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Fast and Thorough: Quality Assurance for Agile Data Warehousing Projects.

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Presentation on theme: "Fast and Thorough: Quality Assurance for Agile Data Warehousing Projects."— Presentation transcript:

1 Fast and Thorough: Quality Assurance for Agile Data Warehousing Projects

2 What?

3 Test Types (1 of 4) Unit – Evaluate the quality of a single developer story – Perhaps a single ETL mapping or a session – Mostly functional, some system meta data Component – Unit-test style verification of an assembly of units, perhaps a workflow Integration – Evaluate the coherence of the full application, as far as it exists to date – Add data scenarios, e.g., nominal, dirty data, missing data, EOP processing System – All the above, but most likely a subset conducted formally as a final certification – Add very technical tests of operational topics

4 Test Types (2 of 4) Functional: Does an object meet its business requirements? Examples: Does it transform a particular set of input as predicted by an example from an SME? Story Tests: Did the product owner accept a user story during the user demo at an iteration’s end? Simulations: Does it transform an entire set of data as predicted by the project’s lead roles? Alpha Tests: Can the team get the app to behave when they take the role of users?

5 Test Types (3 of 4) Scenarios: Consider a compound business situation: Can app support all needs at once? Exploratory Testing: Consider the edges of the business requirements: Other functionality needed? Usability Testing: Will the app sustain the business functions of each “user persona” we intend to support? UAT: Does the app deliver on every case of a structured, business- run, system appraisal? Beta Tests: Does the system perform when real users interact with it and when it’s loaded with realistic data?

6 Test Types (4 of 4) Performance Tests: Does the app have acceptable response times under a normal load? Load Tests: …under the highest conceivable load? Security Tests: Is the system sufficiently resistant to a wide range of techniques to reveal and/or compromise its data? Non-Functional Requirements Tests: Does the application meet a wide range of criteria for long-term inclusion in the department’s IT platform and low total cost of ownership?


8 Target Column Test Types

9 Who?


11 Project Artifact: QA Role & Responsibilities

12 Where?

13 Techniques to Choose From  Unit testing: developer’s standard testing technique  Systems analyst inspection: manual data inspection by systems analyst, esp. matters concerning business rules and source-to-target mappings  Actual-data analytics: scripts, usually of SQL commands, run against actual results  Actual-to-expected result comparisons: usually scripts employing SQL “minus” commands  UAT subset: the appropriate items from the evolving user acceptance script that the solution designer and product owner are accumulating  Full UAT: execution of the full user acceptance test script for the release

14 How?


16 Data-Based Testing Scenarios  Nominal (“happy path”)  Dirty Data (human-visible syntax flaws)  Corrupted Data (machine-visible syntax flaws)  Missing Rows / Tables (e.g., no customers record or file)  Incoherent Data (skipped or mis-sequenced files)  Duplicate Data (e.g., overlap between extracts)  End-of-Period (e.g., month, quarter, year)  Archiving (purging of data that’s too old)  Catch-Up (usually three days per run day)  Restart (test operation instructions)  High-or Full-Volume (performance and 1-in-a million errors)  Resource Outage (FTP node goes down)


18 Where?


20 When?


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