Databricks API Integration: 3 Easy Methods

Businesses generate huge volumes of data that include transactional data, user data, Marketing, and Sales data, etc. All this data needs to be stored in a common place for analysis and to generate insights from it. Databricks is a Data Warehousing, Machine Learning web-based platform that allows users to store data, run analysis, and get insights using SparkSQL. 

APIs are flexible reliable methods to communicate between applications and transfer data. Companies connect various 3rd party tools and platforms as a target or a data source to Databricks using Databricks API.

Databricks API comes integrated with Amazon Web Services, Microsoft Azure, and Google Cloud Platform for data accessibility. Using them, developers can easily connect any data source or application to the Databricks workspace and use pre-configured Machine Learning environments to analyze data.

Databricks APIs are flexible and easy to use. They come with detailed documentation on Databricks REST APIs and other information. In this article, you will learn about Databricks and REST APIs. You will also understand how you can connect to Databricks API using REST API and access data. 

Prerequisites

• An active Databricks account.
• Knowledge of APIs

What is Databricks?

Databricks is a web-based platform and enterprise software developed by the creators of Apache Spark. It is a unified Cloud-based Data Engineering platform focusing on Big Data collaboration and Data Analytics by letting users combine Data Warehouses, Data Lakes, and data from other sources in one place to create a Lakehouse.

Databricks offers a versatile workspace for Data Engineers, Data Scientists, Business Analysts, and Data Analysts to collaborate using Collaborative Notebooks, Machine Learning Runtime, and managed ML Flow. Databricks was founded as an alternative to MapReduce to process Big Data.

Databricks is built on top of Apache Spark, a fast and generic engine for Large-Scale Data Processing. It delivers reliable and high performance. Databricks also supports integration with leading Cloud service providers – Amazon Web Services, Microsoft Azure, and Google Cloud Platform. It comes with Delta Lakes which is an Open Format Storage Layer that assists in handling scalable Metadata, unifying streams, and batch data processing.

Key Features of Databricks 

A few key features of Databricks are listed below:

• Data Compression: Databricks supports data streaming, SQL queries, and Machine Learning. It takes up a huge volume of data, so Databricks comes with a unified Spark Engine to compress data at large scales.
• Collaborative Notebooks: Databricks supports many languages and tools that allow you to access data, analyze it, discover new insights and, build new models using their interactive notebooks. The language supported is Python, Scala, R, and SQL.
• Integrations: Databricks supports integrations with many tools and IDEs like PyCharm, IntelliJ, Visual Studio Code, etc. to make the Data Pipelining more organized. You can also integrate Datbricks with other cloud data storage platforms such as Azure Data Lake Storage, Google BigQuery Cloud Storage, Snowflake, etc., to fetch data in the form of CSV, XML, JSON format.
• Machine Learning Features: Databricks offers many pre-configured Machine Learning environments leveraged with powerful frameworks such as PyTorch, TensorFlow, and Scikit-learn. You can track share experiments, reproduce runs, and manage models collaboratively.

What is REST API?

REST API stands for Representational State Transfer Application Programming Interface is a data source’s frontend that allows users to create, retrieve, update, and delete data items.

It is a software architecture style that is used for guiding the development of the architecture. Dr. Roy Fielding described REST API in 2000 giving developers flexibility and an approach to link components and applications in a microservices architecture because of their versatility.

REST API defines how 2 applications communicate with each other over HTTP. An HTTP request access and use the data with PUT, GET, POST and DELETE command. The components are loosely coupled which makes the data flow through HTTP fast and efficient. 

Key Features of REST API

A few features of REST API are listed below:

• Flexible: REST APIs are flexible as they allow users to make multiple types of calls in different formats. It makes it if possible for users to communicate efficiently with client servers even they are hosted on different servers.
• Layered: REST APIs keep the client, and the server decoupled that makes calls and responses of the REST APIs go through different layers. The layered system makes the APIs scalable.
• Stateless: The server keeps no information about the user who uses the API. The requests sent from a client to a server will include all of the necessary information for the server to comprehend the client’s requests.

What are the Methods to Connect to Databricks APIs?

Now that you have understood Databricks and REST API and why Databricks API is important for developers to connect to other apps and platforms. In this section, you will learn how to connect to Databricks API to request data. The 3 methods to access Databricks API are listed below:

• Method 1: Invoking Databrick API Using Python
• Method 2: Invoking Databrick API Using cURL
• Method 3: Connect Databricks APIs Using Hevo Data

Method 1: Invoking Databrick API Using Python

In this method, python and the request library will be used to connect to Databricks API. The steps are listed below:

• Step 1: Authentication Using Databricks Access Token
• Step 2: Storing the Token in .netrc File
• Step 3: Accessing Databricks API Using Python

Step 1: Authentication Using Databricks Access Token

• Log in to your Databricks account.
• Click on the “Settings” button located in the lower-left corner of the screen.
• Then, click on the “User Settings” option.
• Now, switch to the “Access Token” tab.
• Here, click on the “Generate New Token” button to generate a new personal access token for Databricks API.
• Now, click on the “Generate” button.

