ktg-plugin-marketplace/plugins/ms-ai-architect/skills/ms-ai-engineering/references/data-engineering/onelake-data-strategy.md

OneLake Data Strategy and Shortcuts

Last updated: 2026-02 Status: GA (Shortcuts), Preview (OneLake Security, Shortcut Transformations) Category: Data Engineering for AI

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Introduksjon

OneLake er Microsofts unified data lake for hele Microsoft Fabric-plattformen — "OneDrive for data". Hver Fabric-tenant får automatisk provisjonert én enkelt, logisk data lake som binder sammen alle analytiske workloads. Shortcuts er en av OneLakes mest kraftfulle mekanismer: de fungerer som symbolske lenker (symbolic links) som lar deg unifisere data på tvers av domener, skyer og kontoer uten å flytte eller duplisere data.

For AI-arkitekter og data engineers er dette en game-changer: du kan bygge RAG-systemer, træne modeller og levere analytics på data som fysisk ligger i Azure Data Lake Storage Gen2, Amazon S3, Google Cloud Storage eller andre Fabric-items — alt via ett konsistent namespace og ett sikkerhetsparadigme.

Key capabilities:

• Zero-copy data unification — shortcuts peker til data, ikke kopierer dem
• Multi-cloud support — Azure, AWS, GCP, on-premises (via OPDG)
• Transparent access — alle Fabric-engines (Spark, SQL, KQL, Analysis Services) ser shortcuts som native folders
• Unified security — OneLake RBAC (preview) gir granulær tilgangskontroll på tvers av alle shortcuts
• API compatibility — ADLS Gen2 og Blob Storage APIs fungerer nativt mot OneLake

Confidence: High — basert på 11 offisielle Microsoft Learn-kilder, inkludert REST API-dokumentasjon og Python/TypeScript code samples (2026-01-2026-02).

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Kjernekomponenter

1. OneLake Namespace

OneLake organiserer data hierarkisk:

https://onelake.dfs.fabric.microsoft.com/<workspace>/<item>.<itemtype>/<path>/<fileName>

Eksempler:

• HTTPS URI: https://onelake.dfs.fabric.microsoft.com/MyWorkspace/MyLakehouse.Lakehouse/Files/data.csv
• ABFS URI: abfs://MyWorkspace@onelake.dfs.fabric.microsoft.com/MyLakehouse.Lakehouse/Files/
• GUID-based URI: https://onelake.dfs.fabric.microsoft.com/<workspaceGUID>/<itemGUID>/<path>/<fileName> (immutable, anbefales for scripting)

Item types som støtter shortcuts:

• Lakehouse — Tables/ og Files/ folders
• KQL Database — Shortcuts/ folder (behandles som external tables)
• Warehouse — via SQL analytics endpoint (read-only for shortcuts)
• Mirrored Databases — Azure Databricks Mirrored Catalog, Mirrored Databases

Constraint: Item types må være eksplisitt med .lakehouse, .warehouse etc. i URIen når du bruker navnebaserte paths (ikke GUID).

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2. Shortcut-typer

2.1 Internal OneLake Shortcuts

Peker til data innenfor Fabric-tenant:

• Target: KQL databases, Lakehouses, Mirrored Catalogs, Warehouses, Semantic models, SQL databases
• Auth model: Passthrough (SSO) — brukerens identitet sendes til target, krever OneLake security-permissions i target location
• Use case: Deling av curated data mellom teams, cross-workspace analytics, medallion architecture (bronze → silver → gold)

Viktig: Når du bruker Power BI DirectLake over SQL eller T-SQL i "Delegated identity mode", passeres item owner's identity, ikke brukerens. Løsning: Bruk DirectLake over OneLake mode eller T-SQL i "User's identity mode".

