How the emulator’s surface maps to real Fabric (as documented at
learn.microsoft.com/fabric /
MicrosoftDocs/fabric-docs), and
— the point of this table — whether real work happens or just the API shape.
The emulator’s design bet is that the durable, testable surface is
contracts + storage + identity + orchestration, and those are done for real
(real signed JWTs, real Delta bytes on disk, real RBAC, a real pipeline
interpreter, real cross-engine SQL, real Livy high-concurrency session packing).
The heavyweight or proprietary compute engines are either bring-your-own
(Spark behind the Livy proxy — which is how Fabric itself layers a Livy endpoint
over Spark) or honestly stubbed.
“Real via our own wire-protocol implementation.” A row is 🟢 Real not
only when an external engine/client does the work, but also when the emulator
itself implements Fabric’s wire protocol and the logic behind it — so a real,
unmodified client gets byte- and behaviour-identical responses. Fabric’s
control plane, OneLake’s ADLS/Blob surfaces, the Data Pipeline expression
language + control flow, and the Livy high-concurrency session-packing layer
are all in this category: no engine is being proxied, yet the observable
contract matches real Fabric because we built the protocol, not a mock of it.
Where a row’s execution still needs a heavyweight engine (a REPL’s Spark
statements, a notebook’s cells), that part is split out as 🟠 BYO-engine or 🔴.
| Meaning |
|---|
| 🟢 Real | Genuine work: real signed JWTs, real bytes on disk, a real engine/client computes, real logic enforced — no pretending. |
| 🟡 Emulated | Faithful API contract + persisted state, but no engine — status is clock-derived / management-only. |
| 🟠 Bring-your-own-engine | Real when a real external engine is attached (Spark via the Livy proxy; notebook cells on the Spark sidecar); contract-only (honest 501) otherwise. |
| 🔴 Not implemented | Honest 501 or absent. |
| Fabric feature | Emulator | Type |
|---|
| Workspaces CRUD | Full. Display names are unique tenant-wide — duplicates 409 WorkspaceNameAlreadyExists (uniqueness per the REST reference; fabric-docs covers workspace naming portal-side only) | 🟢 Real |
| Items CRUD + typed collections | Full. Display names are unique per (workspace, type) — duplicates 409 ItemDisplayNameAlreadyInUse; names stay reusable across types, which is why OneLake addresses items as name.Type. The type is validated against the documented ItemType enumeration (50 values, REST reference) — anything else is InvalidItemType, as real Fabric returns — and canonicalised case-insensitively so notebook and Notebook cannot become two types. 24 typed collections alias the generic surface; each collection segment is taken from its own reference page, since they are not derivable (GraphQLApis is capitalised, variableLibraries is not) | 🟢 Real |
| Role assignments / workspace RBAC | Enforced from the validated bearer principal | 🟢 Real |
| Folders | Full | 🟢 Real |
| Capacities (list, assign / unassign) | Full state, no billing/SKU enforcement | 🟢 Real state |
| Long-running operations (202 → poll) | Clock-derived | 🟡 Emulated |
Item job execution (jobs/instances) | Generic items: status clock-derived. DataPipeline jobs really run the interpreter (see Data Factory) and set terminal status from the run | 🟡 Emulated / 🟢 Real (pipelines) |
| Fabric feature | Emulator | Type |
|---|
| Entra OAuth2 tokens / JWKS / client-credentials | entra-emulator mints real signed JWTs | 🟢 Real |
| Workspace managed identity handshake | Provisioned via entra admin API; the identity’s own token passes RBAC | 🟢 Real |
| Key Vault references in connections | Resolved against azure-keyvault-emulator | 🟢 Real |
Governance domains (/v1/admin/domains) | Full admin surface: domain/subdomain CRUD (the hierarchy stops at two levels, as documented), workspace assignment (single-valued — re-assigning moves a workspace), nonEmptyOnly listing, and bulk Admins/Contributors role assignment. Deleting a domain cascades to its subdomains, assignments and roles. No tenant-admin gate: the emulator has no Fabric-administrator role model, so any authenticated principal may call these | 🟢 Real (mgmt); 🟡 tenant-admin gate |
