from surrogate keys (SID) is :

Tracking the SCD - Slowly Changing Dimension.

Let me give you a simple, classical example:

On the 1st of January 2002, Employee 'E1' belongs to Business Unit 'BU1' (that's what would be in your Employee Dimension). This employee has a turnover allocated to him on the Business Unit 'BU1' But on the 2nd of June the Employee 'E1' is muted from Business Unit 'BU1' to Business Unit 'BU2.' All the new turnover have to belong to the new Business Unit 'BU2' but the old one should Belong to the Business Unit 'BU1.'

If you used the natural business key 'E1' for your employee within your datawarehouse everything would be allocated to Business Unit 'BU2' even what actualy belongs to 'BU1.'

If you use surrogate keys, you could create on the 2nd of June a new record for the Employee 'E1' in your Employee Dimension with a new surrogate key.

This way, in your fact table, you have your old data (before 2nd of June) with the SID of the Employee 'E1' + 'BU1.' All new data (after 2nd of June) would take the SID of the employee 'E1' + 'BU2.'

You could consider Slowly Changing Dimension as an enlargement of your natural key: natural key of the Employee was Employee Code 'E1' but for you it becomes

Employee Code + Business Unit - 'E1' + 'BU1' or 'E1' + 'BU2.' But the difference with the natural key enlargement process, is that you might not have all part of your new key within your fact table, so you might not be able to do the join on the new enlarge key -> so you need another id.

19. What is a linked cube?

Ans:- Linked cube in which a sub-set of the data can be analysed into great detail. The linking ensures that the data in the cubes remain consistent.

20. What is a source qualifier?-

Ans:- When you add a relational or a flat file source definition to a mapping, you need to connect it to a Source Qualifier transformation. The Source Qualifier represents the rows that the Informatica Server reads when it executes a session.

21. What are the steps to build the datawarehouse??

Ans:- As far I know...

Gathering bussiness requirements,Identifying Sources,Identifying Facts,

Defining Dimensions,Define Attribues,Redefine Dimensions & Attributes

Organise Attribute Hierarchy & Define Relationship,Assign Unique Identifiers,

Additional convetions:Cardinality/Adding ratios.

1 business modeling 2 data modeling 3 data from the source databases 4 Extration Transformation Loading 5 DataWare house (Data Marts)

22. What are the different architecture of data warehouse??

Ans:- I think, there are two main things.,

1. Top down - (bill Inmon)

2.Bottom up - (Ralph kimbol)

23. What is the difference between view and materialized view??

Ans:- View - store the SQL statement in the database and let you use it as a table. Everytime you access the view, the SQL statement executes.

Materialized view - stores the results of the SQL in table form in the database. SQL statement only executes once and after that everytime you run the query, the stored result set is used. Pros include quick query results.

24. What is the main difference between Inmon and Kimball philosophies of data warehousing?

Ans:- Both differed in the concept of building the datawarehosue..

According to Kimball ...

Kimball views data warehousing as a constituency of data marts. Data marts are focused on delivering business objectives for departments in the organization. And the data warehouse is a conformed dimension of the data marts. Hence a unified view of the enterprise can be obtain from the dimension modeling on a local departmental level.

Inmon beliefs in creating a data warehouse on a subject-by-subject area basis. Hence the development of the data warehouse can start with data from the online store. Other subject areas can be added to the data warehouse as their needs arise. Point-of-sale (POS) data can be added later if management decides it is necessary.

i.e.,

Kimball--First DataMarts--Combined way ---Datawarehouse

Inmon---First Datawarehouse--Later----Datamarts

25. what is junk dimension?

what is the difference between junk dimension and degenerated dimension?

Ans:- Junk dimension: Grouping of Random flags and text Attributes in a dimension and moving them to a separate sub dimension.

Degenerate Dimension: Keeping the control information on Fact table ex: Consider a Dimension table with fields like order number and order line number and have 1:1 relationship with Fact table, In this case this dimension is removed and the order information will be directly stored in a Fact table inorder eliminate unneccessary joins while retrieving order information..

26. What is the definition of normalized and denormalized view and what are the differences between them??

Ans:- Normalization is the process of removing redundancies.

Denormalization is the process of allowing redundancies.

