Data Warehouse Interview Questions And Answers | Data Warehouse Interview Preparation | Intellipaat

Summary

This video by Intellipaat dives into the world of data warehousing, covering essential concepts and top 25 interview questions. It compares databases and data warehouses, explains cluster analysis, hierarchical clustering, and the chameleon method. The video also explores virtual and active data warehousing, XMLA, ODS, and the importance of granularity in fact tables. It discusses various types of SCDs, the speed of OLAP systems, hybrid SCDs, VLDBs, time dimension loading, conform dimensions, and philosophies of data warehousing. The script concludes with insights on ETL cycles, data purging, testing phases, and slice operations, providing a comprehensive guide for those interested in data warehousing.

Takeaways

• 📊 Data Warehousing is crucial for data analysis, especially when dealing with large volumes of data from multiple sources.
• 🔍 The primary difference between a database and a data warehouse lies in the type of data, operations, data dimensions, design, size, and functionality they handle.
• 🤖 Cluster analysis in data warehousing aims for scalability, versatility in data attributes, high dimensionality, noise handling, and interpretability.
• 🌐 The Chameleon method is a hierarchical clustering algorithm that efficiently operates on large datasets in a sparse graph representation.
• 🌐 Virtual data warehousing offers a collective view of data without historical data, acting as a logical data model for analytical decision-making.
• 📈 Active data warehousing represents the current state of a business, integrating analytical perspectives and delivering updated data through reports.
• 📸 Snapshots in data warehousing provide a complete visualization of data at the time of extraction, useful for backup and quick data restoration.
• 🔗 XMLA (XML for Analysis) is an industry standard for accessing data in analytical systems like OLAP, based on XML, SOAP, and HTTP.
• 🔄 ODS (Operational Data Store) serves as an integration point for data from multiple sources, preparing it for further operations and reporting.
• 📏 The granularity of a fact table in data warehousing is designed to be at a low level, capturing the most detailed and frequently recorded data.
• 🌟 Different types of SCDs (Slowly Changing Dimensions) are used to handle changes in dimension data over time, with SCD1, SCD2, and SCD3 catering to various change tracking needs.

Q & A

What is the primary purpose of data warehousing?

Data warehousing is primarily used for data analysis. It consolidates data from multiple sources into a single location, allowing for efficient data modeling and analysis, which is essential for making informed business decisions.

How does data in a data warehouse differ from data in a traditional database?

Data in a data warehouse is typically of a large volume and includes multiple data types sourced from various origins. In contrast, traditional databases usually contain structured, relational, or object-oriented data that is smaller in size and focused on transactional processing.

What are the key differences between database operations and data warehouse operations?

Database operations are centered around transactional processing, ensuring high availability and performance. Data warehouse operations, on the other hand, focus on data modeling and analysis, offering high flexibility and user autonomy for comprehensive data analysis.

Can you explain the concept of cluster analysis in the context of data warehousing?

Cluster analysis in data warehousing is used to achieve scalability and analyze large datasets regardless of their size or the type of attributes they contain. It also allows for the discovery of clusters with attribute shape, high dimensionality, and the ability to handle noise and inconsistencies within the data for better interpretability.

What is the difference between agglomerative and divisive hierarchical clustering?

Agglomerative hierarchical clustering starts at the bottom with individual objects and merges them into larger clusters, whereas divisive hierarchical clustering starts at the top with a single parent cluster and divides it into smaller clusters until each cluster contains a single object.

What is the Chameleon method in data warehousing and how does it work?

The Chameleon method is a hierarchical clustering algorithm used in data warehousing to overcome limitations of existing models. It operates on a sparse graph representing data items as nodes and their weights as edges, allowing for the creation and operation of large datasets. It uses a two-phase algorithm: graph partitioning to create sub-clusters and an agglomerative hierarchical clustering algorithm to find genuine clusters that can be combined.

What is a virtual data warehouse and how does it differ from a traditional data warehouse?