Activity log / audit (GET /v1.0/myorg/admin/activityevents) | Real audit trail — the emulator records events as operations happen, using the documented audit vocabulary (CreateWorkspace, CreateArtifact/UpdateArtifact/DeleteArtifact from admin/operation-list.md; InsertDataDomainAsAdmin & co. with their DataDomainObjectId/FoldersToSetCounter properties from governance/domains-audit-schema.md). Enforces the documented request rules (single-quoted UTC bounds, same-day window) and pages with continuationToken/continuationUri until the token stops coming back. Nothing is synthesised at read time | 🟢 Real |
Tenant settings (GET /v1/admin/tenantsettings) | The documented TenantSetting object in full — settingName, title, enabled, canSpecifySecurityGroups, tenantSettingGroup, the three delegateTo* flags, enabled/excluded security groups (graphId/name), and typed properties validated against the documented TenantSettingPropertyType enum. Optional arrays are omitted, not null, as the reference’s sample does; seeded with the setting names that sample uses. POST /v1/admin/tenantsettings/{name}/update is the real update API (enabled required; response wrapped as {"tenantSettings":[…]}) | 🟢 Real |
Tenant-wide workspace admin (GET /v1/admin/workspaces) | The documented Workspace shape — note it differs from the user-facing surface: the envelope key is workspaces (not value) and the field is name (not displayName). Filters type/state/capacityId/name are enforced, with undocumented enum values returning BadRequest as the reference specifies; domainId is reported from the real domain assignment. Emulator workspaces are always Workspace/Active (no soft delete), so state=Deleted is legitimately empty. No tenant-admin gate — as with the other admin routes | 🟢 Real (mgmt); 🟡 tenant-admin gate |
Tenant-wide item admin (GET /v1/admin/items) | The documented Item shape across every workspace, with workspaceId/capacityId/type/state filters and the reference’s own error codes (InvalidItemType, InvalidItemState). Envelope key is itemEntities — a third spelling after the user-facing value and admin workspaces’ workspaces, which is why each came from its own reference page. Active is the only documented state, so that is all the emulator reports. Fields it does not model (creatorPrincipal, defaultIdentity, tags) are omitted rather than faked | 🟢 Real (mgmt); 🟡 tenant-admin gate |
Capacity tenant-setting overrides (GET /v1/admin/capacities/delegatedTenantSettingOverrides, POST …/{capacityId}/delegatedTenantSettingOverrides/{name}/update) | The documented CapacityTenantSetting (a tenant setting plus delegatedFrom and delegateToWorkspace, and without delegateToCapacity/delegateToDomain). The update body is the documented one — enabled required, no properties, no delegatedFrom — and the response wraps as {"overrides":[…]}. An override may only be created for a setting whose delegateToCapacity is true. Note there is no /v1/admin/capacities list API in Fabric: capacities are listed on the Core surface at /v1/capacities | 🟢 Real (mgmt); 🟡 tenant-admin gate |
Sensitivity labels (bulkSetLabels / bulkRemoveLabels) | The documented admin bulk APIs, reporting per-item successfulItems/failedItems rather than failing whole calls. Every change writes the documented SensitivityLabelEventData to the audit log — SensitivityLabelApplied/Changed/Removed with SensitivityLabelId, OldSensitivityLabelId, ActionSource 3 (Manual), ActionSourceDetail 5 (PublicAPI), ArtifactType 12, and a LabelEventType genuinely computed from label order (upgraded/downgraded/same-order/removed). The label taxonomy is emulator-provided — real Fabric gets labels and their order from Purview, which cannot be attached offline; GET /v1/admin/labels exposes it | 🟢 Real (label APIs + audit) / 🟡 taxonomy |
Purview scanning / classification (governance/) | — | 🔴 Not implemented |
| Lineage (catalog graph) | Via the optional OpenMetadata profile: OneLake shortcut edges and executed pipeline Copy source→sink edges are persisted exactly and witnessed in OM’s graph API. Notebook/Script code is not guessed. Catalog SSO can also be pointed at entra-emulator (22-openmetadata.md) | 🟢 Real (shortcuts + Copy) |
