---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: a8b6befba7be667964ad71ac4ce107ebda0131c24d5f25646805c7a223d7842c
  pageDirectory: concepts
  sources:
    - databricks-connect-databricks-on-aws.md
    - databricks-connect-for-databricks-runtime-122-lts-and-below-databricks-on-aws.md
    - databricks-connect-for-python-databricks-on-aws.md
    - databricks-connect-for-scala-databricks-on-aws.md
    - databricks-connect-support-in-databricks-notebooks-databricks-on-aws.md
    - databricks-connect-usage-requirements-databricks-on-aws.md
    - install-databricks-connect-for-scala-databricks-on-aws.md
    - migrate-to-databricks-connect-for-scala-databricks-on-aws.md
    - tutorial-develop-a-databricks-app-locally-with-databricks-connect-databricks-on-aws.md
    - tutorial-run-code-from-pycharm-on-classic-compute-databricks-on-aws.md
    - tutorial-run-python-code-on-serverless-compute-databricks-on-aws.md
  confidence: 0.95
  provenanceState: merged
  freshnessStatus: unverified
  aliases:
    - databricks-connect
    - databricks-connect test
    - Databricks Connect Overview
    - Databricks Connect versions
    - Troubleshoot Databricks Connect
  citations:
    - file: databricks-connect-databricks-on-aws.md
      start: 1
      end: 4
    - file: databricks-connect-databricks-on-aws.md
      start: 11
      end: 13
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      start: 27
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      start: 24
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      start: 7
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    - file: databricks-connect-databricks-on-aws.md
      start: 8
      end: 9
    - file: databricks-connect-usage-requirements-databricks-on-aws.md
      start: 2
      end: 2
    - file: databricks-connect-usage-requirements-databricks-on-aws.md
      start: 8
      end: 8
    - file: compute-configuration-for-databricks-connect-databricks-on-aws.md
      start: 6
      end: 6
    - file: databricks-connect-usage-requirements-databricks-on-aws.md
      start: 2
      end: 10
    - file: compute-configuration-for-databricks-connect-databricks-on-aws.md
      start: 8
      end: 8
    - file: compute-configuration-for-databricks-connect-databricks-on-aws.md
      start: 9
      end: 9
    - file: databricks-connect-for-databricks-runtime-122-lts-and-below-databricks-on-aws.md
      start: 42
      end: 50
    - file: compute-configuration-for-databricks-connect-databricks-on-aws.md
      start: 12
      end: 18
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    - file: compute-configuration-for-databricks-connect-databricks-on-aws.md
      start: 95
      end: 95
    - file: compute-configuration-for-databricks-connect-databricks-on-aws.md
      start: 99
      end: 99
    - file: databricks-connect-support-in-databricks-notebooks-databricks-on-aws.md
      start: 8
      end: 12
    - file: databricks-connect-support-in-databricks-notebooks-databricks-on-aws.md
      start: 16
      end: 20
    - file: compute-configuration-for-databricks-connect-databricks-on-aws.md
      start: 103
      end: 103
    - file: databricks-connect-for-databricks-runtime-122-lts-and-below-databricks-on-aws.md
      start: 187
      end: 187
    - file: limitations-with-databricks-connect-for-python-databricks-on-aws.md
      start: 6
      end: 6
    - file: limitations-with-databricks-connect-for-python-databricks-on-aws.md
      start: 14
      end: 14
    - file: limitations-with-databricks-connect-for-python-databricks-on-aws.md
      start: 17
      end: 17
    - file: limitations-with-databricks-connect-for-python-databricks-on-aws.md
      start: 18
      end: 18
    - file: limitations-with-databricks-connect-for-python-databricks-on-aws.md
      start: 13
      end: 13
    - file: limitations-with-databricks-connect-for-python-databricks-on-aws.md
      start: 12
      end: 12
    - file: limitations-with-databricks-connect-for-python-databricks-on-aws.md
      start: 15
      end: 15
    - file: databricks-connect-databricks-on-aws.md
      start: 29
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    - file: databricks-connect-databricks-on-aws.md
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    - file: databricks-connect-for-databricks-runtime-122-lts-and-below-databricks-on-aws.md
      start: 111
      end: 111
    - file: databricks-connect-for-databricks-runtime-122-lts-and-below-databricks-on-aws.md
      start: 117
      end: 117
title: Databricks Connect
description: A client library for the Databricks Runtime that enables remote Spark execution from IDEs, notebooks, and custom applications using Spark Connect.
tags:
  - databricks
  - client-library
  - remote-execution
timestamp: "2026-06-19T18:08:59.272Z"
---

