Data Warehousing Quiz

Which of the following statements accurately reflects the market leadership in the global data warehousing market and the Data Warehouse as a Service (DwaaS) segment as of the provided information?

• Snowflake leads the global data warehousing market by a small margin, and AWS Redshift holds the largest share in the DwaaS segment.

• AWS Redshift controls approximately 11.4% of the global data warehousing market, positioning it second to Google BigQuery, but it leads the DwaaS segment with a 20-24% share.

• The global data warehousing market is led by AWS Redshift, while the DwaaS segment's leader is not explicitly stated in terms of market share percentage.

• AWS Redshift controls approximately 11.4% of the global data warehousing market, positioning it second to Snowflake, and also leads the DwaaS segment with a 20-24% share.

In the Data Warehouse architecture, the 'Storage & Data Modeling' stage is primarily responsible for:

• Transforming and validating raw data, and tracking metadata lineage through ETL/ELT pipelines.

• Organizing data into schemas or lakehouse zones to store historical records and optimize queries.

• Collecting raw data from various sources like CRMs, ERPs, and IoT devices.

• Providing business users with access to curated data via BI tools and operational systems under security controls.

A company requires tailored, quick, department-level insights for its Sales team, potentially by drawing data from a broader, existing enterprise-wide repository. Which solution is most suitable?

• An independent Data Mart, as it is standalone and quick to deploy.

• A Data Warehouse, because it is centralized across all departments.

• A dependent Data Mart, as it is a subset of a DW, tailored for Sales, and can draw data from the existing DW.

• A combination of a DW and several independent DMs for scalability.

Which is NOT a primary reason why traditional data warehouses are suboptimal for continuous, real-time analytics?

• Reports typically lag behind live events.

• Real-time queries impose heavy load, slowing the system.

• DWs are batch-oriented, not built for continuous streaming.

• Their inability to integrate with BI tools for immediate visualization.

According to forecasts, which statement reflects CAGR correctly?

• The global data warehousing market is projected to grow at an 8.5% CAGR from 2025–2033, while the DwaaS segment shows a 10.8% CAGR through 2031.

• The DwaaS segment is projected at 8.5% CAGR, while DW is 10.8%.

• Both DW and DwaaS at ~8.5%.

• CAGR for DW given, DwaaS only in value terms.

Walmart’s strategic use of data warehousing is best characterized by:

• Data marts for city-wise demand forecasting & driver behavior.

• Teradata-based DW + departmental DMs for supply chain, finance, marketing → better forecasting, less waste.

• Central repository for purchase, browsing & shipment powering recommendations.

• DM for recommendation system based on user viewing history.

Which statement captures the fundamental difference?

• DW = transaction processing; DM = analytics.

• DW = central enterprise-wide repo for analytics; DM = department-level subset for specific functions.

• DWs are exclusively independent; DMs can be dependent or standalone.

• DW = fast access for teams; DM = scalable across enterprise.

Regarding US and China’s contributions:

• China leads in count, US in capacity.

• US holds majority of global DCs by count; US + China = >70% of global installed capacity.

• Germany + UK + China surpass US in count & capacity.

• US has many DCs but China’s capacity not detailed.

For small teams requiring quick solutions, advantage of a DM is:

• Enterprise-wide standardization & integration.

• Scalability for exponential growth.

• Simplicity, flexibility, quick deployment, cost-effectiveness for departments.

• Single source of truth across business.

Which pairing is incorrect?

• Amazon Redshift → AWS, e-commerce, startups.

• Google BigQuery → media, ads, AI-heavy orgs.

• Microsoft Azure Synapse Analytics → known for independence & partnerships with other clouds.

• Oracle Exadata / Autonomous DW → banking, telecom, legacy enterprises.