| Fabric feature | Emulator | Type |
|---|
| ADLS Gen2 DFS surface (create → append → flush, ranged read, list) | Full, incl. the x-ms-range dialect | 🟢 Real (real bytes) |
| Blob surface | Full | 🟢 Real |
| Delta commits (put-if-absent atomicity) | Real; -race-tested concurrent-commit race | 🟢 Real |
| Shortcuts (OneLake → OneLake) | Symlinks with target-side RBAC (trusted-workspace-access) | 🟢 Real |
| Shortcuts to external targets (S3 / ADLS Gen2 / Dataverse) | Real Amazon S3 / S3-compatible read-through: a Connection carrying the Access Key Id + Secret Access Key pair — as Basic credentials, because Fabric’s S3 connector uses authentication kind “Access Key” while the REST reference’s CredentialType enum has no AccessKey member and Basic is its only two-secret type — makes the emulator sign upstream requests with AWS SigV4 (internal/awssig, verified against AWS’s own published example signature). Witnessed by e2e/s3 against a real SeaweedFS server started with an identity config — the suite proves an unsigned GET is 403 and that a wrong secret is refused, so the pass cannot be vacuous; the object itself is written by boto3. ADLS Gen2 and plain HTTP(S) endpoints keep the Anonymous/Basic/Key/SAS read-through. Dataverse remains an explicit 501 | 🟢 Real (S3 SigV4 + ADLS reads) / 🔴 Dataverse |
| Fabric feature | Emulator | Type |
|---|
| Lakehouse item + Tables/Files storage | Full (via OneLake) | 🟢 Real |
| Notebook authoring / definition round-trip | Full | 🟢 Real |
notebookutils / mssparkutils (fs, credentials, getSecret, lakehouse, runtime) | Functional stdlib shim (python/notebookutils) | 🟢 Real |
| Spark session / statement / batch via the Livy API | Native termination (--spark-agent-url): the emulator implements the Livy contract and drives a persistent statement-executor agent. The default agent is a PySpark Connect client of Sail, not Apache Spark. An external Livy backend remains configurable with --spark-livy-url | 🟢 protocol / 🟠 Sail execution |
| Notebook cell execution | The emulator parses and records the notebook run, resolves attached lakehouse/Environment metadata, and Sail executes cells by default. The same fixture runs on Spark 3.5 JVM and proves unqualified saveAsTable/spark.table bind to OneLake Tables/ | 🟢 orchestration+binding / 🟠 Sail subset |
| Livy High-Concurrency (5-REPL) sessions | Fabric’s packing layer is implemented directly: sessionTag packing, 5-REPL cap + spill, independent lifecycle and slot reuse. Statements use Sail by default, so engine compatibility is limited to the Spark Connect subset | 🟢 protocol / 🟠 Sail execution |
| Environments | Run binding resolves Python requirements, Spark properties, and JAR declarations. Python packages are provisioned per run; config is applied to the real session; JAR-bearing runs explicitly require JVM Spark | 🟢 portable subset / 🟠 engine-specific JARs |
| Spark Job Definitions | V1 definition/main/arguments/libraries parsing, attached lakehouse+Environment resolution, Pending→Completed/Failed callback lifecycle, and real Sail/JVM execution witness | 🟢 orchestration / 🟠 selected engine |
The engine behind the agent is Sail (Rust
Spark-Connect, no JVM). Every row is probed in CI (e2e/sail), not
inferred — the fidelity deltas a Fabric notebook author actually hits.
A Spark 3.5 JVM image (docker/spark-runtime, Fabric Runtime 1.3’s engine
baseline) exists as a CI compatibility oracle (e2e/spark-jvm), and the
statement agent still has its classic-session path. It is also exposed as a
user-facing overlay — docker compose -f docker-compose.yml -f docker-compose.override.yml -f docker-compose.spark-jvm.yml up swaps the
statement agent onto it — so the JVM-only rows below are graded 🟠
(BYO-engine): real with that overlay attached, unavailable on the default
engine. Verified live, not inferred: sc.parallelize([1,2,3,4]).map(x*2) .sum() returns 20 through the Livy agent, and spark._jvm.io.delta.tables .DeltaTable resolves.
