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Home / Blog / Interview Questions on Data Engineering / Top 10 Data Warehouse Interview Questions
Sharat Chandra is the head of analytics at 360DigiTMG as well as one of the founders and directors of AiSPRY. With more than 17 years of work experience in the IT sector and Worked as a Data scientist for 14+ years across several industry domains, Sharat Chandra has a wide range of expertise in areas like retail, manufacturing, medical care, etc. With over ten years of expertise as the head trainer at 360DigiTMG, Sharat Chandra has been assisting his pupils in making the move to the IT industry simple. Along with the Oncology team, he made a contribution to the field of LSHC, especially to the field of cancer therapy, which was published in the British magazine of Cancer research magazine.
Table of Content
A data warehouse serves as a centralised location where combined data from many sources is kept. It's critical for corporate intelligence, facilitating analytical reporting, and uniting disparate data sets.
Data warehousing uses dimensional modelling as a design strategy to organise data into fact tables and dimension tables. Dimension tables hold contextual information, whereas fact tables hold quantitative data for analysis.
A data warehouse is optimized for querying and analyzing large volumes of historical data, whereas a database is optimized for CRUD operations (Create, Read, Update, Delete) and transaction processing.
Key characteristics include subject-oriented, integrated, time-variant, and non-volatile nature, facilitating the consolidation of data for analytical reporting and decision-making.
The process of obtaining data from several sources, converting it into an analysis-ready format, and then feeding it into a data warehouse is known as ETL (Extract, Transform, Load).
AWS offers a fully managed petabyte-scale data warehouse solution called Amazon Redshift. It is used to analyse data with current business intelligence tools and ordinary SQL.
Google BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse. It allows SQL queries to be run against large datasets with fast performance.
Azure Synapse Analytics combines big data analytics with data warehousing, providing a unified platform for large-scale data preparation, data management, and serving.
These cloud-based data warehouses handle scalability through their managed services, which automatically scale resources to meet query and storage demands.
Redshift uses techniques like columnar storage and data compression; BigQuery leverages its serverless infrastructure and Dremel technology; Synapse Analytics uses massive parallel processing to optimize performance.
Data warehouse automation refers to the use of technology to reduce manual efforts in data warehouse development, maintenance, and management, increasing efficiency and reducing errors.
Combining the advantages of a data lake with a data warehouse, a data lakehouse offers the scalability and cost advantages of a lake alongside the data management & ACID transactions in a warehouse.
Modern data warehouses manage real-time data processing by supporting streaming data integration and providing capabilities for real-time analytics and insights.
Machine learning in data warehouses is used for predictive analytics, data mining, and automating data management tasks, improving insights and operational efficiencies.
OLAP (Online Analytical Processing) is a technology that allows users to perform complex queries and analysis on data. It's a key feature of data warehousing that facilitates multidimensional analysis.
Star and snowflake schemas are two types of dimensional modeling techniques. The star schema has a single fact table linked to dimension tables, and the snowflake schema is a more normalized version with hierarchical dimension tables.
Slowly changing dimensions are managed using techniques like Type 1 (overwrite), Type 2 (add new row), and Type 3 (add new column) to track changes over time.
Indexing strategies in data warehouses are crucial for improving query performance by allowing faster search and retrieval of data within large tables.
Best practices include implementing role-based access control, data encryption, regular audits, adhering to compliance standards, and secure networking practices.
High availability and disaster recovery are ensured through redundant systems, failover mechanisms, regular backups, and geographic replication.
Performance monitoring and tuning involve using built-in monitoring tools, analyzing query performance, optimizing SQL queries, and adjusting resource allocation.
Materialized views are precomputed views that store query results and are used in data warehouses to speed up query performance for complex calculations.
Data partitioning divides a table into smaller, more manageable parts, improving query performance, data management, and load operations.
Large-scale data migrations are handled by planning the migration process, choosing efficient data transfer tools, ensuring data integrity, and validating post-migration data.
Data compression reduces the storage footprint of data in warehouses, improving performance in data loading and querying, and reducing storage costs.
The process of obtaining data from multiple source systems, converting it, and putting it into a data warehouse may be automated by integrating data warehouses with ETL tools.
BI tools integrate with data warehouses to provide reporting, dashboards, and data analysis capabilities, leveraging the structured and processed data in the warehouse.
Data warehouses support governance and compliance by providing a consolidated view of data, enabling audit trails, access controls, and adherence to data privacy regulations.
APIs are important for integrating a data warehouse with other systems and applications, allowing for automated data exchange and extending the functionality of the warehouse.
Data warehouses can work in conjunction with data lakes by storing structured, processed data for analytics, while data lakes store raw, unstructured data for broader data exploration.
Cloud-based data warehouses offer scalability, cost-effectiveness, ease of management, and integration with other cloud services.
Cost management in cloud-based warehouses involves understanding pricing models, monitoring usage, optimizing data storage and queries, and using cost-management tools.
Challenges include data migration complexities, adapting to new cloud technologies, ensuring data security and privacy, and training the team on cloud services.
Serverless architecture in cloud data warehousing reduces the need for infrastructure management, allowing focus on analytics and queries while dynamically scaling resources.
AWS offers Redshift, Azure offers Synapse Analytics, and GCP offers BigQuery. Each has unique features and integrations with their respective cloud ecosystems, differing in pricing models, performance optimization techniques, and native capabilities.
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