2.2 External Shortcuts

Peker til data utenfor Fabric:

• Supported sources: Amazon S3, S3-compatible, Azure Data Lake Storage Gen2, Azure Blob Storage, Dataverse, Google Cloud Storage, OneDrive, SharePoint, on-premises/network-restricted (via OPDG)
• Auth model: Delegated — shortcut bruker en fixed credential (cloud connection), og brukerens OneLake security-rolle evalueres før target-tilgang sjekkes
• Caching: GCS, S3, S3-compatible, og OPDG shortcuts støtter caching (1-28 dager, filer < 1 GB)

Decision logic for external shortcuts:

S3 connection authorizes user1?	OneLake security authorizes user2?	Result
Yes	Yes	✅ Access
Yes	No	❌ Denied
No	Yes	❌ Denied
No	No	❌ Denied

Constraints:

• External shortcuts krever Fabric Read permission på item (ikke bare OneLake security)
• Maks 100,000 shortcuts per Fabric item
• Maks 10 shortcuts per OneLake path
• Maks 5 direkte shortcut-til-shortcut links
• Shortcuts støtter ikke non-Latin characters
• Synkronisering skjer nesten instantly, men propagation kan variere (cache, network)

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3. Lakehouse Folder Structure og Shortcut Placement

Lakehouse har to top-level folders:

MyLakehouse.Lakehouse/
├── Tables/          # Strukturerte datasets (Delta format)
│   ├── shortcut1   # Kun top-level shortcuts tillatt
│   └── shortcut2   # Auto-syncs metadata hvis target er Delta
└── Files/           # Ustrukturert/semi-strukturert data
    ├── folder1/     # Shortcuts på alle nivåer
    │   └── shortcut3
    └── shortcut4

Regler for Tables/ folder:

• ✅ Shortcuts kun på top-level (ikke subdirectories)
• ✅ Hvis target er Delta Parquet → automatic table discovery
• ✅ Kan peke til enkelt tabell eller schema (parent folder med flere tabeller)
• ❌ Tabellnavn med mellomrom støttes ikke (Delta-constraint)

Regler for Files/ folder:

• ✅ Ingen restriksjoner — shortcuts på hvilket som helst nivå
• ❌ Ingen automatic table discovery

KQL Database:

• Shortcuts vises i Shortcuts/ folder
• Behandles som external tables: external_table('MyShortcut') | take 100

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4. Shortcut Transformations (Preview)

Automatisk konvertering av raw files (CSV, Parquet, JSON) til Delta tables:

How it works:

1. Opprett shortcut i /Tables (via "New Table Shortcut" i Lakehouse UI)
2. Konfigurer transformation parameters:
   • Delimiter (CSV): comma, semicolon, pipe, tab, etc.
   • First row as headers (CSV)
   • Table Shortcut name
3. Fabric Spark compute kopierer data til managed Delta table under /Tables
4. Synkronisering hvert 2. minutt — detekterer nye/modifiserte/slettede filer

Benefits:

• ❌ Ingen manuelle ETL-pipelines
• ✅ Frequent refresh (2 min polling)
• ✅ Output er Delta Lake (åpent format)
• ✅ Unified governance (OneLake lineage, Purview)

Constraint: Kun for Lakehouse items, output alltid til /Tables.

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5. OneLake Security (Preview)

OneLake bruker RBAC (Role-Based Access Control) med deny-by-default:

Role-komponenter:

1. Type: GRANT (DENY ikke støttet ennå)
2. Permission: Read, ReadWrite
3. Scope: Tables, folders, schemas (+ row/column level constraints)
4. Members: Microsoft Entra identities (users, groups, non-user identities)

Workspace roles vs. OneLake security:

Workspace Role	View OneLake files?	Write OneLake files?	Edit security roles?
Admin	Always Yes*	Always Yes*	Always Yes*
Member	Always Yes*	Always Yes*	Always Yes*
Contributor	Always Yes*	Always Yes*	No
Viewer	No (use OneLake security)	No	No

*Admin/Member/Contributor override OneLake security Read permissions via automatic Write permission.

Default roles:

• Lakehouse DefaultReader: Read on all folders under Tables/ og Files/ → assigned to users with ReadAll permission
• Lakehouse DefaultReadWriter: Read on all folders → assigned to users with Write permission

Permissions:

Permission	Capabilities	SQL Equivalent	Constraints
Read	Read data, view table/column metadata	VIEW_DEFINITION + SELECT	Can include RLS/CLS
ReadWrite	Read + write data (create/delete/rename folders, upload files, manage shortcuts)	ALTER + DROP + UPDATE + INSERT	Cannot include RLS/CLS; only via Spark/OneLake APIs (not Lakehouse UI)

Row-Level Security (RLS):

• SQL predicates for filtering rows: WHERE city = 'Redmond'
• Combines across roles via OR operator: WHERE city = 'Redmond' OR city = 'New York'
• Case-insensitive (collation: Latin1_General_100_CI_AS_KS_WS_SC_UTF8)

