A large volume of data is being generated on a daily basis. Storing this data and ensuring that it can be used by various departments for analytical, reporting and decision making purposes is essential for reporting at various levels. Data warehousing the process of storing, collecting and managing this data. In this blog, we’ll be talking about the top 66 data warehouse interview questions and answers which you must learn in 2021.
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Top Data Warehouse Interview Questions
1. What is a Data warehouse?
A data warehouse is a central repository of all the data used by different parts of the organization. It is a repository of integrated information available for queries, analysis and can be accessed later. When the data has been moved, it needs to be cleaned, formatted, summarized, and supplemented with data from many other sources. And this resulting data warehouse becomes the most dependable source of data for report generation and analysis purposes.
2. What is Data mining?
Data mining is a process of analyzing data from different perspectives, dimensions, patterns and summarizing them into meaningful content. Data is often retrieved or queried from the database in its own format. On the other hand, it can be defined as the method or process to turn raw data into useful information.
3. What is the difference between Data Warehousing and Data Mining?
A data warehouse is for storing data from different transactional databases through the process of extraction, transformation, and loading. Data is stored periodically. It stores a huge amount of data. A couple of use cases for data warehouses are product management and development, marketing, finance, banking, etc. It is used for improving operational efficiency and for MIS report generation and analysis purposes.
Whereas, Data Mining is a process of discovering patterns in large datasets by using machine learning methodology, statistics, and database systems. Data is analyzed regularly here. It analyses mostly on a sample of data. A couple of use cases are Market Analysis and management, identifying anomaly transactions, corporate analysis, risk management, etc. It is used for improving the business and making better decisions.
4. What is data transformation?
Data transformation is the process or method of changing the format, structure, or values of data.
5. What is the difference between database and data warehouse?
|Types of data||Rational or non-relational or object-oriented||Large Volume with multiple data types|
|data operations||databases deal with transactionalprocessing||data warehousesdeal with data modelling, analysis and reporting|
|dimension of data||Databases are two dimensional because it deals with tables which are essentially 2d arrays||In data warehouses, it can have multi-dimensional data they could be 3d 4d|
|data design||Databases have ER based and application-oriented database design||Data warehouses have star snowflake schema and subject-oriented database design|
|size of data||Traditional databases, not big data databases are small usually in gigabytes||Data warehouses are in the terabytes functionality for databases|
|Functionality||High availability and performance||It has flexibility and user autonomy because it’s going to be performing a lot of analysis with the data warehouse|
6. Why do we need a Data Warehouse?
The primary reason for a data warehouse is for an organization to get advantage over its competitors. This also helps the organization to make smart decisions. Smarter decisions can be taken only if the executive responsibilities for taking such decisions have data at their disposal.
7. What are the key characteristics of a data warehouse?
Some of the major key characteristics of a data warehouse are listed below:
- The part of data can be denormalized so that it can be simplified and improve the performance of the same.
- A huge volume of historical data is stored and used whenever it is needed.
- A lot of queries are involved where a lot of data is additionally retrieved to support the queries.
- The data load is controlled
- Ad hoc queries and planned queries are quite common when it comes to data extraction.
8. What is the difference between Database vs. Data lake vs. Warehouse vs Data Mart?
A database is typically structured with a defined schema so structured data can be fit in a database; items are organized as a set of tables with columns and rows, and columns indicate attributes, and rows indicate an object or entity. It has to be structured and filled in here within all these rows and columns. Columns represent attributes, and rows refer to an object or entity. The database is designed to be transactional and generally not designed to perform data analytics. Some examples are Oracle, MySQL, SQL Server, PostgreSQL, MS SQL Server, MongoDB, Cassandra, etc. It is generally used to store and perform business functional or transactional data.
Data warehouse exists on top of several databases, and it is used for business intelligence. Data warehouse gathers the data from all of these databases and creates a layer to optimize data to perform analytics. It mainly stores the processed, refined, highly modeled, highly standardized, and cleansed data.
A data lake is a centralized repository for structure and unstructured data storage. It can be used to store raw data as it is without any structure schema. There is no need to perform any ETL or transformation job on it. Any type of data can be stored here like images, text, files, videos, and even it can store machine learning model artifacts, real-time and analytics output, etc. Data retrieval processing can be done via export, so the schema is defined on reading. It mainly stores raw and unprocessed data. The main focus is to capture and store as much data as possible.