| Notebook pattern | Emulator (Sail) | Type |
|---|
abfss://…@onelake.dfs.fabric.microsoft.com/… production paths | Work unmodified (endpoint override routes the Hadoop URL form) | 🟢 Real |
| Delta write/read/append; SQL over temp views | Full | 🟢 Real |
Time travel option("versionAsOf", n) | Works (SQL VERSION AS OF is a Sail gap) | 🟢 Real / 🔴 SQL form |
MERGE INTO | Works against a registered table target (CREATE TABLE … USING delta LOCATION); path-based delta.`az://…` merge targets don’t resolve | 🟢 Real (registered) / 🔴 path target |
createDataFrame(local_rows) | Works (runners preset localRelationSizeLimit) | 🟢 Real |
sc / RDD API / spark._jvm | Fidelity inversion: works on real Fabric, impossible on Spark Connect — the agent binds sc to a guide-rail stub that raises a clear pointer instead of NameError. Restored by the JVM overlay (classic session): sc.parallelize(…).map(…).sum() verified | 🔴 default / 🟠 JVM overlay |
DML row-count envelopes (INSERT/MERGE counts) | Statement executes; DataFusion’s uint64 count is absorbed as an empty result by the SQL agent | 🟡 Emulated envelope |
| Structured streaming | Partially supported on Sail v0.6.6, and the detail matters: readStream works (verified against the rate source), the streaming query manager works, and the console sink starts — but no durable sink does. memory → No table format found for: memory; parquet/csv → cannot write streaming data to listing table; delta → unsupported extension node for streaming: DeltaWriteNode. Since a streaming job that cannot land data is not useful, this stays 🔴 for real work. Full support on the JVM overlay | 🔴 durable sink / 🟠 JVM overlay |
OPTIMIZE / VACUUM (Delta maintenance) | Not implemented in Sail v0.6.6 (no such commands in its planner). Available on the JVM overlay | 🔴 default / 🟠 JVM overlay |
Java/Scala UDFs, spark.jars | Out of scope for Sail by design, not a technical impossibility. Sail already embeds a foreign runtime — it links CPython via pyo3 (sail-python-udf) for Python UDFs — so a JVM could be embedded too. The real obstacle is that a Spark Java/Scala UDF is compiled against Spark’s own classes (InternalRow, Catalyst encoders), so running one needs Spark’s jars loaded — which is JVM Spark, exactly what Sail exists to avoid. Bytecode translation (GraalVM, TeaVM) does not help: the problem is Spark’s API surface, not executing bytecode. The JVM overlay is the answer | 🔴 default / 🟠 JVM overlay |
CDF options, spark.jars | Accepted but inert on Sail: CDF returns a normal snapshot and JARs have no classloader. The JVM overlay has a real one (verified) | 🔴 default / 🟠 JVM overlay |
| Concurrent Delta overwrite writers | Two independent Connect sessions race at one barrier; one commits and the other receives a transaction failure from the conditional Delta-log create | 🟢 probed conflict rejection |
| Fabric feature | Emulator | Type |
|---|
| SQL-analytics-endpoint semantics over lakehouse Delta | DuckDB runs real SQL (aggregation / join / filter), e2e | 🟢 Real (engine in e2e) |
| Warehouse item management | Full | 🟢 Real |
| T-SQL over TDS + Entra FedAuth | Pure-Go TDS front (internal/tds) terminates the FedAuth handshake (real Entra token, database.windows.net audience), then byte-splices the client’s post-login session to a real per-item SQL Server connection so the engine emits every token itself. Unmodified go-mssqldb and Microsoft ODBC Driver 18 (pyodbc) clients connect and run T-SQL — including RPCs, prepared statements, and transactions. Verified against a real SQL Server; Microsoft’s real dbt-fabric adapter passes debug/seed/run/test end-to-end (e2e/dbt-fabric/) | 🟢 Real (front) / 🟠 SQL Server sidecar |
| Lakehouse SQL analytics endpoint — Delta → engine | The emulator reads the lakehouse’s Tables/<t> Delta in pure Go and reflects (CREATE+INSERT) it into the sidecar on connect, so SELECT hits real OneLake data (matches DuckDB), read-only (writes rejected). Not PolyBase — SQL Server reading Delta in place is a proven dead-end on the Linux container (a throwaway spike; see 16-warehouse-tds.md) | 🟢 Real (reflection) |
