IB Computer Science HL · Year 2 · Lesson 48

Alternative databases + data warehouses

Alternative databases and analytical systems trade relational structure for scale, specialized data types, distribution or analytical power.

Paper 150 minutesA3.4.1 · A3.4.2
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Course homeIA hub2027 case study
Today’s targets

What you need to be able to do

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  • OutlineThe different types of databases as approaches to storing data
  • ExplainThe primary objectives of data warehouses in data management and business intelligence
A3.4.1A3.4.2
Paper 1 lensMatch the depth of every response to the command term. Previously learned content can move quickly, but retrieval must still be accurate.
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Retrieve

Rapid Recall Deck

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Say the answer aloud before flipping. Mark secure knowledge quickly and spend time on the gaps.

  • What alternative database models are covered in this topic?: NoSQL, cloud, spatial, and in-memory databases.
  • When is a spatial database especially useful?: When data has geographic or geometric properties and queries depend on location, distance, regions, or spatial relationships.
  • When is an in-memory database especially useful?: When very low-latency access is important and enough memory is available to keep working data primarily in RAM.
  • What is the main purpose of a data warehouse?: To integrate large amounts of historical data from multiple sources in a form optimized for analysis and business intelligence.
  • What does subject-oriented and integrated mean for a data warehouse?: Data is organized around major analytical subjects and reconciled into consistent formats from different source systems.
  • What do time-variant, non-volatile, and append-oriented imply?: The warehouse preserves historical time context and is designed mainly to add and analyze data rather than constantly overwrite operational records.
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A3.4.1 + A3.4.2 · Learn

Core knowledge and application

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A3.4.1Outline

The different types of databases as approaches to storing data

Alternative databases and analytical systems trade relational structure for scale, specialized data types, distribution or analytical power.

Exam moveGive a brief, focused account containing the essential points.
Required detail 1Databases models: NoSQL, cloud, spatial, in-memory
Required detail 2Examples of the use of the database model in real-world scenarios may include e-commerce platforms, geographic information systems (GIS), managed services, real-time analytics, social media platforms, SaaS

Explain it without notes

Outline: The different types of databases as approaches to storing data in the context of an organization choosing between relational, NoSQL, spatial, distributed and analytical systems.

  • Databases models: NoSQL, cloud, spatial, in-memory
  • Examples of the use of the database model in real-world scenarios may include e-commerce platforms, geographic information systems (GIS), managed services, real-time analytics, social media platforms, SaaS
A3.4.2Explain

The primary objectives of data warehouses in data management and business intelligence

Alternative databases and analytical systems trade relational structure for scale, specialized data types, distribution or analytical power.

Exam moveGive the mechanism or relationship and make the reasons/causes explicit.
Required detail 1Roles of append-only data, subject-oriented data, integrated data, time-variant data, non-volatile data and data optimized for query performance, to ensure efficient data storage and analysis

Explain it without notes

Explain: The primary objectives of data warehouses in data management and business intelligence in the context of an organization choosing between relational, NoSQL, spatial, distributed and analytical systems.

  • Roles of append-only data, subject-oriented data, integrated data, time-variant data, non-volatile data and data optimized for query performance, to ensure efficient data storage and analysis
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Apply

Transfer to a new scenario

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ScenarioAn organization choosing between relational, nosql, spatial, distributed and analytical systems needs a design or technical decision related to today’s topic. Explain what matters and why.
  • Databases models: NoSQL, cloud, spatial, in-memory
  • Examples of the use of the database model in real-world scenarios may include e-commerce platforms, geographic information systems (GIS), managed services, real-time analytics, social media platforms, SaaS
  • Roles of append-only data, subject-oriented data, integrated data, time-variant data, non-volatile data and data optimized for query performance, to ensure efficient data storage and analysis
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Exam lens

Paper 1 practice

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Build the response before checking notesUse precise terminology and match the required depth.
  1. Outline: The different types of databases as approaches to storing data in the context of an organization choosing between relational, NoSQL, spatial, distributed and analytical systems.
  2. Explain: The primary objectives of data warehouses in data management and business intelligence in the context of an organization choosing between relational, NoSQL, spatial, distributed and analytical systems.
Self-checkAnswer the exact command term. For explain, include mechanism/reason; for compare, pair criteria; for discuss/evaluate/justify, build supported reasoning and a conclusion.
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Homework

Finish the learning cycle

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Exam preparation — main task

Complete targeted 2027 case-study research and cumulative Paper 1/Paper 2 practice. Record evidence and technical vocabulary you can use in extended responses.

Retrieval

Repeat today’s recall deck and revisit any item marked ‘Review again’.

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End of Lesson 48.