Data Mart lies between data warehouse and Data Lake. It’s basically a subset of filtered and structured essential data of a specific domain or area for a specific business need.
9. What is a data model?
A data model is simply a diagram that displays a set of tables and the relationship between them. This helps in understanding the purpose of the table as well as their dependency. A data model applies to any software development that involves the creation of database objects to store and manipulate data. This includes transactional systems as well as data warehouse systems. The data model is being designed through three main stages; they are – conceptual data model, logical and physical data model in this order.
A conceptual data model is just a set of square shapes connected by a line. The square shape represents an entity, and the line represents a relationship between the entities. This is very high level and highly abstract, and key attributes should be here.
The logical data model expands the conceptual data model by adding more detail to it and further identifies it as key attributes and non-key attributes. Hence, key attributes or attributes that define the uniqueness of that entity, such as in the time entity, it’s the date that’s a key attribute. It also considers the relationship type, whether it is one to one or one to many or many to many.
The physical data model looks a little similar to a logical data model; however, there are significant changes. Here entities will be replaced by tables, and attributes will be referred to as columns. So tables and columns are words specific to a database, whereas entities and attributes are specific to a logical data model design, so a physical data model always refers to these as tables and columns. It should be database technology compatible.
10. What is Data Modelling?
Data Modelling is a very simple step of simplifying an entity here in the concept of data engineering, and it will be simplifying a complex software by simply breaking it up into diagrams and further breaking into flow charts. Flowcharts are a simple representation of how a complex entity can be broken down into a simple diagram, so this will basically give a visual representation and easier understanding of the complex problem and even better readability to a person who might not be proficient in that particular software usage as well.
Data modeling is generally defined as a framework for data to be used within information systems by supporting specific definitions and formats. It is a process used to define and analyze data requirements needed to support the business processes within the boundary of respective information systems in organizations. Therefore, the creation of data modeling involves experienced data modelers working closely with business stakeholders, as well as potential users of the information system.
11. What are the differences between structured and unstructured data?
Structure data is neat, has a known schema, and could be fit in a fixed table. It uses the DBMS storage method. Scaling schemas is very difficult. Some of the following protocols are ODBS, SQL, and ADO.NET, etc.
Whereas, Unstructured data has no schema or structure. It is mostly unmanaged and very easy to scale in runtime, and can store any type of data. Some of the followed protocols are XML,CSV, SMSM, SMTP, JASON etc.
12. What is ODS?
ODS stands for Operational Data Store and it stores the real-time operational data only and it does not store any long term trend data or historical data.
13. What is the difference between OLTP & OLAP?
|Abbreviation||Online Transaction Processing||Online Analytical Processing|
|Used for||Day to day business transaction||Analysed or reporting purpose|
|Used by||End users, business users||Business Analyst, Decision Makers, Management level users|
|Data Insertion/ Change Frequency||Very frequent||Mostly fixed number of times through scheduled jobs|
|Mostly Used Statement||Select, Insert, Update, Delete||Select|
|Type of System or Source of data||Source system, Main source of data||Target system, data are transferred from OLTP through extraction, Transformation and Loading process|
|Data Volume||Less compare to OLAP||Very high|
|Processing speed or latency||Very fast||Depending on the amount of data, report generation SLA time can be a few seconds to a few hours.|
|Focus||More focus on effective data storing and quick completion of the request. Hence generally limited number indexes are used||Focus on retrieval of data hence more indexes are used.|
|Backup||A more frequent backup needs to be placed. Even runtime incremental backup is always recommended||Time to time backup, less frequent and no need for runtime incremental backup.|
14. What is metadata and why is it used for?
The definition of Metadata is data about data. Metadata is the context that gives information a richer identity and forms the foundation for its relationship with other data. It can also be a helpful tool that saves time, keeps organized, and helps make the most of the files working with. Structural Metadata is information about how an object should be categorized to fit into a larger system with other objects. Structural Metadata establishes a relationship with other files to be organized and used in many ways.
Administrative Metadata is information about the history of an object, who used to own it, and what can be done with it. Things like rights, licenses, and permissions. This information is helpful for people managing and taking care of an object.
One point of data only gains its full meaning when it’s put in the right context. And the better-organized Metadata will reduce the searching time significantly.
