Create Dashboards and Visualize Live Azure Data Lake Storage Data in Geckoboard

Geckoboard is a business intelligence tool that simplifies the visualization of key performance indicators (KPIs) by creating live dashboards. It allows teams to consolidate data from various sources - such as Salesforce, Snowflake, Google Analytics, and spreadsheets - and display it in a visually engaging and easy-to-understand format. Designed for simplicity and clarity, Geckoboard helps businesses monitor performance, track goals, and make data-driven decisions.

When used with CData Connect AI, you gain instant, cloud-to-cloud access to Azure Data Lake Storage data from Geckoboard for dashboards, monitoring, visualizations, and more. This article explains how to connect to Azure Data Lake Storage and create visualizations using Azure Data Lake Storage data in Geckoboard.

CData Connect AI offers a seamless SQL Server, cloud-to-cloud interface for Azure Data Lake Storage, enabling you to effortlessly create dashboards and visualizations using live Azure Data Lake Storage data in Geckoboard. While building visualizations, Geckoboard requires SQL queries to retrieve the necessary data. With built-in optimized data processing, CData Connect AI pushes all supported SQL operations (such as filters and JOINs) directly to Azure Data Lake Storage, utilizing server-side processing for fast and efficient data retrieval of Azure Data Lake Storage data.

Configure Azure Data Lake Storage connectivity for Geckoboard

Connectivity to Azure Data Lake Storage from Geckoboard is made possible through CData Connect AI. To work with Azure Data Lake Storage data from Geckoboard, we start by creating and configuring a Azure Data Lake Storage connection.

1. Log into Connect AI, click Sources, and then click Add Connection
👁 Adding a Connection

2. Select "Azure Data Lake Storage" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to Azure Data Lake Storage.

   Authenticating to a Gen 1 DataLakeStore Account

   Gen 1 uses OAuth 2.0 in Entra ID (formerly Azure AD) for authentication.

   For this, an Active Directory web application is required. You can create one as follows:

   1. Sign in to your Azure Account through the
   2. Select "Entra ID" (formerly Azure AD).
   3. Select "App registrations".
   4. Select "New application registration".
   5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
   6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
   7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

   To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen1.
   • Account: Set this to the name of the account.
   • OAuthClientId: Set this to the application Id of the app you created.
   • OAuthClientSecret: Set this to the key generated for the app you created.
   • TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

   Authenticating to a Gen 2 DataLakeStore Account

   To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen2.
   • Account: Set this to the name of the account.
   • FileSystem: Set this to the file system which will be used for this account.
   • AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.
   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Azure Data Lake Storage Connection page and update the User-based permissions. 👁 Updating permissions
   

Add a Personal Access Token

When connecting to Connect AI through the REST API, the OData API, or the Virtual SQL Server, a Personal Access Token (PAT) is used to authenticate the connection to Connect AI. It is best practice to create a separate PAT for each service to maintain granularity of access.

1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create. 👁 Creating a new PAT
   
4. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, you are ready to connect to Azure Data Lake Storage data from Geckoboard.

Connect live Azure Data Lake Storage data in Geckoboard

Follow these steps to establish a connection from Geckoboard to the CData Connect AI Virtual SQL Server API.

1. Log into Geckoboard
2. Add a custom dashboard name and click Add widget 👁 Click on Add widget.
   
3. Search for "Databases" in the Connect your data search bar 👁 Search and click on Databases.
   
4. Select SQL Server as the Database type 👁 Select SQL Server.
   
5. Fill in the connection details:
   • Connection name: enter a name for the connection to CData Connect AI
   • Host: enter the Virtual SQL Server endpoint: tds.cdata.com
   • Port: : enter 14333
   • Database name: enter the Connection Name of the CData Connect AI data source you want to connect to (for example, ADLS1)
   • Username: enter your CData Connect AI username. This is displayed in the top-right corner of the CData Connect AI interface. For example, [email protected]
   • Password: enter the PAT you generated on the Settings page
   👁 Configuring the connection to the Virtual SQL Server API
   
6. Click Connect

After successfully configuring your connection, you can query and visualize your Azure Data Lake Storage data.

Visualize live Azure Data Lake Storage data in Geckoboard

To visualize live Azure Data Lake Storage data in Geckoboard, follow these steps:

1. Write an SQL query to select the specific Azure Data Lake Storage data needed for visualization in Paste your SQL query compiler screen 👁 Write an SQL query to select specific Azure Data Lake Storage data.
   

Depending on your use case, you can also generate the desired SQL query using the Data Copilot or Query Builder features of CData Connect AI

👁 Generate an SQL query using AI Generator in Connect AI.

2. Choose the visualization type (Line Chart, Bar Chart, or Column Chart) that best suits your business requirements 👁 Choose the visualization type.
   
3. The selected chart will be displayed on Geckoboard 👁 Chart is displayed.
   
4. Click the menu (three dots in the top-right corner of the graph) and select Edit to modify the SQL query or set a refresh interval for the chart 👁 Edit the widget.
   

Live access to Azure Data Lake Storage data from cloud applications

At this stage, you have established a direct, cloud-to-cloud connection to live Azure Data Lake Storage data in Geckoboard. This enables you to create dashboards to monitor and visualize your data seamlessly.

For more details on accessing live data from over 100 SaaS, Big Data, and NoSQL sources through cloud applications like Geckoboard, visit our Connect AI page.