---
title: Databricks Connect
summary: A client library that enables remote connectivity to Databricks compute from IDEs, notebooks, and custom applications using Spark APIs.
sources:
  - databricks-connect-databricks-on-aws.md
  - databricks-connect-for-databricks-runtime-122-lts-and-below-databricks-on-aws.md
  - databricks-connect-for-python-databricks-on-aws.md
  - databricks-connect-for-scala-databricks-on-aws.md
  - databricks-connect-support-in-databricks-notebooks-databricks-on-aws.md
  - databricks-connect-usage-requirements-databricks-on-aws.md
  - install-databricks-connect-for-python-databricks-on-aws.md
  - migrate-to-databricks-connect-for-python-databricks-on-aws.md
  - tutorial-develop-a-databricks-app-locally-with-databricks-connect-databricks-on-aws.md
  - tutorial-run-code-from-pycharm-on-classic-compute-databricks-on-aws.md
kind: concept
createdAt: "2026-06-18T11:04:51.180Z"
updatedAt: "2026-06-19T14:45:05.813Z"
tags:
  - databricks
  - client-library
  - remote-compute
  - spark
aliases:
  - databricks-connect
confidence: 0.95
provenanceState: merged
inferredParagraphs: 2
---

# Databricks Connect

**Databricks Connect** is a client library for the Databricks Runtime that enables you to connect popular IDEs—such as Visual Studio Code, PyCharm, RStudio Desktop, IntelliJ IDEA—notebook servers, and other custom applications to Databricks compute. ^[databricks-connect-databricks-on-aws.md:1-4]

## What can I do with Databricks Connect?

Using Databricks Connect, you can write code using Spark APIs and run them remotely on Databricks compute instead of in a local Spark session. ^[databricks-connect-databricks-on-aws.md:11-13]

- **Interactively develop and debug from any IDE.** Databricks Connect enables developers to develop and debug their code on Databricks compute using any IDE's native running and debugging functionality. The [Databricks Visual Studio Code Extension](/concepts/databricks-visual-studio-code-extension.md) uses Databricks Connect to provide built-in debugging of user code on Databricks. ^[databricks-connect-databricks-on-aws.md:15-16]
- **Build interactive data apps.** Just like a JDBC driver, the Databricks Connect library can be embedded in any application to interact with Databricks. It provides the full expressiveness of Python through PySpark, eliminating SQL programming language impedance mismatch and enabling you to run all data transformations with Spark on Databricks serverless scalable compute. ^[databricks-connect-databricks-on-aws.md:18-19]

## How does it work?

Databricks Connect is built on open-source [Spark Connect](/concepts/spark-connect.md), which has a decoupled client-server architecture for Apache Spark that allows remote connectivity to Spark clusters using the DataFrame API. The underlying protocol uses Spark unresolved logical plans and Apache Arrow on top of gRPC. ^[databricks-connect-databricks-on-aws.md:22-22]

**Code execution model:**

- **General code runs locally:** Python and Scala code runs on the client side, enabling interactive debugging. All code is executed locally, while all Spark code continues to run on the remote cluster. ^[databricks-connect-databricks-on-aws.md:26-26]
- **DataFrame APIs are executed on Databricks compute.** All data transformations are converted to Spark plans and run on the Databricks compute through the remote Spark session. They are materialized on your local client when you use commands such as `collect()`, `show()`, `toPandas()`. ^[databricks-connect-databricks-on-aws.md:27-27]
- **UDF code runs on Databricks compute:** UDFs defined locally are serialized and transmitted to the cluster where they run. APIs that run user code on Databricks include: UDFs, `foreach`, `foreachBatch`, and `transformWithState`. ^[databricks-connect-databricks-on-aws.md:28-28]
- **Dependency management:** Install application dependencies on your local machine (they run locally), and install UDF dependencies on Databricks. ^[databricks-connect-databricks-on-aws.md:29-30]

For Databricks Runtime 13.3 LTS and above, Databricks Connect is an extension of Spark Connect with additions and modifications to support working with Databricks compute modes and [Unity Catalog](/concepts/unity-catalog.md). ^[databricks-connect-databricks-on-aws.md:24-24]

## Supported languages

Databricks Connect is available for:

- [Databricks Connect for Python](/concepts/databricks-connect-for-python.md) — for use with Python-based IDEs and applications ^[databricks-connect-databricks-on-aws.md:6-6]
- [Databricks Connect for R](/concepts/databricks-connect-for-r.md) — for RStudio Desktop and other R-based environments ^[databricks-connect-databricks-on-aws.md:7-7]
- [Databricks Connect for Scala](/concepts/databricks-connect-for-scala.md) — for IntelliJ IDEA and other Scala/Java-based development environments. ^[databricks-connect-databricks-on-aws.md:8-9]