15. What is the difference between ER Modelling vs Dimensional Modelling?
|ER Modelling||Dimension Modelling|
|Used for OLTP Application design.Optimized for Select / Insert / Update / Delete||Used for OLAP Application design. Optimized for retrieving data and answering business queries.|
|Revolves around entities and their relationships to capture process||Revolves around Dimensions for decision making, Doesn’t capture process|
|The unit of storage is a table.||Cubes are units of storage.|
|Contains normalized data.||Contains denormalized data|
16. What is the difference between view and materialized view?
A view is to access the data from its table that does not occupy space, and changes get affected in the corresponding tables while in materialized view pre-calculated data persists it has physical data space occupation in the memory and changes will not get affected in the corresponding tables. Materialized view concept came from database links which earlier mainly used for making a copy of remote data sets. Nowadays, it’s widely used for performance tuning.
The view always holds the real-time data, whereas Materialized view contains a snapshot of data that may not be real-time. There are a couple of methods available to refresh the data in the Materialized view.
17. What does data purging mean?
Data purging name is quite straightforward it is the process involving methods that can erase data permanently from the storage several techniques and strategies can be used for data purging the process of data forging often contrasts with data deletion, so they are not the same deleting data is more temporarily while data purging permanently removes the data this, in turn, frees up more storage and memory space which can be utilized for other purposes the purging process allows us to archive data even if it is permanently removed from the main source giving us an option to recover that data in case we purge it the deleting process also permanently removes the data but does not necessarily involve keeping a backup it generally involves insignificant amounts of data.
18. Please provide a couple of data warehouse solutions which are widely used in the industry currently.
There are a couple of solutions are available in the market, some of the major solutions are
- Oracle Exadata
- Apache Hadoop
- SAP BW4HANA
- Microfocus Vertica
- AWS Redshift
- GCP Big Query]
19. Provide the couples of renowned used ETL tools used in the Industry.
Some of the major ETL tools are
- Oracle Data Integrator
- Microsoft – SQL Server Integrated Services (SSIS)
20. What is a slowly changing dimension?
A slowly changing dimension (SCD) is one that appropriately manages changes of dimension members over time. It applies when business entity value changes over time and in an ad-hoc manner.
21. What are the different types of SCD?
There are six sorts of Slowly Changing Dimension that are commonly used. They are as follows:
Type 0 – Dimension never changes here, dimension is fixed, and no changes are permissible.
Type 1 – No History Update record directly. There’s no record of historical values, only the current state. A kind 1 SCD always reflects the newest values, and when changes in source data are detected, the dimension table is overwritten.
Type 2 – Row Versioning Track changes as version records which will be identified by current flag & active dates and other metadata. If the source system doesn’t store versions, then it’s usually the info warehouse load process that detects changes and appropriately manages the change during a dimension table.
Type 3 – Previous Value column Track change to a selected attribute, add a column to point out the previous value, which is updated as further changes occur.
Type 4 – History Table shows the current value in the dimension table. All changes are tracked and stored in a separate table.
Hybrid SCD – Hybrid SDC utilizes techniques from SCD Types 1, 2, and three to trace change.
Only types 0, 1, and a couple of are widely used, while the others are applied for very specific requirements.
22. What is a Factless fact table?
Factless fact is a fact table without any value. Such a table only contains keys from different dimension tables.
23. What is a fact table?
A fact table contains the measurements, metrics, or facts of a business process. It is located at the middle of a star schema or a snowflake schema, and it is surrounded by dimension tables.
24. What are non-additive facts?
Non-additive facts are not able to sum up for any of the dimensions available in the fact table. If there is any change in the dimension, then the same facts can be useful.
25. What is a conformed fact?
A conformed fact is a type of table that will be used across multiple data marts and multiple fact tables.
26. What is the core dimension?
The core dimension is a Dimension table, which is used, is dedicated for a single fact table or Data Mart.
27. What is dimensional data modelling?
Dimensional modeling is a set of guidelines to design database table structures for easier and faster data retrieval. It is a widely accepted technique. The benefits of using dimensional modeling are its simplicity and faster query performance. Dimension modeling elaborates logical and physical data models to further detail model data and data relationship requirements. Dimensional models map the aspects of every process within the business.
Dimensional Modelling is a core design concept used by many data warehouse designers to design data warehouses. During this design model, all the info is stored in two sorts of tables.
- Facts table
- Dimension table
The fact table contains the facts or measurements of the business, and the dimension table contains the context of measurements by which the facts are calculated. Dimension modeling is a method of designing a data warehouse.