| Warehouse — read-write T-SQL | Client CREATE/INSERT/SELECT relay straight to the sidecar; the warehouse owns its data (no reflection) | 🟢 Real (relay) |
Fabric SQL Database (database/) — OLTP + OneLake mirror | Same read-write TDS/FedAuth path (its own SQL Server database), plus mirroring: POST …/sqlDatabases/{id}/refreshMirror snapshots every table to OneLake as Delta (real Parquet + _delta_log), so Spark / DuckDB / delta-rs query the operational data. Verified with a go-mssqldb-writes → mirror → Delta-reads-back e2e (gated). Continuous/CDC mirroring and write-back-to-Delta are the deferred edge | 🟢 Real (snapshot mirror) |
| Per-item isolation (each item = its own SQL Server database) | Lakehouse/Warehouse routed by type; per-item databases so they never collide | 🟢 Real |
| RBAC → SQL permissions | Workspace role enforced on connect: no role → rejected; Viewer → read-only; Contributor+ → read-write (warehouse) | 🟢 Real |
information_schema / sys.* introspection | Relays natively — reflected/warehouse tables are real SQL Server tables | 🟢 Real (relay) |
| Per-column type fidelity (real SQL types over the wire) | The splice forwards SQL Server’s own COLMETADATA, so every column carries its true native type over the wire (the re-encode fallback, used only by fake test backends, synthesizes INTN/FLTN/BITN and falls back to NVARCHAR text) | 🟢 Real (native) |
| Connection by item name (vs GUID) | Workspace read from the server name (<workspace>.datawarehouse.fabric.microsoft.com), item resolved by display name; a GUID still resolves by id (back-compat). Verified with a real go-mssqldb client | 🟢 Real |
| Fabric feature | Emulator | Type |
|---|
Data Pipeline control flow (If / ForEach / Until / Switch / Filter / Fail, expression language, dependsOn) | Pure-Go interpreter that really executes | 🟢 Real (orchestration) |
| Per-activity policy — retry + backoff + timeout | Applied to every activity type: policy.retry re-runs a failed activity (each retry from scratch; only the final outcome is recorded, carrying retryAttempt); policy.retryIntervalInSeconds is folded into the run’s durationInSeconds as virtual backoff; policy.timeout fails an attempt whose own virtual duration exceeds the limit. No real sleeping — backoff and timeouts are exercised in milliseconds on the controllable clock | 🟢 Real |
ForEach sequential / parallel (isSequential, batchCount) | Iterations run in array order (deterministic); the mode sets the reported wall-clock — sequential iterations add, a parallel batch costs its slowest — matching how real Fabric overlaps them | 🟢 Real |
List pagination (continuationToken) | Opt-in via ?maxPageSize on list endpoints (workspaces, items, capacities, folders, connections, role assignments, shortcuts): returns a page + a continuationToken/continuationUri when more remain; omitted → the full set | 🟢 Real |
| Invoke pipeline (ExecutePipeline) | Resolves the referenced DataPipeline (GUID or name, optional other workspace) and runs it through a fresh interpreter — real recursive interpretation, one level deeper on the same engines. waitOnCompletion (default) gates the parent on the child’s terminal status; parameters flow into the child; a cycle or excessive nesting fails loudly | 🟢 Real |
| Pipeline → notebook activity (TridentNotebook) | Resolves the notebook reference and creates a real RunNotebook job instance the pipeline gates on — the pipeline→jobs linkage is real; the notebook’s cells execute only on the Spark sidecar (otherwise the job is clock-derived, like any RunNotebook job) | 🟢 Real chain / 🟠 exec |
queryactivityruns detail | Full | 🟢 Real |
| Activity-level lineage | Successful Copy execution persists its resolved workspace/item/path source→sink edge, returns it in activity output, and exposes workspace lineage for OpenMetadata ingestion | 🟢 Real (Copy) |
| Copy activity — OneLake → OneLake | Really moves the bytes through the storage layer: a file, or a directory subtree preserving structure; source/sink locations {workspaceId?, itemId, path} are expression-resolved (GUID or name); returns real filesWritten / dataWritten. External stores / format transformation are out of scope and fail loudly | 🟢 Real (in-family) / 🔴 external |
| Lookup activity — OneLake CSV/JSON/Parquet/Delta | Reads real rows from a CSV, JSON, or standalone Parquet file, or a lakehouse Delta table (Tables/<name>, auto-detected — no format hint needed) in OneLake; honors firstRowOnly; the result flows into @activity(…).output for downstream steps. Parquet/Delta reuse the warehouse’s own Parquet reader — a real Delta column keeps its native type (int/float/bool), not a stringified cell | 🟢 Real (CSV/JSON/Parquet/Delta) |
| GetMetadata activity — OneLake path | Stats a real OneLake path: exists / itemType / size / lastModified / childItems; a missing path honestly returns exists:false | 🟢 Real |
| Script / SqlServerStoredProcedure activities | Run real T-SQL against a Warehouse/Fabric-SQL-Database item’s own SQL Server database — the same per-item backend the TDS endpoint and the SQLDatabase mirror share. Script runs each scripts[] entry (Query → real rows back, NonQuery → rows affected); SqlServerStoredProcedure calls a real stored procedure with named parameters. The target is named directly as {workspaceId?, itemId} (the emulator’s own scoped mapping — real Fabric’s linkedService/connection reference isn’t modeled), the same shape Copy/Lookup/GetMetadata already use. Honest error without a warehouse SQL backend attached | 🟢 Real (scoped) |
| Web / external-connector leaves | Stubbed success — reached in dependsOn order and inputs resolved, but nothing executes: Web calls to arbitrary URLs would break the offline/deterministic guarantee | 🟡 Emulated |
| Apache Airflow Job | Typed item + beta file APIs; uploaded Python DAGs sync to an opt-in real Airflow 2.10.5/Python 3.12 sidecar, whose scheduler/executor and REST state determine the Fabric job result (e2e/airflow) | 🟢 Real (sidecar) |
| Dataflow Gen2 (Power Query M engine) | Typed item/definition management round-trips. Refresh, Publish, and in-pipeline execution fail with DataflowEngineNotImplemented; no open Power Query M engine exists to attach | 🟡 mgmt / 🔴 exec |
| Connectors / on-prem gateways | — | 🔴 Not implemented |
| Fabric feature | Emulator | Type |
|---|
| Git integration (connect / status / commit / update / disconnect) | Full, real state | 🟢 Real |
fabric-cicd tool publishing | The real client round-trips definitions (e2e) | 🟢 Real |
| Deployment pipelines — model, assignment, item pairing, Deploy Stage Content (D0–D2) | Real promotion: definitions really copy, pairs decide (not names), metadata only — a deployed lakehouse arrives empty — and target-only items survive. 202 LRO + /result detail. 23-deployment-pipelines.md | 🟢 Real |
| Deployment pipelines — role-assignment CRUD (D3) | Add / Delete / List; Admin is the only role a pipeline defines; mutations require Admin, reads require membership | 🟢 Real |
| Fabric area | Emulator | Type |
|---|
Real-Time Intelligence — Eventhouse / KQL Database (real-time-intelligence/) | Full item management (including the default child database an eventhouse creates, and creationPayload.parentEventhouseItemId), plus the Kusto REST protocol on the eventhouse’s published properties.queryServiceUri — /v1/rest/mgmt, /v1/rest/query, /v2/rest/query — terminated by the emulator (Kusto-audience bearer, workspace RBAC, one isolated engine database per Fabric KQL Database) and executed by Microsoft’s own KQL engine container (kustainer) when the rti profile attaches it. No engine attached → honest 501. 25-rti-kusto.md | 🟡 mgmt / 🟠 exec (BYO Kusto engine) |
Real-Time Intelligence — Eventstream (real-time-intelligence/event-streams/) | Item management only. The attached Kusto engine is a query/ingest engine with no streaming ingestion — a streaming pipeline is a different service, deferred with cause | 🟡 mgmt / 🔴 exec |
CopyJob, KQLDashboard, KQLQueryset, Reflex (Data Activator), WarehouseSnapshot | Typed collections over the generic item surface — create/get/list/patch/delete and definition round-trip, with the collection forcing its type. Type names taken from fabric-docs payloads; no execution engine for any of them (a Reflex does not trigger, a Copy Job does not copy) | 🟡 mgmt / 🔴 exec |
Mirroring — Mirrored Database (mirroring/) | POST …/mirroredDatabases/{id}/refreshMirror mirrors an external SQL Server source (reached via a Connection with Basic credentials) to OneLake as real Delta — reusing the exact same mirror writer the Fabric SQL Database uses (warehouse.Mirror; same code, external source). Proven by a gated e2e: a table seeded directly on an external database (bypassing the emulator’s own per-item routing entirely) mirrors and reads back correctly. Snapshot-on-trigger, not continuous/CDC replication; other source engines (Snowflake, CosmosDB, on-prem via gateway) are out of scope | 🟢 Real (snapshot mirror, SQL Server sources) |
Power BI — Semantic Model query (executeQueries) | Real bounded DAX engine — EVALUATE, SUMMARIZECOLUMNS, measures, SUM/DIVIDE, relationship filter propagation — over imported data.json or compatibility-level-1604 Direct Lake entity partitions backed by current OneLake Delta. Proven by the golden DAX/GX suites and the Spark-written Direct Lake witness. | 🟢 Real (DAX subset + Direct Lake) |
| Power BI — Reports / rendering; full DAX; SemPy over XMLA | No report rendering; DAX beyond the fixture subset; and the native ADOMD.NET/XMLA transport SemPy uses (no CI oracle) — all deferred with cause | 🟡 mgmt / 🔴 render |
Data Science — ML models / experiments / MLflow (data-science/) | Authenticated, workspace-scoped proxy to a real MLflow 3 tracking/model-registry server. Experiment/model creation synchronizes typed Fabric items; experiment/run references are isolated by workspace; successful artifact uploads are mirrored under the experiment item’s OneLake Files/mlflow-artifacts. | 🟢 Real (sidecar) |
Graph (graph/), Real-Time Hub, Copilot / IQ (iq/), Embed, Workload Dev Kit | — | 🔴 Not implemented |
| Capability | Purpose |
|---|
Controllable clock (/_emulator/clock) | Advance virtual time to drive LRO / job status transitions deterministically. |
Fault injection (/_emulator/faults, /_emulator/permissions) | Force failures / throttling / RBAC denials to test client resilience. |
| Svelte management portal | Dashboard, workspaces, operations, clock, and fault controls. |
Parity isn’t claimed from our own tests alone — each 🟢 surface is pinned against
the real, unmodified client a Fabric user runs, executed against the emulator
in CI (e2e/<client>/). If Microsoft’s own tool round-trips unchanged, the
contract holds better than any assertion we could write ourselves.
| Real client (pinned) | Surface exercised | Status |
|---|
fabric-cicd (Microsoft) | Control plane / CI-CD publish | 🟢 e2e/fabric-cicd |
Fabric CLI fab (Microsoft) | Control plane — SPN auth (MSAL) + workspace/item CRUD (Notebook, SemanticModel, Report, DataPipeline, Lakehouse), ls/get/api | 🟢 e2e/fabric-cli |
| Fabric Data Engineering VS Code extension 1.18.1 contract (Microsoft) | Shared-backend/MWC authoring routes through api.powerbi.com; interactive kernel websocket is not claimed | 🟢 e2e/vscode-extension |
| Apache Airflow 2.10.5 | ApacheAirflowJob DAG discovery, scheduling, execution, and status | 🟢 e2e/airflow |
| MLflow 3 + dbt-duckdb | Workspace-scoped experiment/run/artifact/model lifecycle, followed by dbt’s real Delta plugin over the same Spark-written OneLake table | 🟢 e2e/data-science-loop |
deltalake (delta-rs) | OneLake Delta write/read | 🟢 e2e/delta-rs |
azure-storage-file-datalake + Blob SDK | OneLake ADLS Gen2 DFS + Blob | 🟢 e2e/adls-sdk |
azcopy (Microsoft) | OneLake Blob multi-block transfer | 🟢 e2e/azcopy |
| DuckDB | Lakehouse SQL over Delta/Parquet | 🟢 e2e/duckdb |
| PySpark behind the Livy API | Spark sessions / statements | 🟢 e2e/spark, e2e/livy, e2e/notebook-run |
notebookutils | Notebook utility shim | 🟢 e2e/notebookutils |
go-mssqldb | Warehouse/Lakehouse TDS + FedAuth | 🟢 internal/server, internal/tds |
dbt-fabricspark (Microsoft) | Fabric Spark via Livy HC sessions | 🟠 e2e/dbt-fabricspark — debug→seed→run→test on Sail |
dbt-fabric (Microsoft) | Warehouse TDS via ODBC Driver 18 | 🟢 e2e/dbt-fabric — debug→seed→run→test through the TDS splice |
azure-kusto-data (Microsoft) + raw Kusto REST, over kustainer (Microsoft’s own KQL engine) | Eventhouse / KQL Database: /v1/rest/mgmt, /v1/rest/query, /v2/rest/query on the published queryServiceUri — create table, ingest, query values back, per-database isolation | 🟠 e2e/rti — witness of record is CI (amd64). The engine needs AVX2, which Rosetta does not provide, so the default Docker setup on Apple silicon cannot run it; a QEMU x86-64 VM with --cpu-type max can, and does (25-rti-kusto.md) |
The TDS surface now has two independent driver witnesses: go-mssqldb and
the Microsoft ODBC Driver 18 (via dbt-fabric). That second driver mattered —
it exposed a real gap: go-mssqldb tolerated a synthesized FedAuth login, but
ODBC Driver 18 took a compatibility path (prepared-statement RPCs +
sp_reset_connection under mandatory connection pooling) that desynced against a
re-encoding relay. The fix was to byte-splice the post-login session straight
to the real SQL Server (so it emits every token itself), which is exactly the
kind of driver-family gap a single-driver test never surfaces. dbt-fabricspark
likewise drives the high-concurrency Livy layer over its real Livy-session
protocol (method: livy, service-principal auth via entra-emulator).
The emulator targets Microsoft Fabric — the convergence/successor product —
not the earlier Azure analytics services Fabric replaced. That boundary is why
some adjacent dbt adapters and Azure surfaces are intentionally not built:
they belong to predecessor (often retired) products, and their Fabric-native
successors are what we emulate instead.
| Adjacent product / client | Why out of scope | Fabric-era equivalent (in scope) |
|---|
Azure Synapse dedicated SQL pool (dbt-synapse) | Different product: its own control plane (Synapse workspaces) and an MPP T-SQL dialect (DISTRIBUTION = HASH, clustered-columnstore / resource-class DDL) that our vanilla SQL Server sidecar rejects. dbt-synapse layers on dbt-fabric, so the shared SQL path is already covered by the dbt-fabric witness | Fabric Warehouse — 🟢 TDS relay |
Azure Data Lake Analytics — U-SQL / SCOPE (dbt-scope) | Retired service (EOL Feb 2024), proprietary batch language, no Fabric embodiment. The only overlap (Delta on a lake) is Spark/OneLake, already witnessed | Fabric Spark — 🟠 Livy |
| ADLS Gen1 | Retired (Feb 2024), superseded by Gen2 | — |
| ADLS Gen2 (standalone storage account) | Not missing — OneLake is the Gen2 endpoint: hierarchical namespace, the dfs filesystem API, onelake.dfs.fabric.microsoft.com. Fabric has no separate storage account to emulate | OneLake — 🟢 e2e/adls-sdk |
Rule of thumb: if a capability exists only in a product Fabric replaced, it’s out
of scope; its Fabric-native successor is what we build. “We already have the
TDS/SQL Server foundation” makes Synapse cheaper, not done — the remaining
delta is a whole MPP dialect plus a second control plane, for a superseded
target. So the two dbt adapters we build (dbt-fabricspark, dbt-fabric) are
exactly the two that hit live Fabric surfaces; the other two (dbt-synapse,
dbt-scope) target predecessor products outside the emulator’s remit.
Real Fabric’s own Livy endpoint is Microsoft’s implementation of the Livy REST
contract over their Spark platform — they honor the protocol, not the retired
Apache Livy server. And where Fabric adds its own layer on top of that
protocol — high-concurrency REPL packing, which a vanilla Livy server has no
concept of — the emulator implements that layer directly rather than proxying,
because there is nothing to proxy it to. That is the same stance throughout: the
protocol and control plane are the durable, real things (built, not mocked,
so real clients can’t tell the difference), and the compute engine is attached
(Spark; T-SQL on SQL Server; KQL on Microsoft’s own Kusto engine —
25-rti-kusto.md) or deferred when proprietary or without an
implementation to attach at all (Dataflow Gen2’s M engine, Power BI rendering,
Eventstream’s streaming ingestion). Every deferral fails loudly rather than
pretending to succeed. See 13-roadmap.md for the milestone
history and the deferred-with-cause rationale.