27. Why fact table is in normal form?

Ans:- Basically the fact table consists of the Index keys of the dimension/ook up tables and the measures.

so when ever we have the keys in a table .that itself implies that the table is in the normal form.

28. What is Difference between E-R Modeling and Dimentional Modeling.??

Ans:- Basic diff is E-R modeling will have logical and physical model. Dimensional model will have only physical model.

E-R modeling is used for normalizing the OLTP database design.

Dimensional modeling is used for de-normalizing the ROLAP/MOLAP design.

29. What is Difference between E-R Modeling and Dimentional Modeling.??

Ans:- Basic diff is E-R modeling will have logical and physical model. Dimensional model will have only physical model.

E-R modeling is used for normalizing the OLTP database design.

Dimensional modeling is used for de-normalizing the ROLAP/MOLAP design.

30. What is conformed fact?

Ans:- Conformed dimensions are the dimensions which can be used across multiple Data Marts in combination with multiple facts tables accordingly??

31. What are the methodologies of Data Warehousing?

Ans:- Every company has methodology of their own. But to name a few SDLC Methodology, AIM methodology are stardadly used. Other methodologies are AMM, World class methodology and many more.

32. What is BUS Schema?

Ans:- BUS Schema is composed of a master suite of confirmed dimension and standardized definition if facts.

37. What is snapshot??

Ans:- You can disconnect the report from the catalog to which it is attached by saving the report with a snapshot of the data. However, you must reconnect to the catalog if you want to refresh the data.

38. Is OLAP databases are called decision support system ??? true/false?

Ans:- True.

38. What is active data warehousing?

Ans:- An active data warehouse provides information that enables decision-makers within an organization to manage customer relationships nimbly, efficiently and proactively. Active data warehousing is all about integrating advanced decision support with day-to-day-even minute-to-minute-decision making in a way that increases quality of those customer touches which encourages customer loyalty and thus secure an organization's bottom line. The marketplace is coming of age as we progress from first-generation "passive" decision-support systems to current- and next-generation "active" data warehouse implementations

40. Explain in detail about type 1, type 2(SCD),type 3 ?

Ans:- Type-1 Most Recent Value.

Type-2(full History) i) Version Number ii) Flag iii) Date

Type-3

Current and one Privileges value

41. Kindly number What are the steps to be taken to schedule the report?

Ans:- You can schedule any report using Business Objects (reporter) .1) Open report in BO2) Select option " File->Send To- BCA"3) Select the BCA name to which report has to be scheduled4) Set other options for report scheduling like time , any macro , user etc.

42. what is aggregate table and aggregate fact table ... any examples of both??

Ans:- Aggregate tableÂ contains summarised data. The materialized view are aggregated tables. For ex in sales we have only date transaction. if we want to create a report like sales by product per year. in such cases we aggregate the date vales into week_agg, month_agg, quarter_agg, year_agg. to retrive date from this tables we use @aggrtegate function.

43. What is the difference between OLAP and datawarehosue??

Ans:- Datawarehouse is the place where the data is stored for analyzing where as OLAP is the process of analyzing the data,managing aggregations,partitioning information into cubes for indepth visualization.

44. What is the difference between ODS and OLTP??

Ans:- ODS:- It is nothing but a collection of tables created in the Datawarehouse that maintains only current data.

where as OLTP maintains the data only for transactions, these are designed for recording daily operations and transactions of a business.

ods has broad enterprise wide scope ,but unlike the real enterprise data warehouse ,data is refreshd in near real time and used for routin business activity whereas o;yp is online transaction proces which comes across daily bases data .

ODS: Operational Data Source/Store, The source from where we will get the data for DWH is called ODS OLTP: We have content(Front End) associated with the back end.

a fact table without facts.i,e there is no key measure to analyse the business.

47. What is ETL?

Ans:- ETL stands for extraction, transformation and loading.

ETL provide developers with an interface for designing source-to-target mappings, ransformation and job control parameter.

· Extraction

Take data from an external source and move it to the warehouse pre-processor database.

· Transformation

Transform data task allows point-to-point generating, modifying and transforming data.

· Loading

Load data task adds records to a database table in a warehouse.

48. What Snow Flake Schema?

Ans:- 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.

49. What is a dimension table?

Ans:- A dimensional table is a collection of hierarchies and categories along which the user can drill down and drill up. it contains only the textual attributes.