28. What are the types of Dimensional Modelling?
Types of Dimensional Modelling are listed below:
- Conceptual Modelling
- Logical Modelling
- Physical Modelling
29. What is the difference between E-R modelling and Dimensional modelling?
The basic difference is that E-R modeling has a logical and physical model while Dimensional modeling has only a physical model. E-R modeling is required for normalizing the OLTP database design, whereas dimensional modeling is required for de-normalizing the ROLAP/MOLAP design.
30. What is a Dimension Table?
A dimension table is a type of table that contains attributes of measurements stored in fact tables. It contains hierarchies, categories, and logic that can be used to traverse nodes.
31. What is a degenerate dimension?
In a data warehouse, a degenerate dimension is a dimension key in the fact table that does not have its own dimension table. Degenerate dimensions commonly occur when the fact table’s grain is a single transaction (or transaction line).
32. What is the purpose of cluster analysis and data warehousing?
One of the purposes of cluster analysis is to achieve scalability so regardless of the quantity of data system will able to analyze its ability to deal with different kinds of attributes so no matter the data type of the attributes present in the data set able to deal with its discovery of clusters with attribute shape high dimensionality which have multiple dimensions more than 2d to be precise ability to deal with noise, so any inconsistencies in the data to deal with that and interpretability.
33. What is the difference between agglomerative and divisive hierarchical clustering?
The agglomerative hierarchical constraining method allows clusters to be read from bottom to top so that the program always reads from the sub-component first and then moves to the parent in an upward direction. In contrast, divisive hierarchical clustering uses a top to bottom approach in which the parent is visited first and then the child. The agglomerative hierarchical method consists of objects in which each object creates its clusters. These clusters are grouped to form a larger cluster. It is also the process of continuous merging until all the single clusters are merged into a complete big cluster that will consist of the objects of the chart clusters; however, in divisive clustering, the parent cluster is divided into smaller clusters. It keeps on dividing until each cluster has a singular object to represent.
34. What is ODS?
ODS is a database that is designed to integrate data from multiple sources for additional operations of the data. The full form of ODS is the operational data source, unlike the master data source, where the data is not sent back to the operational systems. It may be passed for further operations and to the data warehouse for reporting. In ODS, data can be scrubbed, resolved for redundancy, and checked for compliance with the corresponding business rules, so whatever data in order to filter it out basically to see if there is some data redundancy in the data. It is checked, and it also sees whether the data is compliant with the organization’s business rules. This data can be used for integrating disparate data from multiple sources so that business operations analysis and reporting can be carried out. This is the place where most of the data used in the current operation are housed before it’s transferred to the data warehouse for the longer term and for storage and archiving.
Simple queries on small amounts of data such as finding the status of a customer order, it is easier to find the details from ODS rather than Data warehousing as it does not make sense to search a particular customer order status on a larger dataset which will be more costly to fetch the single records. But for analysis like sentimental analysis, prediction, anomaly detection where data warehousing will perform the role to play with its large data volumes.
ODS is similar to short-term memory, where it only stores very recent information. On the contrary, the data warehouse is more like a long-term memory storing relatively permanent information because a data warehouse is created on a permanent basis.
35. What is the level of granularity of a fact table?
A fact table is usually designed at a low level of granularity. This means that we need to find the lowest amount of information stored in a fact table. For example, employee performance is a very high level of granularity while employee performance daily and employee performance weekly can be considered low levels of granularity because they are much more frequently recorded data. The granularity is the lowest level of information stored in the fact table; the depth of the data level is known as granularity in the date dimension. The level could be year month quarter period week and the day of granularity, so the day being the lowest level the year being the highest level the process consists of the following two steps determining the dimensions that are to be included and determining the location to find the hierarchy of each dimension of that information the above factors of determination will be resent as per the requirements.
36. What’s the biggest difference between Inmon and Kimball philosophies of knowledge warehousing?
These are two philosophies that we’ve in data warehousing. Within the Kimball philosophy, data warehousing is viewed as a constituency of knowledge mods, so data mods are focused on delivering business objectives for departments in a corporation, and therefore the data warehouse may be a confirmed dimension of the info mods hence a unified view of the enterprise are often obtained from the dimension modeling on a departmental area level, and within the Inmon philosophy we will create a knowledge warehouse on a topic by discipline basis hence the event of the info warehouse can start with the info from the web store other subject areas are often added to the info warehouse as their need arises point of sale or pos data are often added later if management decides that it’s required and if we check it out on a kind of algorithmic basis within the Kimball philosophy we first accompany data marts then we combine it and that we get our data warehouse while with Inmon philosophy we first create our data warehouse then we create our data marts.
