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Extend Google Sheets with Databricks Data
Make calls to the API Server from Google Apps Script.
Interact with Databricks data from Google Sheets through macros, custom functions, and add-ons. The CData API Server, when paired with the ADO.NET Provider for Databricks (or any of 200+ other ADO.NET Providers), enables connectivity to Databricks data from cloud-based and mobile applications like Google Sheets. The API Server is a lightweight Web application that produces OData services for Databricks and any source supported by the CData ADO.NET Providers.
Google Apps Script can consume these OData services in the JSON format. This article shows how to create a simple add-on that populates a Google Spreadsheet with Customers data and, as you make changes, executes updates to Databricks data.
About Databricks Data Integration
Accessing and integrating live data from Databricks has never been easier with CData. Customers rely on CData connectivity to:
- Access all versions of Databricks from Runtime Versions 9.1 - 13.X to both the Pro and Classic Databricks SQL versions.
- Leave Databricks in their preferred environment thanks to compatibility with any hosting solution.
- Secure authenticate in a variety of ways, including personal access token, Azure Service Principal, and Azure AD.
- Upload data to Databricks using Databricks File System, Azure Blog Storage, and AWS S3 Storage.
While many customers are using CData's solutions to migrate data from different systems into their Databricks data lakehouse, several customers use our live connectivity solutions to federate connectivity between their databases and Databricks. These customers are using SQL Server Linked Servers or Polybase to get live access to Databricks from within their existing RDBMs.
Read more about common Databricks use-cases and how CData's solutions help solve data problems in our blog: What is Databricks Used For? 6 Use Cases.
Getting Started
Set Up the API Server
Follow the steps below to begin producing secure Databricks OData services:
Deploy
The API Server runs on your own server. On Windows, you can deploy using the stand-alone server or IIS. On a Java servlet container, drop in the API Server WAR file. See the help documentation for more information and how-tos.
The API Server is also easy to deploy on Microsoft Azure, Amazon EC2, and Heroku.
Connect to Databricks
After you deploy the API Server and the ADO.NET Provider for Databricks, provide authentication values and other connection properties needed to connect to Databricks by clicking Settings -> Connections and adding a new connection in the API Server administration console.
To connect to a Databricks cluster, set the properties as described below.
Note: The needed values can be found in your Databricks instance by navigating to Clusters, and selecting the desired cluster, and selecting the JDBC/ODBC tab under Advanced Options.
- Server: Set to the Server Hostname of your Databricks cluster.
- HTTPPath: Set to the HTTP Path of your Databricks cluster.
- Token: Set to your personal access token (this value can be obtained by navigating to the User Settings page of your Databricks instance and selecting the Access Tokens tab).
You can then choose the Databricks entities you want to allow the API Server to access by clicking Settings -> Resources.
Authorize API Server Users
After determining the OData services you want to produce, authorize users by clicking Settings -> Users. The API Server uses authtoken-based authentication and supports the major authentication schemes. Access can also be restricted based on IP address: Connections from all addresses except localhost are blocked by default, so you will need to allow connections from Google's servers for this article. You can authenticate as well as encrypt connections with SSL.
Retrieve Databricks Data
Open the Script Editor from your spreadsheet by clicking Tools -> Script Editor. In the Script Editor, add the following function to populate a spreadsheet with the results of an OData query:
function retrieve(){
var url = "https://MyUrl/api.rsc/Customers?select=Id,City,CompanyName,Country";
var response = UrlFetchApp.fetch(url,{
headers: {"Authorization": "Basic " + Utilities.base64Encode("MyUser:MyAuthtoken")}
});
var json = response.getContentText();
var sheet = SpreadsheetApp.getActiveSheet();
var a1 = sheet.getRange('a1');
var index=1;
var customers = JSON.parse(json).value;
var cols = [["Id","City","CompanyName","Country"]];
sheet.getRange(1,1,1,4).setValues(cols);
row=2;
for(var i in customers){
for (var j in customers[i]) {
switch (j) {
case "Id":
a1.offset(row,0).setValue(account[i][j]);
break;
case "City":
a1.offset(row,1).setValue(account[i][j]);
break;
case "CompanyName":
a1.offset(row,2).setValue(account[i][j]);
break;
case "Country":
a1.offset(row,3).setValue(account[i][j]);
break;
}
}
row++;
}
}
Follow the steps below to add an installable trigger to populate the spreadsheet when opened:
- Click Resources -> Current Project's Triggers -> Add a New Trigger.
- Select retrieve in the Run menu.
- Select From Spreadsheet.
- Select On open.
After closing the dialog, you are prompted to allow access to the application.
Post Changes to Databricks Data
Add the following function to post changes to cells back to the API Server:
function buildReq(e){
var sheet = SpreadsheetApp.getActiveSheet();
var changes = e.range;
var id = sheet.getRange(changes.getRow(),1).getValue();
var col = sheet.getRange(1,changes.getColumn()).getValue();
var url = "http://MyServer/api.rsc/Customers("+id+")";
var putdata = "{\"@odata.type\" : \"CDataAPI.Customers\", \""+col+"\": \""+changes.getValue()+"\"}";;
UrlFetchApp.fetch(url,{
method: "put",
contentType: "application/json",
payload: putdata,
headers: {"Authorization": "Basic " + Utilities.base64Encode("MyUser:MyAuthtoken")}
});
}
Follow the steps below to add the update trigger:
- Click Resources -> Current Project's Triggers.
- Select buildReq in the Run menu.
- Select From Spreadsheet.
- Select On edit.
You can test the script by clicking Publish -> Test as Add-On. Select the version, installation type, and spreadsheet to create a test configuration. You can then select and run the test configuration.
As you make changes to cells, the API Server executes updates to Databricks data.