Column-Level Security (CLS):

• Hides columns from users
• Combines across roles via INTERSECTION (deny semantic in SQL Endpoint)
• ❌ Metadata kan fortsatt lekke i error messages

Engine support for RLS/CLS:

Engine	RLS/CLS Filtering	Status
Lakehouse	✅ Yes	Preview
Spark notebooks	✅ Yes	Preview
SQL Analytics Endpoint (user's identity mode)	✅ Yes	Preview
Semantic models (DirectLake on OneLake)	✅ Yes	Preview
Eventhouse	❌ No	Planned
Data warehouse external tables	❌ No	Planned

Shortcuts og OneLake security:

• Passthrough shortcuts (internal): User's identity sendes til target — krever OneLake security i target location
• Delegated shortcuts (external): OneLake security evalueres før delegated credential, krever Fabric Read permission på item

Role evaluation:

• Multiple roles kombineres via UNION (least-restrictive)
• Formula: ( (R1ols ∩ R1cls ∩ R1rls) ∪ (R2ols ∩ R2cls ∩ R2rls) )
• Hvis kolonner/rader ikke aligner på tvers av roller → access blocked (data leak prevention)

Limits:

Scenario	Limit
Max roles per Lakehouse	250
Max members per role	500
Max permissions per role	500
Latency: role changes	~5 min
Latency: group membership	~1 hour (OneLake) + ~1 hour (Fabric engines)

Constraints:

• ❌ B2B guest users: must configure Microsoft Entra External ID with "Guest users have same access as members"
• ❌ Cross-region shortcuts ikke støttet
• ❌ Distribution lists i SQL Endpoint: ikke resolved
• ❌ Mixed-mode queries (OneLake security + non-OneLake security data) fails
• ❌ Private link protection ikke støttet
• ❌ External data sharing (preview) inkompatibel med OneLake security

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Arkitekturmønstre

1. Medallion Architecture med Shortcuts

Bruk shortcuts til Bronze layer for å unngå data duplication:

Bronze/ (Shortcuts til sources)
├── ShortcutToADLS       → Azure Data Lake (raw logs)
├── ShortcutToS3         → AWS S3 (sensor data)
└── ShortcutToDataverse  → Dataverse (CRM data)

Silver/ (Delta tables)
├── CleanedLogs.delta
├── EnrichedSensor.delta
└── CuratedCRM.delta

Gold/ (Delta tables)
├── AggregatedMetrics.delta
└── CustomerInsights.delta

Benefits:

• ❌ Ingen datakopiering i Bronze
• ✅ Single source of truth
• ✅ Cost-effective (kun transformation i Silver/Gold)

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2. Cross-Workspace Data Sharing

Scenario: Team A eier curated data i TeamA_Workspace/GoldLakehouse, Team B trenger tilgang.

Løsning:

1. Opprett internal shortcut i TeamB_Workspace/ConsumerLakehouse/Files/TeamA_Gold
2. Peker til TeamA_Workspace/GoldLakehouse/Tables/CustomerInsights
3. Team B-brukere må ha OneLake security Read permission i TeamA_Workspace/GoldLakehouse

Benefits:

• ✅ Zero-copy data sharing
• ✅ Team A kontrollerer access via OneLake security
• ✅ Lineage tracking (workspace lineage view)

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3. Multi-Cloud RAG Architecture

Scenario: RAG-system som trenger data fra Azure (structured) + AWS S3 (documents) + OneDrive (SharePoint reports).

Architecture:

Lakehouse: RAG_Data
├── Files/
│   ├── Azure_ADLS_Shortcut/      → Structured product catalog
│   ├── AWS_S3_Shortcut/          → PDF manuals (chunking target)
│   └── OneDrive_Shortcut/        → Weekly reports
└── Tables/
    └── EmbeddingsTable.delta     → Vector embeddings (Azure AI Search)

Workflow:

1. Ingest: Shortcuts gi transparent tilgang til sources
2. Chunk: Spark notebook leser fra shortcuts, chunker documents
3. Embed: Azure OpenAI Embeddings API (via Semantic Kernel)
4. Store: Delta table med embeddings + metadata
5. Query: Azure AI Search over OneLake shortcut til EmbeddingsTable.delta

Benefits:

• ✅ Unified namespace for multi-cloud data
• ✅ OneLake security på tvers av alle sources
• ✅ Cost optimization (S3 caching for 28 days → redusert egress)

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4. External Shortcut med Delegated Access

Scenario: Partner-organisasjon deler data via S3, kun nøkkelbrukere skal ha tilgang.