Both differ within the concept of building the info Warehouse. – Kimball views Data Warehousing as a constituency of knowledge marts. Data marts are focused on delivering business objectives for departments in a corporation, and therefore the Data Warehouse may be a conformed dimension of the info Marts. Hence, a unified view of the enterprise is often obtained from the dimension modeling on a departmental area level. – Inmon explains in creating a knowledge Warehouse on a subject-by-subject area basis. Hence, the event of the info Warehouse can start with data from the web store. Other subject areas are often added to the info Warehouse as their needs arise. Point-of-sale (POS) data is often added later if management decides that it’s necessary.
37. Explain the ETL cycles three-layer architecture.
ETL stands for extraction transformation and loading, so there are three phases involved in it – the primary is the staging layer, then the info integration layer, and the last layer is the access layer. So these are the three layers that are involved for the three specific phases within the ETL cycle, so within the staging layer, it’s used for the info extraction from various data structures of the source, within the data integration layer, data from the staging layer is transformed and transferred to the info base using the mixing layer the data is arranged in hierarchical groups often mentioned as dimensions facts or aggregates during a data warehousing system the mixture of facts and dimension tables is called a schema so basically within the data integration layer, once the info is loaded and data extracted and transformed within the staging layer and eventually the access layer where the info is accessed and may be loaded for further analytics.
38. What’s an OLAP Cube?
The idea behind OLAP was to pre-compute all calculations that are needed for reporting. Generally, calculations are done through a scheduled batch job processing at non-business hours when the database server is normally idle. The calculated fields are stored during a special database, called an OLAP Cube.
An OLAP Cube doesn’t need to loop through any transactions because all the calculations are pre-calculated, providing instant access.
An OLAP Cube may be a snapshot of knowledge at a selected point in time, perhaps at the top of a selected day, week, month, or year.
At any time, you’ll refresh the Cube, using the present values within the source tables.
With very large data sets, it could take an appreciable amount of your time for Excel to reconstruct the Cube.
But with the info sets we’ve been using (just a few thousand rows), the method appears to be instantaneous.
39. Explain the chameleon method utilized in data warehousing.
Chameleon may be a methodology that may be a hierarchical clustering algorithm that overcomes the restrictions of the prevailing models and methods in data warehousing. This method operates on the sparse graph having nodes that represent data items and edges which represent the weights of the info items. This representation allows large data sets to be created and operated successfully. The tactic finds the clusters that are utilized in the info set using the two-phase algorithm. The primary phase consists of the graph partitioning that permits the clustering of the info items into a larger number of sub-clusters; the second phase, on the opposite hand, uses an agglomerative hierarchical clustering algorithm to look for the clusters that are genuine and may be combined alongside the sub-clusters that are produced.
40. What’s virtual data warehousing?
A virtual data warehouse provides a collective view of the finished data. Therein warehouse a virtual data warehouse has no historical data. It is often considered as a logical data model of the given metadata. Virtual data warehousing is that the de facto data system strategy for supporting analytical deciding. It’s one of the simplest ways for translating data and presenting it within the form which will be employed by decision-makers. It provides a semantic map that allows the top user also for viewing because the data is virtualized.
41. What is active data warehousing?
An active data warehouse represents a single state of a business. Active data warehousing considers the analytical perspectives of customers and suppliers. It helps in showing the updated data through reports. Now, this is the most common form of data warehousing, which is used for large businesses and specifically those which deal in the industry of e-commerce or commerce. A form of repository of captured transactional data is known as active data warehousing. Using this concept, trends and patterns are found to be used for future decision making, so based on the analytical results from the data warehouse, It can perform further business decisions active data warehouse as a feature which can integrate the changes of data while scheduled cycles refresh enterprises utilize an active data warehouse and drawing the company’s image in a very statistical manner. So everything is essentially a combination of all the data that is present in various data sources. Combine it all together and then perform some analytics on it to get insights for further business decisions.
42. What is a snapshot with reference to a data warehouse?
Snapshots are pretty common in software, especially in databases, so essentially, it is what the name suggests snapshot refers to the complete visualization of data at the time of extraction. It occupies less space and can be used to backup and restore data quickly, so essentially snapshot a data warehouse when anyone wants to create a backup of it. So using the data warehouse catalog, It’s creating a report, and the report will be generated as shown as soon as the session is disconnected from the data warehouse.
