databricks-lakeflow-connect

name: databricks-lakeflow-connect description: "Build managed ingestion pipelines into Databricks using Lakeflow Connect. Use when ingesting from SaaS apps (Salesforce, Workday Reports, ServiceNow, Google Analytics 4, HubSpot, Confluence) or databases (SQL Server cloud and on-prem; PostgreSQL/MySQL CDC in PuPr) into Unity Catalog with serverless pipelines." compatibility: Requires databricks CLI (>= v0.294.0) metadata: version: "0.1.0" parent: databricks-core

Lakeflow Connect

Build managed ingestion pipelines that pull from SaaS apps and databases into Unity Catalog Delta tables, governed end-to-end and powered by serverless Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables / DLT).

Status: mixed catalog — GA connectors for production use, plus Public Preview, Beta, and Private Preview connectors that expand over time. See the connector catalog below.

What Is Lakeflow Connect?

Managed connectors for ingesting data from SaaS applications and databases. The resulting ingestion pipeline is governed by Unity Catalog and powered by serverless compute and Lakeflow Spark Declarative Pipelines.

Three frames to keep in mind:

• Simple and low-maintenance — no client code to write, no message bus to operate; connector + UC Connection + a serverless pipeline.
• Unified with the lakehouse — credentials stored in UC, output is governed Delta, runs on Jobs and SDP like any other workload.
• Efficient incremental processing — change tracking / CDC / schema evolution / retries are built in.

There are four architecture patterns:

1. SaaS pull — connector reads from an external SaaS via OAuth or API key, lands in a streaming Delta table.
2. Database CDC via gateway — an ingestion gateway runs in the customer's network, stages change events to a UC Volume, a serverless ingestion pipeline applies them as CDC into Delta.
3. Query-based — for sources without native CDC (Oracle / Teradata / SQL Server / PG / MySQL query-based, Snowflake / Redshift / Synapse / BigQuery via Foreign Catalog), the connector issues periodic queries instead of subscribing to a change feed.
4. Community connectors — template-based, out of scope for this skill.

Is Lakeflow Connect the right tool?

Decide this before you build. Lakeflow Connect is the managed pull path for SaaS apps and databases — it is not the answer for every ingestion intent.

Full reasoning, including the Federation-vs-Connect and Auto-Loader-vs-Connect trade-offs, is in 4-ingestion-decision-tree.md.

Connector catalog

Lakeflow Connect ships connectors at multiple release stages. GA and Public Preview connectors are production-supported; Beta and Private Preview are early-access and not production-supported.

GA connectors

Full coverage in this skill.

Public Preview connectors

Production-supported. Configuration may evolve before GA. Deep coverage is being added incrementally; until then, see the public connector reference for current setup steps.

Beta and Private Preview

Early-access connectors are not production-supported. The list changes month to month; check the public connector reference for current availability.

For the Lakeflow-Connect-vs-Auto-Loader-vs-Federation-vs-Delta-Sharing decision, see 4-ingestion-decision-tree.md.

Required Tools

• Databricks CLI v0.294.0+ for databricks pipelines create and databricks connections create. Verify with databricks --version.
• Databricks SDK for Python (databricks-sdk>=0.85.0) if you prefer SDK over CLI.
• Declarative Automation Bundles if authoring as IaC (recommended for any pipeline that ships to a customer environment).

No extra connector-specific SDK is needed. Lakeflow Connect reuses the pipelines API surface — pipelines are created with an ingestion_definition block instead of a libraries block, but the API and CLI are otherwise the same.

Prerequisites

Confirm before creating any pipeline:

1. A Unity Catalog target — catalog and schema must exist; the service principal or user creating the pipeline needs USE CATALOG, USE SCHEMA, CREATE TABLE, and MODIFY on the target schema.
2. A UC CONNECTION object with credentials for the source. SaaS OAuth U2M connections must be created via the UI (Catalog Explorer); API-key and basic-auth connections can be created via CLI / DAB.
3. For database connectors: network reachability between the gateway (classic compute, customer VPC) and the source database. On-prem requires ExpressRoute (Azure) or Direct Connect (AWS).
4. For file connectors: OAuth scope grants on the SaaS file repo (SharePoint / Google Drive).

Minimal Example — Salesforce ingestion pipeline

The canonical authoring path is JSON to databricks pipelines create --json. (There is no SQL CREATE TABLE … FROM CONNECTION syntax for Lakeflow Connect — that syntax exists only for Lakehouse Federation, which is a different product.)

databricks pipelines create --json '{
  "name": "salesforce_to_uc",
  "ingestion_definition": {
    "connection_name": "my_salesforce_oauth_connection",
    "objects": [
      {"table": {"source_schema": "salesforce", "source_table": "Account",
                 "destination_catalog": "main", "destination_schema": "salesforce_raw"}},
      {"table": {"source_schema": "salesforce", "source_table": "Opportunity",
                 "destination_catalog": "main", "destination_schema": "salesforce_raw"}}
    ]
  }
}'

For a DAB-authored version (the production path), see 1-saas-connectors.md.

