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Overview
Databricks has emerged as a powerful unified data platform that simplifies big data processing and advanced analytics. Its support for Apache Spark and seamless integration with cloud platforms like AWS, Azure, and Google Cloud empowers data engineers, analysts, and scientists to process large datasets efficiently. One of the most crucial steps in making the most of Databricks is connecting your external databases, whether PostgreSQL, MySQL, SQL Server, or other RDBMS, so that you can read, transform, and analyze data in a collaborative environment.
In this blog post, we will dive deep into how to mount your database to Databricks, including step-by-step instructions, key considerations, and best practices to ensure secure and scalable access.
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Why Mount a Database to Databricks?
Databricks allows you to work with various data sources, including file systems like Amazon S3, Azure Data Lake, and databases via JDBC. While mounting is typically associated with cloud storage systems, connecting to a database (which might be confused with “mounting”) means creating a live, reusable connection between Databricks and your relational or NoSQL data source.
Mounting or connecting your database to Databricks provides several advantages:
- Centralized Access: Collaborate across teams on a common workspace.
- ETL Simplification: Perform transformations and data movement using Spark SQL or PySpark.
- Scalability: Leverage Databricks’ distributed processing for querying large tables.
- Machine Learning: Prepare data easily for modeling using MLlib or integration with MLflow.
- Automation: Automate reporting, alerts, and workflows using Databricks Jobs and notebooks.
Understanding the Architecture
Before proceeding, it’s important to understand what “mounting” means in the Databricks context:
- Mounting File Systems: Refers to linking external storage (e.g., AWS S3, Azure Blob, GCS) to Databricks DBFS.
- Connecting Databases: Involves using JDBC drivers to connect databases to notebooks or clusters securely, often referred to as “mounting” in colloquial usage. However, it isn’t a mount in the file system sense.
So, while you can’t directly mount a database like you would Amazon S3, you connect to it using a JDBC connection string and optionally register it as a permanent table or catalog entry.
Prerequisites
To connect your database to Databricks, you’ll need:
- A running Databricks workspace.
- A cluster with appropriate libraries installed (e.g., JDBC drivers).
- Database credentials (host, port, database name, username, and password).
- Proper network access (i.e., your Databricks cluster can reach your database).
- Optional: A secure place to store secrets (Databricks Secret Scope, Azure Key Vault, or AWS Secrets Manager).
Supported Databases
Databricks supports a wide range of data sources, including but not limited to:
- PostgreSQL
- MySQL
- Microsoft SQL Server
- Oracle
- Snowflake
- Amazon Redshift
- MongoDB (via Spark Connector)
- Google BigQuery
- Databricks SQL (internal)
Each requires a specific JDBC driver that can be installed via a Maven coordinate or uploaded manually.
Step-by-Step: Connecting a PostgreSQL Database to Databricks
We will use PostgreSQL for demonstration purposes, but the process is similar to other databases.
Step 1: Install the JDBC Driver
Navigate to Clusters > Libraries > Install New > Maven and use:
For MySQL:
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mysql:mysql-connector-java:8.0.23 |
This installs the JDBC driver on your cluster.
Step 2: Store Your Credentials Securely
Option A: Use Databricks Secrets
Create a secret scope:
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databricks secrets create-scope --scope db-creds |
Then store credentials:
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databricks secrets put --scope db-creds --key pg-username databricks secrets put --scope db-creds --key pg-password |
Access them in a notebook:
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username = dbutils.secrets.get(scope="db-creds", key="pg-username") password = dbutils.secrets.get(scope="db-creds", key="pg-password") |
Option B: Use a config file (not recommended for production)
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username = "your_user" password = "your_password" |
Step 3: Define JDBC Connection Parameters
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jdbc_hostname = "your-db-hostname" jdbc_port = 5432 jdbc_database = "your-database" jdbc_url = f"jdbc:postgresql://{jdbc_hostname}:{jdbc_port}/{jdbc_database}" connection_properties = { "user": username, "password": password, "driver": "org.postgresql.Driver" } |
Step 4: Read Data into a Spark DataFrame
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df = spark.read.jdbc( url=jdbc_url, table="public.your_table", properties=connection_properties ) df.show() |
Step 5: Create a Temp or Permanent Table
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df.createOrReplaceTempView("your_table_temp") |
Secure Practices
When working with databases in Databricks, security must be a top priority:
- Avoid hardcoding credentials in notebooks.
- Use Secret Scopes or Azure Key Vault-backed scopes for credential management.
- Enable cluster-level AWS IAM roles or private endpoints for secure access.
- Use network policies or Amazon VPC peering to restrict traffic to the DB.
- Use parameterized queries or safe read/write methods to avoid SQL injection.
Conclusion
With secure practices, performance optimization, and automation in place, your data platform can deliver consistent and reliable value across teams.
Drop a query if you have any questions regarding Databricks and we will get back to you quickly.
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About CloudThat
CloudThat is a leading provider of Cloud Training and Consulting services with a global presence in India, the USA, Asia, Europe, and Africa. Specializing in AWS, Microsoft Azure, GCP, VMware, Databricks, and more, the company serves mid-market and enterprise clients, offering comprehensive expertise in Cloud Migration, Data Platforms, DevOps, IoT, AI/ML, and more.
CloudThat is the first Indian Company to win the prestigious Microsoft Partner 2024 Award and is recognized as a top-tier partner with AWS and Microsoft, including the prestigious ‘Think Big’ partner award from AWS and the Microsoft Superstars FY 2023 award in Asia & India. Having trained 650k+ professionals in 500+ cloud certifications and completed 300+ consulting projects globally, CloudThat is an official AWS Advanced Consulting Partner, Microsoft Gold Partner, AWS Training Partner, AWS Migration Partner, AWS Data and Analytics Partner, AWS DevOps Competency Partner, AWS GenAI Competency Partner, Amazon QuickSight Service Delivery Partner, Amazon EKS Service Delivery Partner, AWS Microsoft Workload Partners, Amazon EC2 Service Delivery Partner, Amazon ECS Service Delivery Partner, AWS Glue Service Delivery Partner, Amazon Redshift Service Delivery Partner, AWS Control Tower Service Delivery Partner, AWS WAF Service Delivery Partner, Amazon CloudFront Service Delivery Partner, Amazon OpenSearch Service Delivery Partner, AWS DMS Service Delivery Partner, AWS Systems Manager Service Delivery Partner, Amazon RDS Service Delivery Partner, AWS CloudFormation Service Delivery Partner, AWS Config, Amazon EMR and many more.
FAQs
1. Can I mount a database to Databricks like I mount cloud storage (e.g., S3 or ADLS)?
ANS: – No, databases cannot be “mounted” in the traditional file system sense like Amazon S3 or ADLS. Instead, you connect to them using JDBC within your Databricks notebook or cluster. The term “mounting a database” is often used informally to refer to setting up a persistent and reusable database connection.
2. What JDBC drivers are supported in Databricks, and how do I install them?
ANS: – Databricks supports many JDBC drivers, including PostgreSQL, MySQL, SQL Server, Oracle, and more. You can install them from the Maven repository via the Clusters > Libraries > Install New > Maven interface. For example, PostgreSQL uses org.postgresql:postgresql:42.2.20.
WRITTEN BY Vinay Lanjewar
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