Cin7 is a connected inventory and order management platform used by growing businesses to synchronise products, stock, orders, and customer data across sales channels. Many organisations need Cin7 data in Power BI to monitor inventory health, order trends, and fulfilment performance.

Using the ZappySys ODBC Driver for REST APIs, you can query Cin7 REST API data with SQL and load it into Power BI without writing custom connector code.

This tutorial shows how to create a Cin7 ODBC DSN, configure API authentication, preview data, connect Power BI, and run practical SQL queries.

Steps

Get Cin7 API Credentials

1. Sign in to your Cin7 account.
2. Open Settings → Integrations & API → API v1 in your Cin7 admin portal.
   • Note your API Username shown on the page.
   • Click Add New API Connection, give it a name (e.g. ZappySys), and copy the generated API Key.
   

   Cin7 – Generate new API connection

3. Store both values securely — the API Username and API Key together act as your login credentials.
   

   Cin7 – API credentials

4. Note your API base URL: https://api.cin7.com/api.

Create a New ODBC DSN Using ZappySys JSON Driver

1. Search for ODBC in the Windows Start menu and open ODBC Data Source Administrator.
   
2. In User DSN or System DSN, click Add.
3. Select ZappySys JSON Driver, then click Continue.
   

Configure Cin7 API Connection

1. Enter a Cin7 endpoint URL in this format:
   https://api.cin7.com/api/v1/Products
2. In Connection Type, select HTTP and click Configure.
3. Set Credential Type to Basic Auth (Username/Password).
4. Set Username to your Cin7 API Username.
5. Set Password to your Cin7 API Key.
6. Click OK to save HTTP authentication settings.
   

   Cin7 – ODBC HTTP connection configuration

7. Set HTTP Request Method to GET.
8. Set JSON Path Filter to $.Products[*] for the Products endpoint.
9. Click Test Connection, then click OK to save the DSN.
   

   Cin7 – JSON Driver configuration

Preview Cin7 Data in ODBC Driver

1. Open the Preview tab in the DSN configuration window.
2. Generate a query and click Preview Data.
3. Confirm rows are returned. If not, recheck endpoint URL, credentials, and JSON Path Filter.
   

   Cin7 – JSON Driver result

Connect Power BI to Cin7 via ODBC

1. Open Power BI Desktop.
2. Go to Home > Get Data > More….
3. Search for ODBC and click Connect.
   

   Power BI – Get data from ODBC connection

4. Select your Cin7 DSN from the data source list.
5. Optionally enter a custom SQL query in Advanced options.
   

   Cin7 – Power BI select driver

6. Click OK, review the preview, and click Load.
   

   Cin7 – Power BI result

7. In the Navigator, select fields or tables you want to import into your model.

Query Cin7 Data with SQL

Use custom SQL queries in the ODBC driver or Power BI to shape data before loading.

Example 1: List Products

SELECT
    ID,
    StyleCode,
    Name,
    Category,
    Brand,
    IsActive,
    ModifiedDate
FROM Products

Example 2: Active Products Only

SELECT
    ID,
    StyleCode,
    Name,
    IsActive
FROM Products
WHERE IsActive=true

Example 3: Recently Modified Products (Last 30 Days)

SELECT
    ID,
    StyleCode,
    Name,
    ModifiedDate
FROM Products
WHERE ModifiedDate >= <<today-10d,FUN_TO_DATE>>

Conclusion

Connecting Cin7 to Power BI using the ZappySys ODBC Driver gives you a fast, SQL-friendly way to build inventory and sales analytics from Cin7 API data. You can centralise operational data, model it in Power BI, and publish dashboards without building custom connectors.

Explore the full capabilities of the ZappySys ODBC Drivers to integrate Cin7 and other APIs into your reporting workflow.

The post How to get data from Cin7 into Power BI appeared first on ZappySys Blog.

Cin7 is a connected inventory and order management platform used by growing businesses to synchronise products, stock, orders, and customer data across multiple sales channels. Many organisations need Cin7 data in SQL Server for reporting, demand planning, fulfilment tracking, and analytics.

In this tutorial, you will learn how to connect to Cin7 REST APIs using SQL Server Integration Services (SSIS) with the ZappySys SSIS PowerPack. This approach helps you build repeatable ETL pipelines without writing custom API integration code.

Steps

Get Cin7 API Credentials

1. Sign in to your Cin7 account.
2. Open Settings → Integrations & API → API v1 in your Cin7 admin portal.
   • Note your API Username shown on the page.
   • Click Add New API Connection, give it a name (e.g. ZappySys), and copy the generated API Key.
   