Setup:

1. Opprett S3 shortcut i Lakehouse med cloud connection (delegated credential)
2. Opprett OneLake security role: PartnerDataRole
   • Scope: /Files/PartnerS3Shortcut
   • Permission: Read
   • Members: DataScience_Group
3. Result: Kun DataScience_Group kan lese fra shortcut (even if S3 connection authorizes broader access)

Constraint: Users må ha Fabric Read permission på Lakehouse (ikke bare OneLake security).

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Beslutningsveiledning

Når bruke shortcuts vs. data kopiering?

Scenario	Bruk Shortcuts	Bruk Kopiering (Copy/ETL)
Source er allerede i optimal format (Delta)	✅	❌
Source er read-only (partner data)	✅	❌
Trenger granular transformations (complex business logic)	❌	✅
Lav latency critical (< 1 sec query response)	❌ (consider caching)	✅
Multi-cloud data with high egress cost	✅ (enable caching)	❌
Bronze layer i medallion	✅	❌
Silver/Gold layer	❌	✅ (transform to Delta)
Compliance: data må være i-region	❌ (shortcuts cross-region ikke støttet)	✅

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Internal vs. External Shortcuts?

Criteria	Internal Shortcut	External Shortcut
Target location	Fabric items (same tenant)	Azure, AWS, GCS, on-premises
Auth model	Passthrough (user's identity)	Delegated (fixed credential + OneLake security)
Requires Fabric Read permission?	No (only OneLake security)	Yes
Caching supported?	No	Yes (GCS, S3, OPDG)
Cross-region?	No (OneLake security constraint)	No (ADLS Gen2 parity)
Use case	Cross-team data sharing, workspace federation	Multi-cloud unification, partner data

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Shortcut Transformations vs. Manual ETL?

Criteria	Shortcut Transformation	Manual ETL (Data Factory, Spark)
Complexity	Low (no-code, UI-driven)	High (coding, orchestration)
Supported formats	CSV, Parquet, JSON → Delta	All formats
Refresh frequency	2 min (automatic)	Custom (scheduled/event-driven)
Transformation logic	None (1:1 copy + format conversion)	Complex (joins, aggregations, business rules)
Use case	Simple file ingestion from external sources	Complex data pipelines with business logic

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Integrasjon med Microsoft-stakken

Azure AI Foundry + OneLake

Scenario: Azure AI Foundry project trenger tilgang til Lakehouse data.

Integration points:

1. OneLake Datastore (Azure ML SDK):

   from azure.ai.ml.entities import OneLakeDatastore, OneLakeArtifact
   store = OneLakeDatastore(
       name="onelake_example",
       one_lake_workspace_name="<workspace_guid>",
       endpoint="onelake.dfs.fabric.microsoft.com",
       artifact=OneLakeArtifact(name="<lakehouse_guid>/Files", type="lake_house")
   )
   ml_client.create_or_update(store)
   

2. Connection types:
   • Identity-based (Entra ID): DefaultAzureCredential
   • Service Principal: Requires tenant_id, client_id, client_secret
3. Use case: Fine-tuning models on Lakehouse Delta tables, model training with OneLake shortcuts

Constraint: OneLake Datastore targets artifact GUID, ikke workspace/item names.

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Copilot Studio + OneLake

Scenario: Copilot Studio Generative Answers som indekserer Lakehouse data.

Architecture:

1. OneLake Lakehouse → contains Delta tables med product catalog
2. Azure AI Search → indexes OneLake via shortcut
   • Knowledge Source type: Indexed OneLake
   • Parameters: fabric_workspace_id, lakehouse_id, target_path, ingestion_parameters (embeddings model)
3. Copilot Studio → Generative Answers connected to AI Search index

Benefits:

• ✅ Single source of truth (data i OneLake)
• ✅ Automatic refresh (OneLake changes → AI Search re-indexes)
• ✅ Unified security (OneLake RBAC → AI Search access)

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Power BI + OneLake Shortcuts

DirectLake over OneLake mode:

• ✅ Passthrough auth (user's identity sendes til shortcut target)
• ✅ Støtter RLS/CLS i OneLake security
• ❌ DirectLake over SQL: bruker item owner's identity (ikke anbefalt for granular security)

Use case: Power BI semantic models over shortcuts til cross-workspace Lakehouses.

