Top 40 Data Warehousing Interview Questions and Answers

A group in transformation refers to a collection of individuals or entities undergoing a process of change or development.

• A group in transformation involves a collective effort towards achieving a common goal.

• It often involves a shift in mindset, behavior, or structure.

• Examples include a team of employees adapting to new work processes or a community coming together to address social issues.

LO Extraction involves several steps to extract data from a source system.

• Identify the source system and the data to be extracted

• Create an extraction structure in the source system

• Define the extraction method (e.g., full extraction, delta extraction)

• Configure the extraction process in the source system

• Execute the extraction process

• Transfer the extracted data to the target system

• Perform data transformation and cleansing, if required

• Load the extracted data into the target syste...read more

Facts are measurable data points, while dimensions provide context to the facts.

• Facts are quantitative data that can be measured or counted.

• Dimensions are qualitative data that provide context to the facts.

• Examples: In a sales database, sales amount is a fact, while product category is a dimension.

Snowflake offers unique architecture with separation of storage and compute, automatic scaling, and support for diverse workloads.

• Snowflake's architecture separates storage and compute, allowing for independent scaling and cost optimization.

• Snowflake automatically handles infrastructure management, reducing the need for manual tuning and maintenance.

• Snowflake supports diverse workloads, including data warehousing, data lakes, and real-time analytics.

• Snowflake's unique multi-c...read more

There are three types of schema in Data Warehousing: Star Schema, Snowflake Schema, and Fact Constellation Schema.

• Star Schema: central fact table connected to dimension tables in a star shape

• Snowflake Schema: extension of star schema with normalized dimension tables

• Fact Constellation Schema: multiple fact tables connected to dimension tables in a complex structure

SCD stands for Slowly Changing Dimension in Data Warehousing.

• SCD is a technique used in data warehousing to track changes to dimension data over time.

• There are different types of SCDs - Type 1, Type 2, and Type 3.

• Type 1 SCD overwrites old data with new data, Type 2 creates new records for changes, and Type 3 maintains both old and new values in separate columns.

• Example: In a customer dimension table, if a customer changes their address, a Type 2 SCD would create a new record ...read more

SCD Type 1 in IICS involves overwriting existing data with new data without maintaining historical changes.

• In IICS, use the Mapping Designer to create a mapping that loads data from source to target.

• Use a Lookup transformation to check if the record already exists in the target table.

• If the record exists, update the existing record with new data using an Update Strategy transformation.

• If the record does not exist, insert the new record into the target table.

• Ensure that the ma...read more

Data warehousing in Snowflake is a cloud-based data storage and analytics platform that allows users to store and analyze large volumes of data.

• Snowflake provides a centralized repository for storing structured and semi-structured data.

• It enables users to run complex queries and perform analytics on large datasets.

• Snowflake's architecture separates storage and compute, allowing for scalable and efficient data processing.

• Users can easily scale up or down based on their data st...read more

Data mining is the process of discovering patterns in large datasets, while data warehousing is the process of storing and managing data from multiple sources.

• Data mining involves analyzing data to extract insights and patterns.

• Data warehousing involves collecting and storing data from various sources for easy access and analysis.

• Data mining is used to identify trends and patterns in data that can be used for decision-making.

• Data warehousing is used to provide a centralized r...read more

DWH is structured and optimized for querying, while Data Lake is a vast repository for raw data of all types and formats.

• DWH is schema-on-write, meaning data structure must be defined before loading data

• Data Lake is schema-on-read, allowing for flexibility in data structure

• DWH is typically used for structured data like transactional data

• Data Lake can store structured, semi-structured, and unstructured data like logs, images, videos

• DWH is optimized for fast querying and analys...read more

Standard ADSO is for persistent storage and reporting, while write optimized DSO is for temporary storage. Keys in ADSO are used for data modeling and performance optimization.

• Standard ADSO is used for persistent storage and reporting, while write optimized DSO is used for temporary storage before loading data to a standard ADSO.

• Write optimized DSO does not store data persistently, making it suitable for temporary data storage during data loads.

• Keys in ADSO are defined to uni...read more

Slowly changing dimensions are attributes that change over time, but at a slow rate.

• SCD is a technique used in data warehousing to handle changes in dimensions over time

• Type 1 SCD overwrites old data with new data

• Type 2 SCD creates a new record for each change and maintains a history

• Type 3 SCD adds a new column to the existing record to store the new value

• Examples of SCD include customer addresses, product prices, and employee job titles

Role playing dimensions refer to the various aspects or characteristics that can be portrayed in a role playing scenario.

• Role playing dimensions can include personality traits, emotions, communication styles, and decision-making processes.