43. What is XMLA?
XMLA is XML for analysis, and it is a SOAP-based XML protocol that can be used and considered as a standard for accessing data in the OLAP method, data mining, or data sources on the internet. It is the simple object access protocol XMLA that uses to discover and execute methods that fetch information from the internet while the execute allows the application to execute against the data sources that are present in XMLA. XMLA is a standard methodology for accessing data in analytical systems such as OLAP. It is based on XML soap and HTTP XMLA specifies MDXML as a query language in XMLA 1.1 version. The only construct is the MDXML in an MDX statement enclosed in the tag.
44. What is the junk dimension?
A Junk Dimension is a type of dimension table consisting of attributes that do not belong in the fact table or in any of the other existing dimension tables. The characteristics of these attributes are usually text or various flags, e.g., non-generic comments or very simple yes/no or true/false indicators. These sorts of attributes typically remain when all the apparent dimensions within the business process are identified, and thus the designer is faced with the challenge of where to place these attributes that don’t belong within the other dimensions.
In some scenarios where data might not be appropriate to store within the schema. The info or attributes are often stored during a junk dimension; the character of the junk during this particular dimension is typically Boolean or flag values. A single dimension is formed by lumping a small number of dimensions. This is called a junk dimension adjunct dimension has unrelated attributes the process of grouping these random flags and text attributes in a dimension by transmitting them to a distinguished sub-dimension is related to junk dimension, so essentially any data that need not be stored in the data warehouse because it is unnecessary is stored in the junk dimension.
45. What are the different types of SCDs used in data warehousing?
SCDs stands for slowly changing dimensions. It is basically a dimension where data changes do not happen frequently or on any regular basis. There are three types of SCDs the first is SCD1 it is a record that is used to replace the original record. Even when there is only one record existing within the database, the present data is going to be replaced, and therefore the new data will take its place.
SCD2 is the new record file that is added to the dimension table. The record exists in the database with the current data and the previous data that is stored in the audit or history.
SCD3 uses the original data that is modified to the new data. This consists of two records, one record that exists in the database and the other record which will replace the old database record with this new information.
46. Which one is faster: multidimensional OLAP or relational OLAP?
Multi-dimensional OLAP also known as MOLAP is faster than relational OLAP because of the following reasons in MOLAP.
The data is stored in a multi-dimensional queue; the storage is not in the relational database but in proprietary formats. MOLAP stores all the possible combinations of data in a multidimensional array.
47. What is hybrid SCD?
Hybrid SCDs are combinations of both SCD1 and SCD2. It may happen that in a table, some columns are important and need to track changes for them that are captured by the historical data for them. Whereas in some columns, even if the data changes, that does not need to bother. For such tables are implemented hybrid SCDs wherein some columns are of type 1, and some are of type 2. So basically, a blanket rule is not applied on the entire table rather than customized on which particular columns where a particular rule needed to be applied.
48. Why do we overwrite the execute method and struts so as parts of the start framework?
We can develop the action servlets and the action form servlets and other circuit classes in the action form class. We can develop a validated method that can return action errors object and in this method. We can write the validation code as well if this method returns null or action errors with the size of zero. The web container will call execute as part of the action class. If it returns a size greater than zero, it will call the execute method. It will rather execute the JSP servlet or the HTML file as the value for the input attribute is part of the attribute in the struts-config XML file.
49. What is VLDB?
VLDB stands for very large database and it is a database that contains a particularly sizable amount of tuples or rows or occupies a particularly large physical file system storage. VLDB database sizes are normally in Terabytes only.
50. How are the time dimensions loaded?
Time dimensions are usually loaded by a program that loops through all possible dates appearing within the data, its commonplace for 100 years to be represented during a time dimension with one row per day.
51. What are conformed dimensions?
Conform dimensions are the dimensions that can be used across multiple data marks in combination with multiple fact tables. A conform dimension is a dimension that has exactly the same meaning and contents; when being referred from different fact tables, it can refer to multiple tables in multiple data marts within the same organization itself.