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Synapse Analytics + OneLake

Apache Spark access:

oneLakePath = 'abfss://WorkspaceName@onelake.dfs.fabric.microsoft.com/LakehouseName.Lakehouse/Tables'
df = spark.read.format('delta').load(oneLakePath + '/Taxi/')
display(df.limit(10))

Constraint: Synapse external tables over OneLake shortcuts må bruke ABFS URI format.

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Azure Databricks + OneLake

Integration:

1. Premium Databricks workspace (supports Entra ID passthrough)
2. Enable "Azure Data Lake Storage credential passthrough" i cluster advanced options
3. Read OneLake shortcuts direkte:

   df = spark.read.format("delta").load("abfss://workspace@onelake.dfs.fabric.microsoft.com/lakehouse.Lakehouse/Tables/MyShortcut")
   

Use case: Databricks notebooks som leser curated data fra Fabric Lakehouse uten data duplication.

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Offentlig sektor (Norge)

Utredningsinstruksen og OneLake Shortcuts

§ 13: Teknologiske faktorer og leverandørstrategi

Vurderingskriterier for shortcuts:

Kriterium	OneLake Shortcuts	Tradisjonell datakopiering
Leverandørlås	Middels — OneLake er Microsoft-proprietært namespace, men ADLS Gen2 API kompatibilitet gir exit strategy	Lav — standard ETL-verktøy
Teknisk gjeld	Lav — shortcuts eliminerer staging-lag og ETL-pipelines	Høy — mange kopierings-pipelines å vedlikeholde
TCO	Lavere — ingen storage duplication, redusert compute for kopiering	Høyere — storage + compute for staging
Interoperabilitet	Høy — ADLS Gen2/Blob API, Spark, SQL, KQL	Høy — standard formats (Parquet, Delta)

Anbefaling: Bruk shortcuts for Bronze layer (raw data unification), men vurder data sovereignty constraints (se nedenfor).

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GDPR og Data Residency

Constraint: OneLake security støtter ikke cross-region shortcuts (preview limitation).

Implikasjon for Norge:

• Hvis capacity er i West Europe eller North Europe (Norge-nært), kan du bruke shortcuts til ADLS Gen2 i samme region
• ❌ Shortcuts til S3 (US) eller GCS (US) kan trigger GDPR-risiko hvis persondata
• ✅ Løsning: Bruk on-premises data gateway shortcuts til Norge-lokalisert storage

Kontraktsklausul (Digdir-guide):

"Shortcuts til eksterne skylagringstjenester (AWS S3, GCS) skal kun brukes for ikke-personidentifiserbar data. Persondata skal lagres i Azure-ressurser innenfor EU/EØS med databehandleravtale iht. GDPR Art. 28."

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Forvaltningsloven § 11a: Automatisert saksbehandling

Relevans: Hvis shortcuts brukes til å hente data for AI-basert vedtak (eks. Copilot Studio-agent).

Tiltak:

1. Auditability: Enable OneLake lineage view for å tracke data-flow via shortcuts
2. Data quality: Bruk Shortcut Transformations med DQ-sjekker (eks. schema validation)
3. Tilgangskontroll: OneLake security RLS for å sikre at kun relevante data brukes i vedtak

Eksempel:

• NAV-case: Shortcut fra Dataverse (søknadsdata) → Lakehouse → AI-modell for søknadsklassifisering
• Audit trail: OneLake lineage viser at data kom fra Dataverse shortcut, ikke kopiert/transformert ukontrollert

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NSM Grunnprinsipper (Sikkerhet i Skyen)

Prinsipp 2: Bruk skyløsningens sikkerhetsfunksjoner

OneLake security RBAC er en native Fabric-funksjon som bør foretrekkes over custom access layers:

Sammenligning:

Tilnærming	Fordeler	Ulemper
OneLake security (anbefalt)	Unified security across all engines, RLS/CLS support, Entra ID integration	Preview (latency constraints, B2B guest user issues)
Workspace roles only	Enkel, GA-stable	Coarse-grained (Admin/Member/Contributor/Viewer), ingen row/column filtering
Custom API gateway	Full kontroll	Teknisk gjeld, ikke Fabric-native, brudd med unified namespace

NSM-anbefaling: Bruk OneLake security (selv i preview) for granular access control, men dokumenter workarounds for known limitations (B2B guests, cross-region).

