gooddata-assessment

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更新时间2026年7月27日 15:53

Take inventory of a GoodData Cloud / GoodData.CN estate and produce a migration-readiness readout — workspace, dataset, metric, insight, and dashboard counts; a MAQL-complexity histogram (which metrics use BY / WITHIN / BY ALL / FOR time transforms); an insight visualization-type mix; and per- dashboard AUTO / HINT / MANUAL / UNHANDLED tags scored against the gooddata-to-sigma converter's actual coverage. Use when a user wants to scope a GoodData→Sigma migration, audit estate sprawl, or pick which dashboards to convert first. Read-only, all-free pre-scoping over the declarative workspace export. Also assesses the legacy GoodData Platform (classic /gdc, SST/TT auth) and sweeps EVERY project a user can access under one domain in a single run.

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twells89

twells89/sigma-migration-skills

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GoodData assessment

Read-only migration-readiness readout for a GoodData Cloud / .CN estate. Pulls the declarative layout (no writes) and scores it against what gooddata-to-sigma can actually convert.

Status: working + live-validated. scripts/assess.py reuses the converter's own MAQL translator for honest coverage scoring (no guessing) and tags each dashboard AUTO / HINT / MANUAL / UNHANDLED.

What it reports

Inventory — workspaces, datasets, metrics, insights, dashboards.
MAQL complexity — histogram of metrics by construct (plain agg / WHERE / BY-WITHIN-BY ALL context / FOR time transforms / ranking), since context + time intel are the conversion-risk drivers (maql-mapping.md).
Visualization mix — insight visualizationUrl histogram, with each type tagged against viz-type-mapping.md (auto-mappable vs flagged).
Per-dashboard tag — AUTO (fully mappable), HINT (minor manual), MANUAL (context MAQL / RLS review), UNHANDLED (exotic widgets / compute-only metrics).
Shortlist — value/cost-ranked migration order.

Usage

GoodData Cloud / .CN (Bearer API token, /api/v1):

eval "$(../gooddata-to-sigma/scripts/get-token.sh)"
python3 scripts/assess.py --workspace <id>     # or --all (all workspaces in ONE org)

Legacy GoodData Platform (classic /gdc, SST/TT auth) — different product, detected by a help.gooddata.com/doc/enterprise / /gdc/... footprint rather than <org>.cloud.gooddata.com:

export GOODDATA_PLATFORM_HOST=https://acme.on.gooddata.com
export GOODDATA_PLATFORM_USER=...  GOODDATA_PLATFORM_PASSWORD=...
python3 scripts/assess_platform.py --all       # EVERY project the user can access — one identity, one sweep
python3 scripts/assess_platform.py --project <pid>

Multi-"instance" note. On the Platform, "separate instances" are projects under one domain, so --all sweeps them all from a single login — this is the answer to "can one API pull across instances?" On Cloud, each org is a separate host+token; there you re-point GOODDATA_HOST/GOODDATA_TOKEN and re-run assess.py --all per org, then merge. Platform scoring reuses the same MAQL translator as Cloud (classic refs are normalized first). See ../gooddata-to-sigma/refs/gooddata-platform-api.md.

Usage telemetry (per-dashboard view counts) is the universal weak spot — it is not in the declarative model; note it as a manual input if needed.

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name	gooddata-assessment
description	Take inventory of a GoodData Cloud / GoodData.CN estate and produce a migration-readiness readout — workspace, dataset, metric, insight, and dashboard counts; a MAQL-complexity histogram (which metrics use BY / WITHIN / BY ALL / FOR time transforms); an insight visualization-type mix; and per- dashboard AUTO / HINT / MANUAL / UNHANDLED tags scored against the gooddata-to-sigma converter's actual coverage. Use when a user wants to scope a GoodData→Sigma migration, audit estate sprawl, or pick which dashboards to convert first. Read-only, all-free pre-scoping over the declarative workspace export. Also assesses the legacy GoodData Platform (classic /gdc, SST/TT auth) and sweeps EVERY project a user can access under one domain in a single run.
user-invocable	true

GoodData assessment

Read-only migration-readiness readout for a GoodData Cloud / .CN estate. Pulls the declarative layout (no writes) and scores it against what gooddata-to-sigma can actually convert.

Status: working + live-validated. scripts/assess.py reuses the converter's own MAQL translator for honest coverage scoring (no guessing) and tags each dashboard AUTO / HINT / MANUAL / UNHANDLED.

What it reports

Inventory — workspaces, datasets, metrics, insights, dashboards.
MAQL complexity — histogram of metrics by construct (plain agg / WHERE / BY-WITHIN-BY ALL context / FOR time transforms / ranking), since context + time intel are the conversion-risk drivers (maql-mapping.md).
Visualization mix — insight visualizationUrl histogram, with each type tagged against viz-type-mapping.md (auto-mappable vs flagged).
Per-dashboard tag — AUTO (fully mappable), HINT (minor manual), MANUAL (context MAQL / RLS review), UNHANDLED (exotic widgets / compute-only metrics).
Shortlist — value/cost-ranked migration order.

Usage

GoodData Cloud / .CN (Bearer API token, /api/v1):

eval "$(../gooddata-to-sigma/scripts/get-token.sh)"
python3 scripts/assess.py --workspace <id>     # or --all (all workspaces in ONE org)

Legacy GoodData Platform (classic /gdc, SST/TT auth) — different product, detected by a help.gooddata.com/doc/enterprise / /gdc/... footprint rather than <org>.cloud.gooddata.com:

export GOODDATA_PLATFORM_HOST=https://acme.on.gooddata.com
export GOODDATA_PLATFORM_USER=...  GOODDATA_PLATFORM_PASSWORD=...
python3 scripts/assess_platform.py --all       # EVERY project the user can access — one identity, one sweep
python3 scripts/assess_platform.py --project <pid>

Multi-"instance" note. On the Platform, "separate instances" are projects under one domain, so --all sweeps them all from a single login — this is the answer to "can one API pull across instances?" On Cloud, each org is a separate host+token; there you re-point GOODDATA_HOST/GOODDATA_TOKEN and re-run assess.py --all per org, then merge. Platform scoring reuses the same MAQL translator as Cloud (classic refs are normalized first). See ../gooddata-to-sigma/refs/gooddata-platform-api.md.

Usage telemetry (per-dashboard view counts) is the universal weak spot — it is not in the declarative model; note it as a manual input if needed.