   Cin7 – Generate new API connection

3. Store both values securely — the API Username and API Key together act as your login credentials.
   

   Cin7 – API credentials

Get Cin7 Base URL and Endpoint Path

1. Identify the REST endpoint you want to query (for example, products, customers, or sales orders).
2. Keep your API base URL and endpoint path ready for SSIS configuration.
3. Example:
   https://api.cin7.com/api/v1/Products

Create an HTTP Connection in SSIS

1. Create or open your SSIS project and package.
2. In Connection Managers, right-click and select New Connection….
3. Select ZS-HTTP from the SSIS connectors list.
4. Set Credentials Type to Basic Auth.
5. Enter your API Username in the Username field and your API Key in the Password field
6. Click OK to save the connection.
   

   Cin7-HTTP connection

Create and Configure JSON Source

1. Drag a Data Flow Task into Control Flow and open it.
2. In Data Flow, drag and drop the ZS JSON Source.
3. Open the JSON Source editor and enable Use credentials.
4. Select the Cin7 HTTP connection you created earlier.
5. Set the API URL. Example to extract products:
   

   https://api.cin7.com/api/v1/products

6. Set HTTP Request Method to GET.
7. Add the following HTTP Header:
   1. Accept: application/json
8. Set JSON Path Filter based on the endpoint (for example, $.Products[*] for the Products endpoint).
9. Click Preview to confirm rows are returned, then click OK.
   

   Cin7 – JSON Source configuration

Configure Destination Component

1. Add an Upsert destination (or another destination of your choice, such as a Trash destination or JSON/CSV/XML destination).
2. Connect the output arrow from JSON Source to the destination component.
3. Configure your SQL Server connection and target table.
4. Check the options you want to use: Insert or/and Update.
5. Map the source columns to destination columns in the Mappings section.
   

   Cin7 – Upsert destination

6. Click OK and execute the package.
7. Validate that Cin7 rows have been loaded into your SQL Server table.
   

   Cin7 – Upsert Destination example

Conclusion

You now have a working SSIS integration to extract Cin7 data using ZappySys SSIS PowerPack and Cin7 REST APIs. With this pattern, you can automate extraction of inventory, product, and order data into SQL Server for reporting and analytics.

Explore the full capabilities of the ZappySys SSIS PowerPack to optimise your data integration tasks. By applying this pattern, you can extend your solution to include transformations, data validation, error handling, and scheduling through SQL Server Agent.

The post SSIS Tutorial: How to get data from Cin7 appeared first on ZappySys Blog.

While ZappySys SSIS PowerPack includes a dedicated Amazon S3 CSV File Destination, many users ask whether there is a dedicated JSON destination for Amazon S3.

The good news is that you can already achieve this using existing ZappySys components without writing any custom code. In this article, we’ll explore three different approaches to exporting JSON files to Amazon S3 using SSIS.

Prerequisites

Before you begin, ensure you have the following:

• ZappySys SSIS PowerPack installed
• An Amazon S3 connection is configured
• An SSIS package
• A data source such as SQL Server, API, CSV, Excel, XML, or JSON

Method 1: Export JSON Directly to Amazon S3 (Recommended)

If your goal is simply to generate a JSON file in Amazon S3, the easiest approach is to use the Export JSON File Task.

The task supports writing the generated JSON directly to an Amazon S3 bucket, eliminating the need to create a temporary local file.

Recommended for: Direct exports with minimal package complexity.

Step 1 – Add Export JSON File Task

Add the Export JSON File Task to your Control Flow.

Step 2 – Configure the Source

Select the data source you wish to export and configure the Source Connection (OLEDB, ADO.NET (SQL), or ODBC).

Step 3 – Configure Amazon S3 Destination

In the destination settings:

• Select Amazon S3 as the destination.
• Select or create an Amazon S3 connection.
• Specify the Bucket Name.
• Optionally specify a Folder.
• Enter the output JSON file name.

Step 4 – Execute the Package

Run the package.

The Export JSON File Task generates the JSON document and uploads it directly into your Amazon S3 bucket.

Benefits

• No intermediate local files
• Simple configuration
• Faster execution
• Easier package maintenance

Method 2: Generate JSON Locally, then Upload to Amazon S3

If your workflow requires validating, compressing, encrypting, archiving, or otherwise processing the JSON file before upload, first generate the JSON locally and then upload it using the Amazon S3 Task.

Recommended for: Advanced ETL workflows requiring intermediate file processing.

Step 1 – Generate the JSON File

Use the Export JSON File Task to save the JSON document locally.

C:\Exports\Customers.json

Step 2 – Upload using Amazon S3 Task

Add an Amazon S3 Task after the Export JSON File Task.