Running the pipeline

Once authored, deploy and trigger a run. The bundle path gives the cleanest run-by-key command:

databricks bundle deploy -t dev
databricks bundle run salesforce_ingestion           # KEY = the pipeline resource key in the bundle; waits by default
databricks bundle run salesforce_ingestion --no-wait

A pipeline created imperatively with pipelines create --json has no run-by-name CLI — start and poll an update by pipeline ID instead:

databricks pipelines start-update <pipeline-id>             # returns an update_id
databricks pipelines get-update  <pipeline-id> <update-id>  # poll one update's status
databricks pipelines list-updates <pipeline-id>             # recent updates and their states

That asymmetry is one more reason to author with a Declarative Automation Bundle.

Detailed guides

Workflow

For each new ingestion pipeline:

1. Pick the connector category — SaaS / database / file / push — and read the matching reference file.
2. Verify prerequisites — UC target, source credentials, network path (for databases), region availability.
3. Create the UC CONNECTION — UI for OAuth U2M, CLI / DAB for everything else.
4. Author the pipeline — databricks pipelines create --json for one-offs, DAB YAML for anything shipping to a customer.
5. Trigger the first run and watch the event log; see 5-troubleshooting-and-monitoring.md for the SQL.
6. Schedule the triggered pipeline with a Jobs pipeline_task (cron or interval). Lakeflow Connect supports triggered runs only — continuous: false selects triggered mode but is not itself a schedule, so the cadence comes from the Jobs trigger.

Anti-patterns

Three forms that look plausible but fail — wrong vs. right:

1. CREATE TABLE ... FROM CONNECTION is Lakehouse Federation, not Lakeflow Connect.

-- WRONG: Federation syntax; no LFC equivalent exists
CREATE TABLE main.salesforce_raw.account FROM CONNECTION my_salesforce_conn;

// RIGHT: author an ingestion_definition (see the Minimal Example above)
{"ingestion_definition": {"connection_name": "my_salesforce_conn", "objects": [/* ... */]}}

2. An ingestion pipeline carries ingestion_definition, never a libraries block.

// WRONG: libraries is for a standard SDP pipeline running your notebooks/files
{"name": "salesforce_to_uc", "libraries": [{"notebook": {"path": "/Repos/.../ingest"}}]}
// RIGHT:
{"name": "salesforce_to_uc", "ingestion_definition": {"connection_name": "...", "objects": []}}

3. continuous: true is rejected — Lakeflow Connect is triggered-only.

// WRONG: continuous mode fails at create
{"continuous": true, "ingestion_definition": {/* ... */}}
// RIGHT: continuous:false (or omit) + schedule with a Jobs pipeline_task
{"continuous": false, "ingestion_definition": {/* ... */}}

Important

• Triggered only, no continuous mode — pipelines run on a schedule or on-demand, never continuously. Check the connector reference for the latest status.
• Compute-only billing — Lakeflow Connect is billed in DBUs (no per-row fee). Database connectors also incur classic-compute gateway DBUs in addition to the serverless ingestion pipeline DBUs. See the pricing page for current rates.
• Salesforce auth is OAuth U2M only — no machine-to-machine, no basic auth. Connection creation requires a UI walk-through.
• Database staging retention is 30 days by default in the UC Volume between the gateway and the ingestion pipeline.
• Limits per pipeline — most SaaS connectors cap at 250 tables per pipeline. Split across multiple pipelines if needed.
• This lands raw tables — Lakeflow Connect writes source-faithful tables (the ingestion landing zone). Build the medallion Bronze/Silver/Gold transforms on top of them with databricks-pipelines.

Key Concepts

• UC CONNECTION is the credential anchor — every Lakeflow Connect pipeline points at a UC connection. The connection owns the auth; the pipeline references it by name.
• Serverless ingestion pipeline + (optional) classic gateway — SaaS connectors are pure serverless. Database connectors split into a customer-network gateway (classic) and a serverless ingestion pipeline (Delta-bound).
• CDC and schema evolution are built in — for sources that support change tracking or CDC, the connector applies changes incrementally and evolves the target schema. Data-type changes typically require a full snapshot reload.
• Streaming Delta output — destination tables are governed Delta tables; CDC sources are applied with change semantics (APPLY CHANGES / AUTO CDC, or apply_changes_from_snapshot for snapshot sources). Compatible with downstream materialized views and Spark streaming.
• OAuth U2M is UI-only — DAB / CLI cannot bootstrap OAuth U2M connections. Hand the one-time browser step to a human (Catalog Explorer > External Data > Connections > Create connection > pick the source > sign in), then resume once databricks connections get <connection_name> reports READY.

Common Issues

For common errors and their fixes — duplicate-key violations, watermark / cursor problems, schema evolution, gateway region availability, the channel runtime-channel setting, and pipelines that run but land no data — see 5-troubleshooting-and-monitoring.md, which also has the event-log queries to diagnose them.

Related Skills

• databricks-pipelines — the SDP runtime that Lakeflow Connect pipelines run on. For Auto Loader and downstream pipeline patterns.
• databricks-zerobus-ingest — push-based gRPC ingestion. Sibling to Lakeflow Connect's pull-based connectors.
• databricks-dabs — author Lakeflow Connect pipelines as IaC.
• databricks-unity-catalog — managing catalogs, schemas, and the UC CONNECTION objects that LFC credentials live in.
• databricks-jobs — schedule ingestion pipelines with pipeline_task.

Resources

• Lakeflow Connect public docs hub
• Connector reference (per-connector setup)
• Pricing