gooddata/gooddata-python-sdk
Nov 2024 – May 2026
5 Months active
Languages Used
PythonMarkdownYAMLbashyamlMakefileShell
Technical Skills
AI IntegrationAPI IntegrationSDK DevelopmentCLI developmentConfiguration managementPython
Jan Soubusta
PROFILE
Jan Soubusta
Contributed to the gooddata-python-sdk repository by delivering end-to-end features such as AI Lake integration with HyperLogLog support, native Analytics-as-Code YAML tooling, and AI chat capabilities within ComputeService. Leveraged Python, YAML, and Shell scripting to implement typed APIs, automated validation pipelines, and CLI enhancements, while maintaining robust test coverage and documentation. Improved repository hygiene and compliance by refining configuration management, stabilizing test harnesses, and aligning documentation with evolving standards. Addressed cross-version compatibility and streamlined onboarding through AIDA framework upgrades. The work emphasized automation, data modeling, and backend development, resulting in safer data models, faster validation cycles, and maintainable codebases.
Overall Statistics
Feature vs Bugs
69%Features
Repository Contributions
28Total
Bugs
4
Commits
28
Features
9
Lines of code
600,425
Activity Months5
Your Network
87 peopleSame Organization
@gooddata.com51
Adam FiedlerMember
alexole01Member
Andy ChumakMember
Anna BenkeMember
Auto ImplementerMember
GoodData AutomationMember
nknbichMember
Christopher BonillaMember
Dan HomolaMember
Shared Repositories
36
Adam FiedlerMember
alexole01Member
Marcelo G. AlmironMember
Anna BenkeMember
Auto ImplementerMember
GoodData AutomationMember
Christopher BonillaMember
Dan HomolaMember
dmitriinikitingdMember
Work History
May 2026
11 Commits • 2 Features
May 1, 20262026-05 monthly summary for gooddata/gooddata-python-sdk: Delivered end-to-end AI Lake integration with HyperLogLog (HLL) support and auxiliary datasets, enabling robust pre-aggregation analytics and richer data tooling. Implemented typed, long-running AI Lake operations exposed via CatalogAILakeService (analyze_statistics, get_operation, wait_for_operation) with unit tests and staging-ready wiring. Introduced SourceReferenceIdentifier for HLL-backed aggregated facts, allowing references to either facts or attributes; updated catalog/identifier and dataset wiring; unit tests passed. Added HLL_TYPE org setting helpers (set_hll_type/get_hll_type) with idempotent behavior, constants, and tests. Added first-class AUXILIARY dataset support in declarative LDM (type discriminator and relaxed field rules) with end-to-end tests for AAC round-trips and declarative_ldm guards. Expanded documentation for AI Lake surfaces and HLL settings; updated admin docs and API surface descriptions. Stabilized API-client regeneration pipeline: removed NUL-related postprocessing and jq-cycle-workarounds, regened against staging, and repointed make api-client to staging; verified no StackOverflow, no NUL leakage in merged specs; fast, more reliable regen pipeline. Business value: safer data models, more automation, reduced risk in regeneration; CQ-2320 and DX-346.
11 Commits • 2 Features
May 1, 20262026-05 monthly summary for gooddata/gooddata-python-sdk: Delivered end-to-end AI Lake integration with HyperLogLog (HLL) support and auxiliary datasets, enabling robust pre-aggregation analytics and richer data tooling. Implemented typed, long-running AI Lake operations exposed via CatalogAILakeService (analyze_statistics, get_operation, wait_for_operation) with unit tests and staging-ready wiring. Introduced SourceReferenceIdentifier for HLL-backed aggregated facts, allowing references to either facts or attributes; updated catalog/identifier and dataset wiring; unit tests passed. Added HLL_TYPE org setting helpers (set_hll_type/get_hll_type) with idempotent behavior, constants, and tests. Added first-class AUXILIARY dataset support in declarative LDM (type discriminator and relaxed field rules) with end-to-end tests for AAC round-trips and declarative_ldm guards. Expanded documentation for AI Lake surfaces and HLL settings; updated admin docs and API surface descriptions. Stabilized API-client regeneration pipeline: removed NUL-related postprocessing and jq-cycle-workarounds, regened against staging, and repointed make api-client to staging; verified no StackOverflow, no NUL leakage in merged specs; fast, more reliable regen pipeline. Business value: safer data models, more automation, reduced risk in regeneration; CQ-2320 and DX-346.
May 2026
April 2026
5 Commits • 2 Features
Apr 1, 2026April 2026 performance highlights for gooddata/gooddata-python-sdk: native Analytics-as-Code (AAC) YAML support delivered in the Python SDK, automated Python project validation with auto-fix integrated into the AIDA policy, and repository hygiene improvements, alongside WASM visualization/dashboard stability fixes. These efforts reduce dependencies, improve code quality, and accelerate AAC adoption while delivering measurable business value and maintainability.
April 2026
5 Commits • 2 Features
Apr 1, 2026April 2026 performance highlights for gooddata/gooddata-python-sdk: native Analytics-as-Code (AAC) YAML support delivered in the Python SDK, automated Python project validation with auto-fix integrated into the AIDA policy, and repository hygiene improvements, alongside WASM visualization/dashboard stability fixes. These efforts reduce dependencies, improve code quality, and accelerate AAC adoption while delivering measurable business value and maintainability.
