Skip to main content

Connect From Anywhere to Databricks SQL

Announcing open-source Go, Node.js, Python, and CLI connectors to Databricks SQL
Share this post

Today we are thrilled to announce a full lineup of open source connectors for Go, Node.js, Python, as well as a new CLI that makes it simple for developers to connect to Databricks SQL from any application of their choice. Along the same theme of empowering developers, we have also published the official Databricks JDBC driver on the Maven central repository, making it possible to use it in your build system and confidently package it with your applications.

Databricks SQL connectors: connect from anywhere and  build data apps powered by your lakehouse Databricks SQL connectors: connect from anywhere and
build data apps powered by your lakehouse

Since its GA earlier this year, the Databricks SQL Connector for Python has seen tremendous adoption from our developer community, averaging over 1 million downloads a month. We are excited to announce that the connector is now completely open source.

We would like to thank the contributors to the open source projects that provided the basis for our new Databricks SQL connectors. We invite the community to join us on GitHub and collaborate on the future of data connectivity.

Databricks SQL Go Driver

Go is a popular open source language commonly used for building reliable cloud and network services and web applications. Our open source driver implements the idiomatic database/sql standard for database access.

Here’s a quick example of how to submit SQL queries to Databricks from Go:

    package main
    import (
        _ ""
    // replace these values
    const (
        token = "dapi***********"
        hostname = "********"
        path = "/sql/1.0/endpoints/*******"
    func main() {
        dsn := fmt.Sprintf("databricks://:%s@%s%s", token, hostname, path)
        db, err := sql.Open("databricks", dsn)
        if err != nil {
            log.Fatalf("Could not connect to %s: %s", dsn, err)
        defer db.Close()
        db.Query("CREATE TABLE example (id INT, text VARCHAR(20))")
        db.Query("INSERT INTO example VALUES (1, \"Hello\"), (2, \"World\")")
        rows, err := db.Query("SELECT * FROM example")
        if err != nil {
        for rows.Next() {
            var text string
            var id int
            if err := rows.Scan(&id, &text); err != nil {
          fmt.Printf("%d %s\n", id, text)


    1 Hello
    2 World

You can find additional examples in the examples folder of the repo. We are looking forward to the community’s contributions and feedback on GitHub.

Databricks SQL Node.js Driver

Node.js is very popular for building services in JavaScript and TypeScript. The native Node.js driver, written entirely in TypeScript with minimum external dependencies, supports the async/await pattern for idiomatic, non-blocking operations. It can be installed using NPM (Node.js 14+):

$ npm i @databricks/sql

Here is a quick example to create a table, insert data, and query data:

    const { DBSQLClient } = require('@databricks/sql');
    // replace these values
    const host = '********';
    const path = '/sql/1.0/endpoints/*******';
    const token = 'dapi***********';
    async function execute(session, statement) {
      const utils = DBSQLClient.utils;
      const operation = await session.executeStatement(statement, { runAsync: true });
      await utils.waitUntilReady(operation);
      await utils.fetchAll(operation);
      await operation.close();
      return utils.getResult(operation).getValue();
    const client = new DBSQLClient();
    client.connect({ host, path, token }).then(async client => {
      const session = await client.openSession();
      await execute(session, 'CREATE TABLE example (id INT, text VARCHAR(20))');
      await execute(session, 'INSERT INTO example VALUES (1, "Hello"), (2, "World")');
      const result = await execute(session, 'SELECT * FROM example');
      await session.close();
    }).catch(error => {


id │  text    │
1'Hello'2'World'    └────┴─────────┘

The driver also provides direct APIs to get table metadata such as getColumns. You can find more samples in the repo. We are looking forward to the Node.js community’s feedback.

Databricks SQL CLI

Databricks SQL CLI is a new command line interface (CLI) for issuing SQL queries and performing all SQL operations.As it is built on the popular open source DBCLI package, it supports auto-completion and syntax highlighting. The CLI supports both interactive querying as well as the ability to run SQL files.You can install it using pip (Python 3.7+).

python3 -m pip install databricks-sql-cli

To connect, you can provide the hostname, HTTP path, and PAT as command line arguments like below, by setting environment variables, or by writing them into the [credentials] section of the config file.

$ dbsqlcli --hostname '********' --http-path '/sql/1.0/endpoints/*******' --access-token 'dapi***********'

You can now run dbsqlcli from your terminal, with a query string or .sql file.

$ dbsqlcli -e 'SELECT * FROM samples.nyctaxi.trips LIMIT 10'
$ dbsqlcli -e query.sql
$ dbsqlcli -e query.sql > output.csv

Use --help or check the repo for more documentation and examples.

Databricks JDBC Driver on Maven

Java and JVM developers use JDBC as a standard API for accessing databases. Databricks JDBC Driver is now available on the Maven Central repository, letting you use this driver in your build system and CI/CD runs. To include it in your Java project, add the following entry to your application’s pom.xml:


Here is some sample code to query data using JDBC driver:

    import java.sql.*;
    public static void main(String[] args) throws Exception {
        // Open a connection
        // replace the values below
        String token = "dapi*****";
        String url = "jdbc:databricks://********;" +            "transportMode=http;ssl=1;AuthMech=3;httpPath=sql/protocolv1/o/*****;" +
                "UID=token;" +
                "PWD=" + token;
        try (Connection conn = DriverManager.getConnection(url);
             Statement stmt = conn.createStatement();
             ResultSet rs = stmt.executeQuery("SELECT * FROM samples.nyctaxi.trips");) {
            // Extract data from result set
            while ( {
                // Retrieve by column name
                System.out.print("ID: " + rs.getString("col_name"));

Connect to the Lakehouse from Anywhere

With these additions, Databricks SQL now has native connectivity to Python, Go, Node.js, the CLI, ODBC/JDBC, as well as a new SQL Execution REST API that is in Private Preview. We have exciting upcoming features on the roadmap including: additional authentication schemes, support for Unity Catalog, support for SQLAlchemy, and performance improvements. We can’t wait to see all the great data applications that our partner and developer communities will build with Databricks SQL.

The best data warehouse is a Lakehouse. We are excited to enable everybody to connect to the lakehouse from anywhere! Please try out the connectors, and we would love to hear your feedback and suggestions on what’s next to build! (Contact us on GitHub and the Databricks Community)

Join the conversation in the Databricks Community where data-obsessed peers are chatting about Data + AI Summit 2022 announcements and updates. Learn. Network. Celebrate.

Try Databricks for free

Related posts

See all Engineering Blog posts