52. What is the difference between a data warehouse and a data mart?
A data warehouse is a set of data isolated from operational systems, so it is basically a way from the database itself; it is a view of the database. This helps an organization deal with its decision-making process. The Data Mart is a subset of a data warehouse that is geared to a particular business line. Data mods provide the stock of condensed data collected in the organization for analysis on a particular field or entity. So this is basically stating that the data warehouse contains a whole variety of information while a data mart is just a subset of that information based on a particular business line or model.
53. What are the five main testing phases of a project?
ETL test is performed in five stages which are the following the identification of data sources and requirements; first, you will identify which data sources you want for your data warehouse and what are the requirement of the data warehouse and the analytical requirements that your organization needs the acquisition of data naturally after identifying the data source you will acquire that data implementing business logic and dimensional modeling on that data building and publishing that data and the reports that you will create out of the analytics that you perform.
54. What do you mean by the slice action and how many slice operated dimensions are used?
A slice operation is the filtration process in a data warehouse. It selects a specific dimension from a given cube and provides a new sub-cube in the slice operation. Only a single dimension is used, so basically, out of a multi-dimensional data warehouse, if it needs a very specific dimension that needs further analytics or processing, then it will use the slice operation in that data warehouse.
55. What are the stages of data warehousing?
There are 7 Steps to Data Warehousing:
Step 1: Determine Business Objectives
Step 2: Collect and Analyze Information
Step 3: Identify Core Business Processes
Step 4: Construct a Conceptual Data Model
Step 5: Identify Data Sources and Data Transformations planning
Step 6: Set Tracking Duration
Step 7: Implement the Plan
56. What is the difference between data cleaning and data transformation?
Data cleaning is the process that removes data that doesn’t belong in your dataset. Data transformation is that the method by which data from one format or structure converts into another. Transformation processes also can be mentioned as data wrangling or data mugging, transforming, and mapping data from one “raw” data form into another format for warehousing and analyzing. This text focuses on the processes of cleaning that data.
57. What is normalization?
Normalization is a multi-step process that puts data into tabular form, removing duplicated data from the relation tables.
58. What is the benefit of Normalization?
Normalization helps in reducing data redundancy. Thus it saves physical database spaces and has minimal write operation cost.
59. What is Denormalization in a Database?
Denormalization is employed to access the info from a higher or lower normal sort of database. It creates redundancy and stores multiple copies of the same data into different tables.
60. What is the benefit of denormalization?
Denormalization adds required redundant terms into the tables to avoid using complex joins and lots of other complex operations. Denormalization doesn’t mean that normalization won’t be done, but the denormalization process takes place after the normalization process.
61. What is an Extent?
An Extent is a fixed number of contiguous data blocks as per configuration. It is obtained during a single allocation and used to store a specific type of information.
62. What is an Index?
An Index is associated with a database table for retrieval of quick data search or filter operation. An index can consist of one or more columns associated with it. Different types of indexes are available in databases like Unique Key indexes, primary key indexes, Bitmap indexes, and B-Tree indexes. Indexes also hold separate tablespace for storing the preferences of data. Indexes are not recommended where insert, update and delete operations frequently occur rather than a select statement.
63. What is a source qualifier?
A source qualifier represents the rows that the Server reads when it executes a session. Source qualifier transformation needs to be connected for the addition of a relational or a flat file source definition to a mapping.
64. What is ETL Pipeline?
ETL Pipeline refers to a group of processes to extract the info from one system, transform it, and cargo it into some database or data warehouse. ETL pipelines are built for data warehousing applications, which incorporate both enterprise data warehouses and subject-specific data marts. ETL pipelines also are used for data migration solutions. Data warehouse/ business intelligence engineers build ETL pipelines.
65. What is the Data Pipeline?
Data Pipeline refers to any set of process elements that move data from one system to a different one. Data Pipeline is often built for an application that uses data to bring value. It is often used for integrating the info across the applications, building the info-driven web products, and completing the data mining activities. Data engineers build the data pipeline.
66. What is Fact? What are the types of Facts?
A fact may be a central component of a multi-dimensional model that contains the measures to be analyzed. Facts are related to dimensions.
Types of facts are:
- Additive Facts
- Semi-additive Facts
- Non-additive Facts
With this, we are at the end of the blog on the top 66 data warehouse interview questions. We hope that you found this helpful and are now better-equipped to attend your upcoming interview sessions. If you wish to learn more about such concepts, join Great Learning’s PGP Data Science and Business Analytics Course to upskill today. Great Learning also offers mentor support, interview preparation and live sessions with industry experts!
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