March 2026
6 Commits • 2 Features
Mar 1, 2026Month: 2026-03 — Concise monthly summary for the GoodData Python SDK (gooddata/gooddata-python-sdk). Key features delivered: - AIDA framework upgrade and onboarding enhancements: Regenerated onboarding artifacts, updated AIDA rules/policies to align with the latest version, and adopted the newer AIDA framework version with updated preferences. Also untracked generated rule files and updated .gitignore to improve hygiene and reduce drift. Major bugs fixed: - Internal tooling and test stability: Stabilized VCR outputs and ensured deterministic YAML serialization; removed incorrect copyright headers from AIDA templates and updated ignore rules to prevent recurrence. Documentation and standards: - Documentation standards alignment: Updated the pull request body template to align with gdc-aida standards, improving consistency and clarity across contributions. Overall impact and accomplishments: - Improved onboarding experience for users, stronger policy alignment, and reduced CI noise, enabling faster iteration and higher confidence in production releases. - Demonstrated technical proficiency in Python SDK development, AIDA framework management, test tooling stabilization, and maintenance of repository hygiene.
6 Commits • 2 Features
Mar 1, 2026Month: 2026-03 — Concise monthly summary for the GoodData Python SDK (gooddata/gooddata-python-sdk). Key features delivered: - AIDA framework upgrade and onboarding enhancements: Regenerated onboarding artifacts, updated AIDA rules/policies to align with the latest version, and adopted the newer AIDA framework version with updated preferences. Also untracked generated rule files and updated .gitignore to improve hygiene and reduce drift. Major bugs fixed: - Internal tooling and test stability: Stabilized VCR outputs and ensured deterministic YAML serialization; removed incorrect copyright headers from AIDA templates and updated ignore rules to prevent recurrence. Documentation and standards: - Documentation standards alignment: Updated the pull request body template to align with gdc-aida standards, improving consistency and clarity across contributions. Overall impact and accomplishments: - Improved onboarding experience for users, stronger policy alignment, and reduced CI noise, enabling faster iteration and higher confidence in production releases. - Demonstrated technical proficiency in Python SDK development, AIDA framework management, test tooling stabilization, and maintenance of repository hygiene.
March 2026
February 2026
4 Commits • 2 Features
Feb 1, 2026February 2026: Key outcomes for gooddata-python-sdk. Features delivered: migrated package validation to AIDA configuration and extended validation profiles (lint, type-check, Python 3.14 tests) with optional compatibility and full-matrix test commands; and updated copyright to 2026 to align with repository standards. Bug fix: restored the gooddata-dbt CLI entrypoint by directing the console script to dbt_plugin.main and adding a compatibility layer for multi-version support. Impact: stronger packaging quality assurance, broader validation coverage, and smoother cross-version CLI usability, enabling safer releases and faster validation cycles. Technologies: AIDA-based validation routing, Python packaging, CLI tooling, cross-version compatibility, and repo compliance practices. JIRA references: DX-331, DX-339.
February 2026
4 Commits • 2 Features
Feb 1, 2026February 2026: Key outcomes for gooddata-python-sdk. Features delivered: migrated package validation to AIDA configuration and extended validation profiles (lint, type-check, Python 3.14 tests) with optional compatibility and full-matrix test commands; and updated copyright to 2026 to align with repository standards. Bug fix: restored the gooddata-dbt CLI entrypoint by directing the console script to dbt_plugin.main and adding a compatibility layer for multi-version support. Impact: stronger packaging quality assurance, broader validation coverage, and smoother cross-version CLI usability, enabling safer releases and faster validation cycles. Technologies: AIDA-based validation routing, Python packaging, CLI tooling, cross-version compatibility, and repo compliance practices. JIRA references: DX-331, DX-339.
November 2024
2 Commits • 1 Features
Nov 1, 2024November 2024 milestone for the Python SDK: AI Chat Integration in ComputeService with History Management enables interactive AI sessions and persistent chat history within a workspace. The work includes PoC commits to expose AI use cases and a return type update to ChatHistoryResult, establishing the foundation for AI-assisted analytics in the SDK.
2 Commits • 1 Features
Nov 1, 2024November 2024 milestone for the Python SDK: AI Chat Integration in ComputeService with History Management enables interactive AI sessions and persistent chat history within a workspace. The work includes PoC commits to expose AI use cases and a return type update to ChatHistoryResult, establishing the foundation for AI-assisted analytics in the SDK.
November 2024
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Quality Metrics
Correctness93.6%
Maintainability91.4%
Architecture92.8%
Performance90.8%
AI Usage35.6%
Skills & Technologies
Programming Languages
MakefileMarkdownPythonShellYAMLbashyaml
Technical Skills
AI IntegrationAPI DevelopmentAPI IntegrationAPI developmentAPI integrationAPI testingAutomationCLI DevelopmentCLI developmentConfiguration ManagementConfiguration managementData EngineeringData ValidationDevOpsModeling
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline