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Data warehouse ⇄ Analytics

Databricks to Microsoft Power Bi integration — real-time data sync

Keep Databricks and Microsoft Power Bi in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Why teams connect Databricks and Microsoft Power Bi

Flow Microsoft Power Bi data into Databricks in real time — no exports, no schedulers, no custom scripts.

Microsoft Power Bi is a read-only source: Stacksync reads its data in real time and delivers it into Databricks, so Databricks always reflects the current state of Microsoft Power Bi — without exports, scripts, or schedulers.

Microsoft Power Bi is where teams explore, visualize, and report; Databricks is the store of record that holds the raw tables and full history behind those views. The two overlap wherever the same events, users, and metrics matter to both, and when the bridge between them is a nightly export or a hand-built extract, dashboards lag the warehouse and analysts spend the morning arguing over whose number is right.

Common use cases

  • 01 Mirror Workspaces, Reports, Dashboards, and Dataset metadata into a catalog database so teams discover Power BI content from a central index.
  • 02 Pull Dataflow and Datasource definitions to document lineage between Power BI and its upstream warehouses in a governance database.
  • 03 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • 04 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.

Common sync patterns

Shared user and account keys

Users and accounts tracked in Microsoft Power Bi line up with the customer or user rows in Databricks on a stable key, so both sides count the same population.

Corrections propagate instead of reloading

When a record is fixed or backfilled on one side, the change reaches the other without a full reload, keeping history consistent across both.

One number both sides agree on

Metrics and aggregates stay aligned between the two systems, so a figure shown in Microsoft Power Bi matches the Databricks table it was built from instead of drifting between refreshes.

What you can sync between Databricks and Microsoft Power Bi

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

Databricks objects Microsoft Power Bi objects How this pairing syncs
Volumes Unity Catalog file storage used for staging bulk loads. Users and Access Workspace users and roles read via GET /groups/{id}/users or the admin scanner APIs; read for access reviews and identity reconciliation against an IdP. Volumes is specific to Databricks and Users and Access to Microsoft Power Bi — each maps to any object or custom field on the other side.
SQL Warehouses The compute endpoint a sync connects to for query execution. Datasets (Semantic Models) Tabular models behind reports; their rows are read with DAX via POST datasets/{id}/executeQueries, and datasource and refresh metadata via REST list endpoints. SQL Warehouses is specific to Databricks and Datasets (Semantic Models) to Microsoft Power Bi — each maps to any object or custom field on the other side.
Change Data Feed Row-level change records on Delta tables that drive incremental reads. Reports Report definitions and metadata read via GET /reports and per-workspace GET /groups/{id}/reports; enumerated to mirror the content inventory into a catalog. Change Data Feed is specific to Databricks and Reports to Microsoft Power Bi — each maps to any object or custom field on the other side.
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Dashboards and Tiles Dashboards and their pinned tiles read via GET /dashboards and /dashboards/{id}/tiles; inventoried to map dashboard content back to its source datasets. Catalogs is specific to Databricks and Dashboards and Tiles to Microsoft Power Bi — each maps to any object or custom field on the other side.
Schemas Group tables and views; syncs typically target a dedicated schema per source system. Dataflows Power Query ETL definitions read via GET /groups/{id}/dataflows; used to document lineage from upstream sources into datasets. Schemas is specific to Databricks and Dataflows to Microsoft Power Bi — each maps to any object or custom field on the other side.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. Datasources and Gateways The connections behind datasets and dataflows and the on-premises gateways serving them, read via REST for lineage and dependency mapping. Delta Tables is specific to Databricks and Datasources and Gateways to Microsoft Power Bi — each maps to any object or custom field on the other side.

How changes propagate between Databricks and Microsoft Power Bi

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

Databricks Microsoft Power Bi Sub-second propagation

DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.

DeliveryMicrosoft Power Bi does not accept inbound record writes, so this direction carries requests rather than records: Microsoft Power Bi's output flows back as field updates on the originating Databricks records.

Microsoft Power Bi Databricks Interval-based propagation

DetectionStacksync polls Microsoft Power Bi for changes on an incremental schedule, reading only records changed since the previous pass. Pull-based: no webhooks or change-data-capture.

DeliveryEach detected change is applied to Databricks as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
  • Microsoft Power Bi: Endpoints throttle per user and per app and return HTTP 429 with a Retry-After header. Execute Queries allows up to 120 requests per minute per user and caps a query at 100,000 rows (or 1,000,000 values) and 15 MB; admin scanner metadata APIs are limited to about 500 requests per hour with 16 concurrent.
What ships with Databricks ⇄ Microsoft Power Bi

Connect Databricks and Microsoft Power Bi for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Microsoft Power Bi connection.

Real-time

Real-time sync

Changes in Databricks or Microsoft Power Bi instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Microsoft Power Bi data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Databricks or Microsoft Power Bi record.

Observability

Monitoring

Track your Databricks ⇄ Microsoft Power Bi sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Databricks and Microsoft Power Bi.

How the Databricks and Microsoft Power Bi connectors work

Databricks

Integration surface
SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
Authentication
Personal access tokens or OAuth machine-to-machine credentials for service principals
Change detection
Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
Capabilities
read · write · CDC
Rate limits
Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits

Microsoft Power Bi

Integration surface
Power BI REST API, including the Execute Queries (DAX) endpoint and admin scanner APIs; a read-write XMLA endpoint is available on Premium, Premium-Per-User, and Fabric capacity.
Authentication
Microsoft Entra ID (Azure AD) OAuth 2.0 bearer tokens. Supports delegated user tokens and app-only service principals; service principals must be enabled under the tenant's Developer settings and granted workspace access. Tenant-wide admin reads require the Tenant.Read.All scope.
Change detection
Pull-based: no webhooks or change-data-capture. Content is polled via REST list endpoints, dataset changes are inferred from Refresh History and the admin Activity Events audit log, and the scanner API's GetModifiedWorkspaces reports workspaces changed since a given time.
Capabilities
read
Rate limits
Endpoints throttle per user and per app and return HTTP 429 with a Retry-After header. Execute Queries allows up to 120 requests per minute per user and caps a query at 100,000 rows (or 1,000,000 values) and 15 MB; admin scanner metadata APIs are limited to about 500 requests per hour with 16 concurrent.
How it works

How to connect Databricks to Microsoft Power Bi — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate Databricks and Microsoft Power Bi with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Databricks connected
    Microsoft Power Bi connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Databricks and Microsoft Power Bi objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Databricks ⇄ Microsoft Power Bi
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Databricks Microsoft Power Bi
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Databricks and Microsoft Power Bi integration FAQ

SECURITY

Security teams trust Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

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SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 482 integrations available for Databricks and Microsoft Power Bi.

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