• For example, in a customer service role play, the dimensions could include empathy, active listening, problem-solving, and conflict resolution.

• Understanding and utilizing role playing dimensions can help individuals develop their skills and ...read more

DataWarehouse can be built using Pentaho by designing ETL processes, creating data models, and scheduling jobs.

• Design ETL processes to extract, transform, and load data into the DataWarehouse.

• Create data models to define the structure of the DataWarehouse.

• Use Pentaho Data Integration tool for ETL processes.

• Schedule jobs to automate data loading and processing.

• Utilize Pentaho Reporting and Analysis tools for data visualization and analysis.

SCD2 is a type of slowly changing dimension in data warehousing to track historical data changes.

• Use effective dating to track changes over time

• Add new records for changes instead of updating existing ones

• Include attributes like start date, end date, and version number

• Maintain history of changes for auditing purposes

Facts are measurable data in a data warehouse, while dimensions provide context to the facts.

• Facts are quantitative data that can be measured, such as sales revenue or quantity sold.

• Dimensions are descriptive attributes related to the facts, such as time, location, or product category.

• Facts are typically stored in fact tables, while dimensions are stored in dimension tables.

• Dimensions help to provide context and allow for slicing and dicing of the data for analysis.

• Example: I...read more

Types of SCD include Type 1, Type 2, and Type 3.

• Type 1 SCD: Overwrites old data with new data, no history is maintained.

• Type 2 SCD: Maintains historical data by creating new records for changes.

• Type 3 SCD: Creates separate columns to store historical and current data.

• Examples: Type 1 - Employee address updates overwrite old address. Type 2 - Employee salary changes create new record with effective date. Type 3 - Employee job title history stored in separate columns.

SCD Type 1 is a slowly progressive form of sickle cell disease where red blood cells become crescent-shaped due to abnormal hemoglobin.

• SCD Type 1 is characterized by the presence of hemoglobin S (HbS) without any other abnormal hemoglobin variants.

• Patients with SCD Type 1 may experience symptoms such as anemia, pain crises, and organ damage.

• Treatment for SCD Type 1 focuses on managing symptoms and preventing complications.

• Examples of complications associated with SCD Type 1 i...read more

The Slowly Changing Dimension Type 2 (SCD2) transformation is used for handling historical data changes in a data warehouse.

• SCD2 transformation is used to track historical changes in dimension tables.

• It maintains multiple versions of a record by adding new rows with updated information and end-dating the previous record.

• Commonly used in scenarios where historical data needs to be preserved and queried.

• Example: When a customer changes their address, a new row is added with the...read more

Fact tables contain quantitative data that can be measured, while dimension tables contain descriptive attributes related to the facts.

• Fact tables store numerical data such as sales revenue, quantity sold, etc.

• Dimension tables store descriptive attributes like product name, customer name, etc.

• Fact tables are typically larger in size compared to dimension tables.

• Fact tables are connected to dimension tables through foreign keys.

Best practices for DWH

• Design a scalable and flexible architecture

• Ensure data quality and consistency

• Implement proper security measures

• Use ETL tools for data integration

• Create a data dictionary for easy understanding

• Regularly monitor and optimize performance

• Implement disaster recovery and backup plans

An overall datawarehouse solution is a centralized repository of data that is used for reporting and analysis.

• Designing and implementing a data model

• Extracting, transforming, and loading data from various sources

• Creating and maintaining data quality and consistency

• Providing tools for reporting and analysis

• Ensuring data security and privacy

A DHA (Data Handling Application) is created to manage and process data efficiently.

• Identify the data sources and types of data to be handled

• Design a data model and schema for organizing the data

• Implement data collection and storage mechanisms

• Develop data processing algorithms and workflows

• Ensure data security and privacy measures

• Create user-friendly interfaces for data input and retrieval

• Perform regular data quality checks and maintenance

• Integrate with other systems or appli...read more

Data warehousing is structured and optimized for querying, while data lake is a more flexible storage solution for raw data.

• Data warehousing involves storing structured data in a relational database for optimized querying.

• Data lakes store raw, unstructured data in its native format for flexibility and scalability.

• Data warehousing is useful for business intelligence and reporting, providing a structured and organized data repository.

• Data lakes are useful for storing large volu...read more

Data warehousing methodologies include Kimball, Inmon, and Data Vault.

• Kimball methodology focuses on building data marts first and then integrating them into a data warehouse

• Inmon methodology involves building a centralized data warehouse first and then creating data marts

• Data Vault methodology focuses on flexibility and scalability by using hubs, links, and satellites

Performance tuning of Data Warehouse involves optimizing queries, indexing, partitioning, and hardware configurations.