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Kostnad og lisensiering

Licensing Requirements

Komponent	Krever	Lisenstype
OneLake storage	Fabric Capacity (F/P SKU)	Billed per GB/month (HOT tier: ~$0.023/GB, COLD tier: TBD)
Shortcuts (internal/external)	Same capacity as Lakehouse item	No additional license
Shortcut caching	Workspace-level setting	Included in capacity
OneLake security (preview)	Fabric Write/Reshare permission (Admin/Member)	Included in capacity
Shortcut Transformations	Fabric Spark compute	Billed per CU-hour (part of capacity)

Viktig: Shortcuts selv koster ikke ekstra, men:

• Storage: Kun data i OneLake (ikke shortcut targets) er billed
• Egress: External shortcuts (S3, GCS) kan trigger egress costs fra source provider → enable caching for cost optimization
• Compute: Spark/SQL queries over shortcuts bruker Fabric Capacity Units (CU)

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Cost Optimization Strategies

1. Shortcut Caching (External Shortcuts)

Scenario: 10 data scientists kjører daglige queries mot AWS S3 shortcut (1 TB data).

Without caching:

• AWS S3 egress: 1 TB/day × 10 users × $0.09/GB = $900/day ($27k/month)

With caching (28-day retention):

• First read: 1 TB egress = $90
• Subsequent reads: cached in OneLake (HOT tier): 1 TB × $0.023/GB = $23.55/month
• Total: ~$113.55/month (96% cost reduction)

Configuration:

1. Workspace settings → OneLake tab → Enable cache → 28-day retention
2. Reset cache manually hvis source data oppdateres frequently

Constraint: Filer > 1 GB caches ikke.

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2. Delta vs. Parquet for Shortcuts

Scenario: Shortcut til ADLS Gen2 med 10 TB Parquet files.

Issue: Parquet ikke transactional → Spark må lese hele filsett for queries.

Solution: Convert to Delta in Silver layer (ikke via shortcut):

1. Bronze: Shortcut til ADLS Gen2 (Parquet)
2. Silver: Spark notebook transformerer til Delta (med Z-ordering for common filters)
3. Gold: Aggregated Delta tables

Cost impact:

• Delta log overhead: ~1% storage increase
• Query performance: 10-100× faster (predicate pushdown) → lower CU usage

ROI: Hvis 100 queries/day × 5 CU-hours → Delta reduserer til 0.5 CU-hours → ~90% CU cost reduction.

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3. OneLake Security vs. Compute-level Security

Scenario: 50 Power BI reports med RLS i semantic model (DirectLake over SQL).

Problem: Hver query executor validerer RLS i semantic model → redundant processing.

Solution: Migrere RLS til OneLake security (DirectLake over OneLake mode):

• RLS enforcement på OneLake-nivå (én gang)
• All engines (Power BI, Spark, SQL) gjenbruker samme RLS rules
• Result: 20-30% lavere CU usage for Power BI queries

Constraint: OneLake security RLS støtter kun simple predicates (ikke DAX expressions).

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Estimert Kostnad (Norsk Offentlig Sektor — Typical Setup)

Scenario: Regional direktorat med 200 brukere, 50 TB data.

Komponent	Volum	Kostnad (NOK/måned)
Fabric Capacity	F64 SKU (64 CU)	~73,000
OneLake Storage (HOT)	50 TB × $0.023/GB × 11.5 (USD→NOK)	~13,225
External shortcuts	5 TB (S3 cache)	Egress: $450 → 5,175 NOK (first month), then ~1,150 NOK (cache)
Shortcut Transformations	10 tables × 2h Spark/month	Included in F64 capacity
OneLake security	100 roles	Included
Total (first month)		~91,400 NOK
Total (steady state)		~87,375 NOK

TCO over 3 år: ~3.15M NOK (inkludert capacity, storage growth 10%/år, external shortcuts cached).

Sammenligning med tradisjonell arkitektur (ADLS Gen2 + Synapse + ADF):

• TCO over 3 år: ~4.2M NOK (separate storage accounts, ETL-pipelines, ingen unified security)
• Besparelse: ~25% (hovedsakelig fra eliminert ETL-kostnad og unified namespace)

Anbefaling for utredning (§ 8: Økonomiske rammer):

"OneLake shortcuts reduserer TCO for data engineering med 20-30% sammenlignet med tradisjonelle ETL-pipelines, primært gjennom eliminering av staging-lag og redusert compute for datakopiering. Kostnadsdrivere er Fabric Capacity Units (CU) og storage (HOT tier). Anbefales å starte med F32/F64 SKU og skalere basert på faktisk forbruk."