50. What are modeling tools available in the Market?

Ans:- There are a number of data modeling tools

Tool Name Company Name,Erwin Computer Associates ,Embarcadero Embarcadero Technologies ,Rational Rose IBM Corporation ,Power Designer Sybase Corporation ,

Oracle Designer Oracle Corporation

51. What are the vaious ETL tools in the Market?

Ans:- Various ETL tools used in market are:

1. Informatica,2. Data Stage,3. MS-SQL DTS(Integrated Services 2005)

4. Abinitio,5. SQL Loader,6. Sunopsis,7. Oracle Warehouse Bulider,

8. Data Junction

52. What is Dimensional Modelling? Why is it important ?

Why is Data Modeling Important?

---------------------------------------

Data modeling is probably the most labor intensive and time consuming part of the development process. Why bother especially if you are pressed for time? A common response by practitioners who write on the subject is that you should no more build a database without a model than you should build a house without blueprints.

The goal of the data model is to make sure that the all data objects required by the database are completely and accurately represented. Because the data model uses easily understood notations and natural language , it can be reviewed and verified as correct by the end-users.

The data model is also detailed enough to be used by the database developers to use as a "blueprint" for building the physical database. The information contained in the data model will be used to define the relational tables, primary and foreign keys, stored procedures, and triggers. A poorly designed database will require more time in the long-term. Without careful planning you may create a database that omits data required to create critical reports, produces results that are incorrect or inconsistent, and is unable to accommodate changes in the user's requirements.

57. What is the Difference between OLTP and OLAP?

Ans:- Main Differences between OLTP and OLAP are:-

1.User and System Orientation

OLTP: Customer-oriented, used for data analysis and querying by clerks, clients and IT professionals.

OLAP: Market-oriented, used for data analysis by knowledge workers( managers, executives, analysis).

2. Data Contents

OLTP: Manages current data, very detail-oriented.

OLAP: Manages large amounts of historical data, provides facilities for summarization and aggregation, stores information at different levels of granularity to support decision making process.

3. Database Design

OLTP: Adopts an entity relationship(ER) model and an application-oriented database design.

OLAP: Adopts star, snowflake or fact constellation model and a subject-oriented database design.

4. View

OLTP: Focuses on the current data within an enterprise or department.

OLAP: Spans multiple versions of a database schema due to the evolutionary process of an organization; integrates information from many organizational locations and data stores.

57. What is Fact table?

Ans:- Fact Table contains the measurements or metrics or facts of business process. If your business process is "Sales" , then a measurement of this business process such as "monthly sales number" is captured in the Fact table. Fact table also contains the foriegn keys for the dimension tables.

58. What are the Different methods of loading Dimension tables?

Ans:- Conventional Load:

Before loading the data, all the Table constraints will be checked against the data.

Direct load:(Faster Loading)

All the Constraints will be disabled. Data will be loaded directly.Later the data will be checked against the table constraints and the bad data won't be indexed.

58. What is a lookup table?

Ans:- A lookUp table is the one which is used when updating a warehouse. When the lookup is placed on the target table (fact table / warehouse) based upon the primary key of the target, it just updates the table by allowing only new records or updated records based on the lookup condition.

61. What are Data Marts?

Ans:- Data Marts are designed to help manager make strategic decisions about their business.

Data Marts are subset of the corporate-wide data that is of value to a specific group of users.

There are two types of Data Marts:

1.Independent data marts – sources from data captured form OLTP system, external providers or from data generated locally within a particular department or geographic area.

2.Dependent data mart – sources directly form enterprise data warehouses.

62. What is a data warehousing?

Ans:- Data Warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated.This makes it much easier and more efficient to run queries over data that originally came from different sources.

Typical relational databases are designed for on-line transactional processing (OLTP) and do not meet the requirements for effective on-line analytical processing (OLAP). As a result, data warehouses are designed differently than traditional relational databases.

a data ware housing is analysing the business this is timevarient,non volitail,integreated and subject oriented a data ware house is support decision making.

63. Differences between star and snowflake schemas?

Ans:- Star schema - all dimensions will be linked directly with a fat table.

Snow schema - dimensions maybe interlinked or may have one-to-many relationship with other tables.

64. Which columns go to the fact table and which columns go the dimension table?

Ans:- The Primary Key columns of the Tables(Entities) go to the Dimension Tables as Foreign Keys.