• Copy the access token that you just generated and store it in a safe location.

Step 2: Storing the Token in .netrc File

• Now create a file with .netrc and add machine, log in, and password properties in the file. The syntax for the file is shown below.

machine <databricks-instance>
login token
password <token-value>

• Here, the <data-instance> is the Instance ID part of your Workspace URL for Databricks Deployment.
• Let’s say your Workspace URL is https://abc-d1e2345f-a6b2.cloud.databricks.com then the instance ID or <databricks-instance> is abc-d1e2345f-a6b2.cloud.databricks.com.
• The token is the literal string token.
• The <token-value> is the value of the token you copied. For example: api1234567890ab1cde2f3ab456c7d89efa.
• Finally, after creating the .netrc file, the resulting file will look similarly as shown below:

machine abc-d1e2345f-a6b2.cloud.databricks.com
login token
password dapi1234567890ab1cde2f3ab456c7d89efa

Step 3: Accessing Databricks API Using Python

• Open any code editor that supports Python.
• For invoking Databricks API using Python, the popular library requests will be used for making HTTP requests. You will go through the process of getting the list of information about specific Databricks clusters. 
• Here, the .netrc file will be used to pass the credentials.
• The code for accessing Databricks API using Python is given below:

import requests
import json

instance_id = 'dbc-a1b2345c-d6e7.cloud.databricks.com'

api_version = '/api/2.0'
api_command = '/clusters/get'
url = f"https://{instance_id}{api_version}{api_command}"

params = {
  'cluster_id': '1234-567890-batch123'
}

response = requests.get(
  url = url,
  params = params
)

print(json.dumps(json.loads(response.text), indent = 2))

• Here, first, you need to import 2 libraries then provide all the information regarding instance ID, app commands, app versions, URL to call, additional parameters for the cluster, etc. 
• After running the code, the finalized result will look like as shown in the image below:

{
  "cluster_id": "1234-567890-batch123",
  "spark_context_id": 1234567890123456789,
  ...
}

Method 2: Invoking Databrick API Using cURL

In this method, cURL is used to access Databrick APIs. It follows simple cURL command in the terminal window. The steps are listed below:

• Follow Step 1 and Step 2 of Method 1, if you haven’t created the .netrc file yet. Else skip it.
• Open your terminal window and write the commands as given below.

curl --netrc --get 
https://abc-d1e2345f-a6b2.cloud.databricks.com/api/2.0/clusters/get 
--data cluster_id=1234-567890-patch123

• Here, replace the https://abc-d1e2345f-a6b2.cloud.databricks.com with your Workspace URL. 
• After completing the command correctly with your credentials, URL, and cluster-ID. The result will look similarly as given below.

{
  "cluster_id": "1234-567890-patch123",
  "spark_context_id": 123456789012345678,
  "cluster_name": "job-239-run-1",
  "spark_version": "8.1.x-scala2.12",
  ...
}

Method 3: Connect Databricks APIs Using Hevo Data

Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, external connectors for REST API, and Streaming Services and simplifies the ETL process.

It supports 150+ data sources including external connectors for Magento REST APIs and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo not only loads the data onto the desired Data Warehouse/destination but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.

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Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensures that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.

Check out why Hevo is the Best:

1. Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
2. Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
3. Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
4. Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
5. Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
6. Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, E-Mail, and support calls.
7. Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.

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That’s it! You have connected Databricks APIs and understood how to access data using Databricks REST API.

Conclusion 

In this article, you learned about Databricks, REST APIs, and different methods to connect to Databricks APIs using Python, and cURL. Databricks APIs allow developers to communicate with apps and platforms and integrate Databricks with them. Companies usually integrate Visualization tools, Reporting tools, and data sources using Databricks APIs. 

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Companies need to analyze their business data stored in multiple data sources. The data needs to be loaded to the Databricks to get a holistic view of the data. Hevo Data is a No-code Data Pipeline solution that helps to transfer data from 150+ sources to desired Data Warehouse. It fully automates the process of transforming and transferring data to a destination without writing a single line of code.

Want to take Hevo for a spin? Sign Up here for a 14-day free trial and experience the feature-rich Hevo suite first hand.

Share your experience of learning about Databricks APIs in the comments section below!