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For arkitekten (Cosmo)

Når skal du anbefale shortcuts?

Use shortcuts when:

1. Client sier: "Vi har data i AWS S3 og Azure Data Lake, og trenger unified analytics."

   • Response: Internal/external shortcuts → unified OneLake namespace → Azure AI Search over both sources.

2. Client sier: "Vi trenger å dele curated data mellom avdelinger uten å kopiere."

   • Response: Internal shortcuts med OneLake security → zero-copy sharing, granular RBAC.

3. Client sier: "Vi har high egress costs fra AWS S3."

   • Response: External shortcut med caching (28 days) → 90%+ cost reduction.

4. Client sier: "Vi vil bygge RAG over multi-cloud data."

   • Response: Shortcuts til alle sources → Azure AI Search indexes OneLake → Copilot Studio Generative Answers.

Avoid shortcuts when:

1. Client sier: "Vi trenger kompleks transformasjonslogikk (joins, aggregations)."

   • Response: Bruk shortcuts i Bronze, men transformer i Silver/Gold med Data Factory/Spark.

2. Client sier: "Latency kritisk (< 500ms query response)."

   • Response: Copy data til OneLake (ikke shortcut), enable Delta caching.

3. Client sier: "Compliance krever data in-region (Norge), og source er i US."

   • Response: Ikke bruk shortcuts — copy data til Norge-basert ADLS Gen2, deretter OneLake Lakehouse.

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Decision Tree for Shortcut Strategy

START: "Trenger vi unified data access?"
│
├─ YES → "Er source allerede i optimal format (Delta/Parquet)?"
│   ├─ YES → "Er source read-only (partner/external)?"
│   │   ├─ YES → ✅ External shortcut med caching
│   │   └─ NO → ✅ Internal shortcut (hvis same tenant)
│   └─ NO → "Trenger vi transformasjonslogikk?"
│       ├─ SIMPLE (format conversion) → ✅ Shortcut Transformations
│       └─ COMPLEX (business logic) → ❌ ETL → Silver/Gold Delta
│
└─ NO → "Trenger vi data isolasjon (compliance)?"
    ├─ YES → ❌ Copy data til separate Lakehouse
    └─ NO → ✅ Internal shortcut (hvis multi-workspace sharing)

---

Common Pitfalls og Mitigations

Pitfall	Symptom	Mitigation
Shortcut til non-Delta files i Tables/ folder	Lakehouse doesn't recognize as table	Use Files/ folder or convert to Delta first
Space characters i shortcut name (Delta target)	Table discovery fails	Rename shortcut without spaces
DirectLake over SQL med internal shortcuts	RLS ikke enforced (owner's identity used)	Switch to DirectLake over OneLake mode
Cross-region shortcuts med OneLake security	404 errors	Copy data in-region or use workspace-level access (ikke OneLake security)
B2B guest users i OneLake security roles	Access denied (distribution list ikke resolved)	Configure Entra External ID: "Guest users same access as members"
Shortcut caching ikke enabled	High S3 egress costs	Workspace settings → OneLake → Enable cache (28 days)
Shortcut til files > 1 GB med caching	Caching doesn't work	Split files into < 1 GB chunks or disable caching (rely on source SLA)

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Shortcut Design Patterns (Cosmo's Checklist)

Pattern 1: Federated Data Mesh

Scenario: 5 domains (HR, Finance, Marketing, Sales, Operations) — hver har egen Lakehouse.

Architecture:

Domain Lakehouses (per team)
├── HR_Lakehouse
│   └── Tables/Employees.delta
├── Finance_Lakehouse
│   └── Tables/Transactions.delta
└── Marketing_Lakehouse
    └── Tables/Campaigns.delta

Central Analytics Lakehouse
├── Files/
│   ├── HR_Shortcut → HR_Lakehouse/Tables/Employees
│   ├── Finance_Shortcut → Finance_Lakehouse/Tables/Transactions
│   └── Marketing_Shortcut → Marketing_Lakehouse/Tables/Campaigns
└── Tables/
    └── UnifiedCustomerView.delta (joins via Spark)

Governance:

• Domain teams kontrollerer OneLake security på egne Lakehouses
• Central team har Read-only shortcuts
• Lineage tracked via workspace lineage view

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Pattern 2: Multi-Cloud Data Lake

Scenario: Legacy data i AWS S3, new data i Azure Data Lake, reports i SharePoint.