The Primary Key columns of the Dimension Tables go to the Fact Tables as Foreign Keys.

65. What is ODS?

Ans:- 1. ODS means Operational Data Store.

2. A collection of operation or bases data that is extracted from operation databases and standardized, cleansed, consolidated, transformed, and loaded into an enterprise data architecture. An ODS is used to support data mining of operational data, or as the store for base data that is summarized for a data warehouse. The ODS may also be used to audit the data warehouse to assure summarized and derived data is calculated properly. The ODS may further become the enterprise shared operational database, allowing operational systems that are being reengineered to use the ODS as there operation databases.

66. What does level of Granularity of a fact table signify?

Ans:- Granularity

The first step in designing a fact table is to

determine the granularity of the fact table. By

granularity, we mean the lowest level of information

that will be stored in the fact table. This

constitutes two steps:

Determine which dimensions will be included.

Determine where along the hierarchy of each dimension

the information will be kept.

The determining factors usually goes back to the

Requirements

67. How are the Dimension tables designed?

Ans:- Most dimension tables are designed using Normalization principles upto 2NF. In some instances they are further normalized to 3NF.

Find where data for this dimension are located.

Figure out how to extract this data.

Determine how to maintain changes to this dimension (see more on this in the next section).

Change fact table and DW population routines.

68. What are conformed dimensions?

Ans:- Conformed dimentions are dimensions which are common to the cubes.(cubes are the schemas contains facts and dimension tables)

Consider Cube-1 contains F1,D1,D2,D3 and Cube-2 contains F2,D1,D2,D4 are the Facts and Dimensions

here D1,D2 are the Conformed Dimensions

69. What is a level of Granularity of a fact table?

Ans:- Level of granularity means level of detail that you put into the fact table in a data warehouse. For example: Based on design you can decide to put the sales data in each transaction. Now, level of granularity would mean what detail are you willing to put for each transactional fact. Product sales with respect to each minute or you want to aggregate it upto minute and put that data.

71. What is SCD1 , SCD2 , SCD3?

Ans:- SCD Stands for Slowly changing dimensions.

SCD1: only maintained updated values.

Ex: a customer address modified we update existing record with new address.

SCD2: maintaining historical information and current information by using

A) Effective Date,B) Versions,C) Flags,or combination of these

SCD3: by adding new columns to target table we maintain historical information and current information.

72. What are slowly changing dimensions?

Ans:- SCD stands for Slowly changing dimensions. Slowly changing dimensions are of three types

SCD1: only maintained updated values.

Ex: A customer address modified we update existing record with new address.

SCD2: Maintaining historical information and current information by using

A) Effective Date,B) Versions,C) Flags,or combination of these

scd3: By adding new columns to target table we maintain historical information and current information

73. What are Semi-additive and factless facts and in which scenario will you use such kinds of fact tables?

Ans:- Snapshot facts are semi-additive, while we maintain aggregated facts we go for semi-additive.

EX: Average daily balance

A fact table without numeric fact columns is called factless fact table.

Ex: Promotion Facts

While maintain the promotion values of the transaction (ex: product samples) because this table doesn’t contain any measures.

74. What are non-additive facts?

Ans:- Non-Additive: Non-additive facts are facts that cannot

be summed up for any of the dimensions present in the

fact table.

75. What are conformed dimensions?

Ans:- Conformed dimensions mean the exact same thing with every possible fact table to which they are joined

Ex:Date Dimensions is connected all facts like Sales facts, Inventory facts..etc

76. How do you load the time dimension?

Ans:- Time dimensions are usually loaded by a program that

loops through all possible dates that may appear in

the data. It is not unusual for 100 years to be

represented in a time dimension, with one row per day.

77. Why are OLTP database designs not generally a good idea for a Data Warehouse?

Ans:- Since in OLTP,tables are normalised and hence query response will be slow for end user and OLTP doesnot contain years of data and hence cannot be analysed.

78. What type of Indexing mechanism do we need to use for a typical datawarehouse?

ANs:- On the fact table it is best to use bitmap indexes. Dimension tables can use bitmap and/or the other types of clustered/non-clustered, unique/non-unique indexes.

To my knowledge, SQLServer does not support bitmap indexes. Only Oracle supports bitmaps.