A virtual data warehouse provides a collective view of the complete data without storing historical data itself. It acts as a logical data model of metadata, offering a semantic map for end-users to view data virtually. Unlike traditional data warehouses that store actual data, a virtual data warehouse focuses on presenting data in a form usable for decision-makers.

What is the role of an active data warehouse in business?

An active data warehouse represents a single state of a business and integrates the changes of data while scheduled cycles refresh. It helps deliver updated data through reports and is commonly used in large businesses, especially in e-commerce, to find trends and patterns for future decision-making.

What is the significance of snapshots in the context of data warehousing?

Snapshots in data warehousing are complete visualizations of data at the time of extraction. They occupy less space and can be used for quick backup and restore of data, providing a point-in-time representation of the data warehouse's state.

What is XMLA and how is it used in data warehousing?

XMLA, or XML for Analysis, is a standard for accessing data in OLAP, data mining, or data sources over the internet. It uses DISCOVER and EXECUTE methods for fetching information and executing actions against data sources, respectively. XMLA is based on XML, SOAP, and HTTP and specifies MDX as a query language, making it an industry standard for analytical systems.

What is the purpose of an Operational Data Store (ODS) in data warehousing?

An ODS is designed to integrate data from multiple sources for additional operations. It serves as an intermediate step where data can be scrubbed, resolved for redundancy, and checked for compliance with business rules before being transferred to the data warehouse for long-term storage and archiving.

What is the difference between a view and a materialized view in the context of databases?

A view is a virtual table representation that does not occupy physical space and reflects changes in the underlying tables. A materialized view, however, stores pre-calculated data and occupies physical space, and changes in the base tables do not affect the materialized view.

What is a Slowly Changing Dimension (SCD) and what are the types?

A Slowly Changing Dimension (SCD) is a dimension in which data changes infrequently. There are three types: SCD1, which replaces the original record with the new one; SCD2, which adds a new record alongside the existing one, maintaining history; and SCD3, which modifies the original data and also maintains a record of the change.

What is the difference between Multi-Dimensional OLAP (MOLAP) and Relational OLAP (ROLAP) in terms of performance?

MOLAP is generally faster than ROLAP because it stores data in a multi-dimensional structure in proprietary formats, which allows for quicker access and manipulation. ROLAP, on the other hand, relies on relational databases and SQL, which may be slower due to the need for additional processing and compatibility issues with non-technical tools like Excel.

What is a hybrid SCD and when is it used?

A hybrid SCD is a combination of SCD1 and SCD2. It is used when some columns in a table require historical tracking of changes, while others do not. This allows for customization of the SCD type applied to specific columns within the same table.

What are the main differences between a data warehouse and a data mart?

A data warehouse is a large, organization-wide repository of data isolated from operational systems, while a data mart is a smaller, subject-specific subset of the data warehouse focused on a particular business line or department. Data warehouses are typically larger than 100 gigabytes and contain a variety of information, whereas data marts are smaller and focus on a specific area of the business.

What is the ETL cycle and what are its three layers?

The ETL cycle stands for Extraction, Transformation, and Loading. It consists of three layers: the Staging layer for data extraction from various sources, the Data Integration layer for transforming and transferring data to the database, and the Access layer where the data is made accessible for further analytics.

What is data purging and how does it differ from data deletion?

Data purging is the process of permanently erasing data from storage, freeing up space for other uses. It often involves archiving data before purging, allowing for recovery if needed. Data deletion, on the other hand, is typically temporary and does not involve keeping a backup, focusing on removing insignificant amounts of data.

What are the five main testing phases of an ETL project?

The five main testing phases of an ETL project are: identification of data sources and requirements, acquisition of data, implementation of business logic and dimensional modeling, building and publishing data and reports, and performing analytics based on the ETL process.

What is a slice operation in data warehousing and how does it work?

A slice operation in data warehousing is a filtration process that selects a specific dimension from a given cube, providing a new sub-cube. It is used when a particular dimension is needed for further analysis or processing, allowing users to focus on a single dimension within a multi-dimensional data warehouse.