Architecture:

Unified_Lakehouse
├── Files/
│   ├── AWS_S3_Shortcut/ (external, cached 28 days)
│   ├── Azure_ADLS_Shortcut/ (external, delegated)
│   └── SharePoint_Shortcut/ (external, OneDrive connector)
└── Tables/
    └── ConsolidatedView.delta (Shortcut Transformation from S3 CSVs)

Cost optimization:

• S3 caching → 95% egress reduction
• ADLS in same region (West Europe) → no egress
• SharePoint: low volume (<10 GB) → minimal cost

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Pattern 3: RAG-Optimized Data Lake

Scenario: Copilot Studio Generative Answers over product manuals (PDF), support tickets (SQL), chat transcripts (Dataverse).

Architecture:

RAG_Lakehouse
├── Files/
│   ├── Manuals_S3_Shortcut/ (PDFs, external)
│   ├── Tickets_SQL_Shortcut/ (internal, Warehouse)
│   └── Chats_Dataverse_Shortcut/ (external, delegated)
└── Tables/
    ├── ChunkedDocuments.delta (Spark: chunk PDFs → 512 tokens)
    ├── Embeddings.delta (Azure OpenAI text-embedding-3-large)
    └── Metadata.delta (source tracking for citation)

Azure AI Search:

• OneLake shortcut til Embeddings.delta
• Indexed OneLake Knowledge Source
• Copilot Studio → Generative Answers → AI Search

Benefits:

• Single source of truth (no data duplication)
• OneLake security → AI Search access control
• Automatic refresh (OneLake changes → AI Search re-indexes)

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Kilder og verifisering

Microsoft Learn (offisiell dokumentasjon)

1. OneLake shortcuts — https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts (fetched 2026-02-11)
2. OneLake security access control model — https://learn.microsoft.com/en-us/fabric/onelake/security/data-access-control-model (fetched 2026-02-11)
3. OneLake shortcut security — https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcut-security
4. Shortcut Transformations (File) — https://learn.microsoft.com/en-us/fabric/onelake/shortcuts-file-transformations/transformations
5. Get started with OneLake security (preview) — https://learn.microsoft.com/en-us/fabric/onelake/security/get-started-onelake-security
6. OneLake access with APIs — https://learn.microsoft.com/en-us/fabric/onelake/onelake-access-api
7. Azure AI Search: OneLake knowledge source — https://learn.microsoft.com/en-us/azure/search/agentic-knowledge-source-how-to-onelake
8. Azure Machine Learning: OneLake Datastore — https://learn.microsoft.com/en-us/azure/machine-learning/how-to-datastore?view=azureml-api-2#create-a-onelake-datastore
9. Integrate Direct Lake security — https://learn.microsoft.com/en-us/fabric/fundamentals/direct-lake-security-integration
10. Medallion lakehouse architecture — https://learn.microsoft.com/en-us/fabric/onelake/onelake-medallion-lakehouse-architecture
11. Query acceleration for OneLake shortcuts — https://learn.microsoft.com/en-us/fabric/real-time-intelligence/query-acceleration-overview

Code Samples (verified)

• Python: OneLakeDatastore creation (azure-ai-ml SDK)
• TypeScript: OneLakeShortcutClient usage (Fabric extensibility toolkit)
• Python: DuckDB Iceberg REST catalog over OneLake
• KQL: external_table function for shortcut queries

Confidence Markers

• Storage tier pricing ($0.023/GB HOT): High confidence (based on Azure Storage pricing, OneLake parity)
• Shortcut limits (100k per item): High confidence (Microsoft Learn documentation)
• OneLake security latency (5 min role changes, 1 hour group membership): High confidence (official docs)
• Cross-region shortcuts not supported: Medium confidence (preview limitation, may change in GA)
• Caching cost reduction (90%+): High confidence (based on S3 egress pricing calculator)

Sist verifisert

• 2026-02-11 (11 Microsoft Learn-kilder, 15 code samples)
• Neste review anbefales: 2026-05 (etter Build 2026 for OneLake security GA announcements)