## Setup and requirements

Before you begin, you need the following:

- A Databricks workspace with Unity Catalog enabled. See Get started with Unity Catalog. ^[databricks-connect-usage-requirements-databricks-on-aws.md:2-2]
- Python 3 installed on your local development machine with a compatible minor version (the minor version must match the cluster's Python version for UDFs). ^[databricks-connect-usage-requirements-databricks-on-aws.md:8-8]
- Databricks Connect installed. ^[compute-configuration-for-databricks-connect-databricks-on-aws.md:6-6, databricks-connect-usage-requirements-databricks-on-aws.md:2-10]
- The Databricks workspace instance name (Server Hostname) for your compute. ^[compute-configuration-for-databricks-connect-databricks-on-aws.md:8-8]
- If connecting to classic compute, the cluster ID. ^[compute-configuration-for-databricks-connect-databricks-on-aws.md:9-9]

For Databricks Runtime 12.2 LTS and below, additional requirements include a Java Runtime Environment (JRE) 8 and that the Databricks Connect major/minor version exactly matches the cluster's Databricks Runtime version. ^[databricks-connect-for-databricks-runtime-122-lts-and-below-databricks-on-aws.md:42-50]

## Configuring a connection

Databricks Connect searches for configuration properties in the following order and uses the first configuration it finds: ^[compute-configuration-for-databricks-connect-databricks-on-aws.md:12-18]

1. The `DatabricksSession` class's `remote()` method
2. A Databricks configuration profile
3. The `DATABRICKS_CONFIG_PROFILE` environment variable
4. An environment variable for each configuration property
5. A Databricks configuration profile named `DEFAULT`

### Configuring to a cluster

For connecting to a classic cluster, you can use:

- **Configuration profiles:** Create a profile with `host`, `token`, and `cluster_id` fields. Then reference it from your code: ^[compute-configuration-for-databricks-connect-databricks-on-aws.md:33-33]

```python
from databricks.connect import [[databrickssession|DatabricksSession]]
spark = [[databrickssession|DatabricksSession]].builder.profile("<profile-name>").getOrCreate()
```

- **Environment variables:** Set `DATABRICKS_HOST`, `DATABRICKS_TOKEN`, and `DATABRICKS_CLUSTER_ID`. ^[compute-configuration-for-databricks-connect-databricks-on-aws.md:67-67]

- **Direct configuration:** Specify parameters in code using `remote()`: ^[compute-configuration-for-databricks-connect-databricks-on-aws.md:20-20]

```python
from databricks.connect import [[databrickssession|DatabricksSession]]
spark = [[databrickssession|DatabricksSession]].builder.remote(
    host="https://<workspace-url>",
    token="<token>",
    cluster_id="<cluster-id>"
).getOrCreate()
```

### Configuring to serverless compute

Databricks Connect for Python and Scala support connecting to serverless compute. To use this feature: ^[compute-configuration-for-databricks-connect-databricks-on-aws.md:79-79]

- Set the environment variable `DATABRICKS_SERVERLESS_COMPUTE_ID` to `auto`. ^[compute-configuration-for-databricks-connect-databricks-on-aws.md:81-81]
- Or use `DatabricksSession.builder.serverless().getOrCreate()`. ^[compute-configuration-for-databricks-connect-databricks-on-aws.md:87-87]

### Validating the connection

Run the `databricks-connect test` command to validate that your environment, default credentials, and connection to compute are correctly set up: ^[compute-configuration-for-databricks-connect-databricks-on-aws.md:95-95]

In Databricks Connect 14.3 and above, you can also validate using `validateSession()`: ^[compute-configuration-for-databricks-connect-databricks-on-aws.md:99-99]

```python
[[databrickssession|DatabricksSession]].builder.validateSession(True).getOrCreate()
```

## [DatabricksSession](/concepts/databrickssession.md) behavior

- **In a local development environment:** `DatabricksSession.builder.getOrCreate()` gets the existing Spark session for the provided configuration if it exists, or creates a new one. `DatabricksSession.builder.create()` always creates a new Spark session. ^[databricks-connect-support-in-databricks-notebooks-databricks-on-aws.md:8-12]
- **In Databricks notebooks:** `DatabricksSession.builder.getOrCreate()` returns the default Spark session (accessible through the `spark` variable). A new Spark session is created if additional connection parameters are set. ^[databricks-connect-support-in-databricks-notebooks-databricks-on-aws.md:16-20]

## Disabling Databricks Connect

Databricks Connect (and the underlying Spark Connect) services can be disabled on any given cluster by setting: ^[compute-configuration-for-databricks-connect-databricks-on-aws.md:103-103]

```spark
spark.databricks.service.server.enabled false
```

