MongoDB Atlas to Databricks: 3 Simple Ways to Integrate Data

Easily move your data from MongoDB Atlas To Databricks to enhance your analytics capabilities. With Hevo’s intuitive pipeline setup, data flows in real-time—check out our 1-minute demo below to see the seamless integration in action!

Organizations might want to move their data from different sources to the destination of their choice for multiple reasons. For instance, a data warehouse unifies data from disparate sources to provide a single source of truth to enable you to make informed data-driven decisions. Similarly, a data lake supports all forms of data–structure, semi-structured and unstructured–and helps businesses make use of their data as and when they require it. In this blog post, we’re going to discuss one such scenario where you may want to migrate your data from MongoDB Atlas to Databricks. 

MongoDB Atlas is a managed cloud database service for MongoDB, but it may have limitations in terms of scalability and performance for certain workloads. Databricks, on the other hand, offers a highly scalable and distributed data processing environment that can handle large volumes of data and perform complex computations efficiently. If an organization’s data and analytics requirements have grown exponentially, it may opt for a MongoDB Atlas Databricks integration.

On that note, let’s dive right into the article and look at three simple ways to move data from MongoDB to Databricks.

What is MongoDB Atlas?

MongoDB Atlas is a fully managed cloud database service for deploying, managing, and scaling MongoDB across multiple cloud providers.

Key Features of MongoDB Atlas:

• Fully Managed: Automates database deployment, maintenance, updates, and backups.
• Global Clusters: Distribute data globally with multi-region replication.
• Autoscaling: Automatically scales based on workload demand.
• Security: Built-in encryption, VPC peering, role-based access control, and auditing.
• Backup and Restore: Automated backups with point-in-time recovery.

What is Databricks?

Databricks is an integrated data analytics platform developed to facilitate working with massive datasets and machine learning. Based on Apache Spark, it creates a seamless collaboration environment for data engineers, data scientists, and analysts.

Key Features of Databricks

• Unified Data Analytics Platform: Combines data engineering, data science, and analytics in one platform.
• Integrated with Apache Spark: Provides high-performance data processing using Apache Spark.
• Collaborative Notebooks: Interactive notebooks for data exploration and collaboration.
• Delta Lake for Reliable Data Lakes: Ensures data reliability and quality with ACID transactions.
• Machine Learning Capabilities: Supports the full machine learning lifecycle from model development to deployment.

What are the Ways to Connect Mongodb Atlas to Databricks?

Method 1: Beginner-Friendly Way to Migrate Data from MongoDB Atlas to Databricks

Step 1: Connect MongoDB Atlas as your source.

Step 2: Connect Databricks as your destination.

Method 2: Manual Method for Connecting MongoDB Atlas to Databricks

Step 1: Create a Databricks Cluster and Add the Connector as a Library

• Create a Databricks cluster.
• Navigate to the cluster detail page and select the Libraries tab.
• Click the Install New button.
• Select Maven as the Library Source.
• Use the Search Packages feature, find ‘mongo-spark’. This should point to org.mongodb.spark:mongo-spark-connector_2.12:3.0.1 or newer.
• Click Install.

Step 2: Create a MongoDB Atlas Instance and Prep Data

• Sign up for MongoDB Atlas.
• Create an Atlas free tier cluster.
• Enable Databricks clusters to connect to the cluster by adding the external IP addresses for the Databricks cluster nodes to the whitelist in Atlas.
• Add some Data to your Database or use Sample Databases available.

Step 3: Update Spark Configuration with the Atlas Connection String

• Copy the Connection String from Atlas. It has the form of “mongodb+srv://<username>:<password>@<databasename>.xxxxx.mongodb.net/“
• Back in Databricks in your cluster configuration, under Advanced Options (bottom of page), paste the connection string for both the spark.mongodb.output.uri and spark.mongodb.input.uri variables.
• Alternatively you can explicitly set the option when calling APIs like: spark.read.format(“mongodb”).option(“spark.mongodb.input.uri”, connectionString).load().

And you are done! Now you can perform various operations on this data. 

Use Cases of Connecting MongoDB Atlas with Databricks

1. Real-time data analytics: Load real-time MongoDB Atlas data into Databricks.
2. ETL Pipelines: In Databricks, transform and clean data from MongoDB Atlas before loading it into data warehouses or BI tools.
3. Leverage the power of Databricks for the development and deployment of machine learning models on MongoDB Atlas data.
4. Aggregation of Data: Scale data aggregagtion from MongoDB within the Databricks platform for business intelligence.
5. IoT Data Processing: Stream and Process IoT Data Stream from MongoDB Atlas to Databricks in Real Time.

What Can You Achieve by Replicating Data from MongoD Atlas to Databricks?

• If an organization wants to leverage the advanced analytics features of Databricks on their MongoDB data, migrating this data to Databricks is the best option.
• Databricks offers seamless integration with various data sources. If an organization needs to combine their MongoDB data with data from other sources, and perform complex transformations, migrating to Databricks can enable better integration and data consolidation.
• Databricks has a rich ecosystem of tools and libraries for data analytics, including support for popular programming languages like Python, R, and Scala. Organizations that want to take advantage of this ecosystem might choose to migrate their MongoDB Atlas data to Databricks.

Conclusion

The rich feature loaded platform of Databricks makes it a great choice for organizations that want to unify their data under one hood and make prompt data-driven decisions. And using an automated data pipeline takes away the stress of managing an in-house pipeline or executing a custom ETL script without any errors.

You can enjoy a smooth ride with Hevo Data’s 150+ plug-and-play integrations (including 50+ free sources) like MongoDB Atlas to Firebolt. Hevo Data is helping many customers take data-driven decisions through its no-code data pipeline solution for several such integrations. 

Saving countless hours of manual data cleaning and standardizing, Hevo Data’s pre-load data transformations to connect MongoDB Atlas to Firebolt gets it done in minutes via a simple drag and drop interface or your custom Python scripts. No need to go to Firebolt for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo Data’s interface and get your data in the final analysis-ready form. Sign up for a 14-day free trial today and check out our unbeatable pricing to choose a plan that fits your requirements best.

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