• Identify and optimize slow-running queries by analyzing execution plans and indexing strategies.

• Implement proper indexing on tables to improve query performance.

• Partition large tables to distribute data and queries across multiple physical storage units.

• Optimize hardware configurations such as memory, CPU, and storage to handle large data volumes efficiently.

DataWarehouse modelling involves designing the structure of the database to efficiently store and retrieve data.

• Identify the business requirements and data sources

• Design dimensional model using facts and dimensions

• Normalize or denormalize data based on query patterns

• Implement ETL processes to load data into the DataWarehouse

• Consider performance optimization techniques like indexing and partitioning

Partitioning in data warehouse involves dividing large tables into smaller, more manageable parts based on certain criteria.

• Partitioning helps improve query performance by allowing parallel processing of data.

• Common partitioning methods include range, list, hash, and composite partitioning.

• Example: Partitioning a sales table by date can improve query performance when searching for sales data within a specific time frame.

ODS stands for Operational Data Store, a database that is used for reporting and analysis in real-time.

• ODS is a database that stores detailed and current data from various sources for reporting and analysis.

• It acts as a central repository for data from different operational systems.

• ODS allows for real-time data integration and provides a consistent view of data for reporting purposes.

• It is used to support operational reporting, data mining, and business intelligence.

• Example: ...read more

Staging refers to the process of dividing a construction project into smaller parts or stages.

• Staging helps in better project management and reduces the risk of delays and cost overruns.

• Types of staging include linear staging, concurrent staging, and phased staging.

• Linear staging involves completing one section of the project before moving on to the next.

• Concurrent staging involves working on multiple sections of the project simultaneously.

• Phased staging involves completing t...read more

SCD-2 is a type of slowly changing dimension in data warehousing. Session log is a record of activities performed during a session.

• Session log tracks changes made to data during a session

• It helps in troubleshooting and auditing data changes

• Session log can include details like timestamp, user performing the action, and type of change

• It is important for maintaining data integrity in a data warehouse

Slowly Changing Dimensions (SCD) include Type 1, Type 2, and Type 3 dimensions.

• Type 1: Overwrite existing data with new data, no history is kept.

• Type 2: Create a new record for each change, maintaining history.

• Type 3: Create a new attribute to store changes, keeping limited history.

SCD Type 2 implementation involves tracking historical changes in data by creating new records for each change.

• Identify the columns that need to be tracked for changes

• Add effective start and end dates to track the validity of each record

• Insert new records for changes and update end dates for previous records

• Maintain a surrogate key to uniquely identify each version of the record

LO extraction is the process of extracting data from SAP systems using Logistics Information System (LIS) tables.

• LO extraction is commonly used in SAP BW (Business Warehouse) for data warehousing purposes.

• It involves extracting data related to logistics, such as sales, purchasing, inventory, etc.

• The extracted data is transformed and loaded into the data warehouse for reporting and analysis.

• Examples of LO extraction include extracting sales order data, delivery data, material ...read more

Facts are measurable data points, while dimensions provide context to the facts.

• Facts are quantitative data points that can be measured or counted.

• Dimensions provide context to facts and are descriptive attributes that help categorize or group the facts.

• Example: In a sales database, sales revenue would be a fact, while product category would be a dimension.

Slowly Changing Dimensions (SCD) are used in data warehousing to track changes to data over time. Types include Type 1, Type 2, and Type 3.

• Type 1 SCD: Overwrites old data with new data, losing historical information.

• Type 2 SCD: Creates a new record for each change, preserving historical data.

• Type 3 SCD: Tracks changes by adding columns to the existing record, allowing for limited historical analysis.

SCD 2 is a type of slowly changing dimension in data warehousing, where historical data is preserved by creating new records for changes.

• Use effective date and end date columns to track changes over time

• Implement Type 2 SCD in ETL processes to handle updates and inserts

• Maintain history of changes by creating new records instead of updating existing ones

Data Warehouse design involves structuring data for efficient querying and analysis.

• Identify business requirements and data sources

• Design dimensional model with facts and dimensions

• Implement ETL processes to load data into the warehouse

• Optimize queries for performance

• Consider scalability and data governance

Data warehousing is the process of collecting, storing, and managing data from various sources for analysis and reporting.

• Data warehousing involves extracting data from multiple sources and consolidating it into a central repository.

• It is used for analytical reporting, business intelligence, and decision-making purposes.

• Data warehouses are designed for query and analysis rather than transaction processing.

• Examples of data warehousing tools include Amazon Redshift, Snowflake, ...read more