## Limitations

- [Unity Catalog](/concepts/unity-catalog.md) is required ^[databricks-connect-usage-requirements-databricks-on-aws.md:2-2]
- [Structured Streaming](/concepts/structured-streaming-on-shared-tables.md) is not supported ^[databricks-connect-for-databricks-runtime-122-lts-and-below-databricks-on-aws.md:187-187]
- RDDs and Spark Context are not supported ^[databricks-connect-for-databricks-runtime-122-lts-and-below-databricks-on-aws.md:187-187]
- Creating DataFrames with an unresolved logical plan larger than 128 MB is not supported ^[limitations-with-databricks-connect-for-python-databricks-on-aws.md:6-6]
- `CREATE TABLE <table-name> AS SELECT` does not work reliably; use `spark.sql("SELECT ...").write.saveAsTable("table")` instead ^[limitations-with-databricks-connect-for-python-databricks-on-aws.md:14-14]
- Distributed ML training is not supported ^[limitations-with-databricks-connect-for-python-databricks-on-aws.md:17-17]
- Synchronizing the local development environment with the remote cluster is not supported ^[limitations-with-databricks-connect-for-python-databricks-on-aws.md:18-18]
- The following Databricks Utilities are not available: `credentials`, `library`, `notebook workflow`, `widgets` ^[limitations-with-databricks-connect-for-python-databricks-on-aws.md:13-13]
- `dataframe.display()` API is not available ^[limitations-with-databricks-connect-for-python-databricks-on-aws.md:12-12]
- Libraries that use RDDs, Spark Context, or access the underlying Spark JVM (such as Mosaic geospatial, GraphFrames, or GreatExpectations) are not supported ^[limitations-with-databricks-connect-for-python-databricks-on-aws.md:15-15]

## Data dependencies

- **Install application dependencies on your local machine** — these run locally and need to be installed as part of your project (such as in your Python virtual environment). ^[databricks-connect-databricks-on-aws.md:29-29]
- **Install UDF dependencies on Databricks** — see Manage UDF dependencies. ^[databricks-connect-databricks-on-aws.md:30-30]

## Access Databricks Utilities

You can use `dbutils.fs` and `dbutils.secrets` utilities from the Databricks Utilities module. Supported commands include `dbutils.fs.cp`, `dbutils.fs.head`, `dbutils.fs.ls`, `dbutils.fs.mkdirs`, `dbutils.fs.mv`, `dbutils.fs.put`, `dbutils.fs.rm`, `dbutils.secrets.get`, `dbutils.secrets.getBytes`, `dbutils.secrets.list`, and `dbutils.secrets.listScopes`. ^[databricks-connect-for-databricks-runtime-122-lts-and-below-databricks-on-aws.md:111-111]

To access `dbutils` in a way that works both locally and in Databricks clusters: ^[databricks-connect-for-databricks-runtime-122-lts-and-below-databricks-on-aws.md:117-117]

```python
def get_dbutils(spark):
  from pyspark.dbutils import DBUtils
  return DBUtils(spark)
```

## Related concepts

- [Spark Connect](/concepts/spark-connect.md) — The open-source gRPC-based protocol that Databricks Connect extends
- [Unity Catalog](/concepts/unity-catalog.md) — Required for Databricks Connect workspace connectivity
- Databricks Runtime — The compute environment that runs the remote Spark session
- [OAuth U2M authentication](/concepts/user-to-machine-u2m-authentication.md) — Supported authentication method for serverless compute
- [Configuration profiles](/concepts/databricks-configuration-profiles.md) — Used to store connection parameters for Databricks Connect
- [databricks-connect test](/concepts/databricks-connect.md) — CLI command to validate the connection to Databricks compute

## Sources

- databricks-connect-databricks-on-aws.md
- compute-configuration-for-databricks-connect-databricks-on-aws.md
- databricks-connect-usage-requirements-databricks-on-aws.md
- databricks-connect-for-databricks-runtime-122-lts-and-below-databricks-on-aws.md
- limitations-with-databricks-connect-for-python-databricks-on-aws.md
- databricks-connect-support-in-databricks-notebooks-databricks-on-aws.md
- tutorial-develop-a-databricks-app-locally-with-databricks-connect-databricks-on-aws.md
- code-examples-for-databricks-connect-for-python-databricks-on-aws.md
- install-databricks-connect-for-python-databricks-on-aws.md
- migrate-to-databricks-connect-for-python-databricks-on-aws.md
- tutorial-run-code-from-pycharm-on-classic-compute-databricks-on-aws.md

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