Configure the task to:

• Upload File
• Select the generated JSON file
• Select the Amazon S3 Bucket
• Specify the destination folder (optional)

Step 3 – Delete the Local File (Optional)

Enable Delete File After Successful Transfer if you no longer need the local copy.

Alternatively, use the Advanced File System Task to:

• Delete uploaded files
• Move processed files to an archive folder
• Rename processed files
• Create backup copies

Benefits

• Validate JSON before upload
• Archive processed files
• Compress or encrypt files
• Greater workflow flexibility

Method 3: Generate JSON in Data Flow and Save to Amazon S3

If your package already performs transformations in a Data Flow, you can generate JSON without using the Export JSON File Task.

Use the JSON Generator Transform together with the Amazon S3 CSV File Destination.

Although the destination component is named CSV File Destination, it can also write JSON when configured appropriately.

Recommended for: Existing Data Flow-based ETL packages.

Step 1 – Add JSON Generator Transform

Add a JSON Generator Transform to your Data Flow.

Step 2 – Configure Amazon S3 CSV File Destination

Connect the JSON Generator output to the Amazon S3 CSV File Destination.

Configure the destination as follows:

• Disable Header Row
• Disable Quote Around Values
• Output only the generated JSON column

The generated JSON is written directly to Amazon S3.

Benefits

• Entire workflow remains inside the Data Flow
• Works with existing transformations
• No temporary files
• High-performance pipeline

Which Method Should You Choose?

Conclusion

Although ZappySys SSIS PowerPack does not include a dedicated Amazon S3 JSON Destination component, exporting JSON files to Amazon S3 is fully supported using existing components.

Depending on your ETL requirements, you can:

• Export JSON directly to Amazon S3 using Export JSON File Task.
• Generate a local JSON file and upload it using Amazon S3 Task.
• Create JSON in a Data Flow using JSON Generator Transform and write it using the Amazon S3 CSV File Destination.

These approaches provide flexible, code-free solutions for automating JSON exports to Amazon S3 in SSIS.

Related Articles

• Export JSON from SQL Server using SSIS
• Load Data from SQL Server to Amazon S3
• Split Large Files while Loading Data using SSIS
• Amazon S3 Task Documentation

The post How to Export JSON Files to Amazon S3 using SSIS (3 Easy Methods) appeared first on ZappySys Blog.

Airtable is a cloud-based platform that combines spreadsheet-style usability with relational data capabilities. Many teams use Airtable to manage projects, CRM pipelines, operations, and content workflows. To analyze this data at scale, you can connect Airtable to Power BI and build interactive reports.

Using the ZappySys ODBC Driver for REST APIs, you can query Airtable API data with SQL and load it into Power BI without writing custom integration code.

This tutorial shows how to create an Airtable ODBC DSN, configure API authentication, preview table data, connect Power BI, and run practical SQL queries.

Steps

Create an Airtable Personal Access Token

1. Sign in to your Airtable account.
2. Go to the Airtable token page and click to create a new token.
3. Grant the minimum required scopes for your scenario, such as

   data.records:read

   and

   schema.bases:read

   .
4. Allow access to the specific base(s) and copy the token to a secure location.
   

   Airtable – Create a token

Get Airtable Base ID and Table ID

1. Open the target table in Airtable.
2. Copy the Base ID and Table ID from the URL or API documentation page.
3. Keep these IDs handy because you will use them in the API URL, for example:
   https://api.airtable.com/v0/{BASE_ID}/{TABLE_ID}

Create a New ODBC DSN Using ZappySys JSON Driver

1. Search for ODBC in the Windows Start menu and open ODBC Data Source Administrator.
   
2. In User DSN or System DSN, click Add.
3. Select ZappySys JSON Driver, then click Continue.
   

Configure Airtable API Connection

1. Enter the Airtable API URL in this format:
   https://api.airtable.com/v0/{BASE_ID}/{TABLE_ID}
2. In Connection Type, select HTTP and click Configure.
3. Set Credential Type to Static Token / API Key.
4. Paste your Airtable Personal Access Token in the token field.
5. Click OK to save HTTP authentication settings.
   

   Airtable – ODBC HTTP Static token connection

6. Set HTTP Request Method to GET.
7. Set JSON Path Filter to:
   $.records[*]
8. Click Test Connection, then click OK to save the DSN.
   

   Airtable – JSON Driver configuration

Preview Airtable Data in ODBC Driver

1. Open the Preview tab in the DSN configuration window.
2. Generate a query and click Preview Data.
3. Confirm rows are returned. If not, recheck token permissions, Base ID, Table ID, and JSON Path Filter.
   

   Airtable – JSON Driver result

Connect Power BI to Airtable via ODBC

1. Open Power BI Desktop.
2. Go to Home > Get Data > More….
3. Search for ODBC and click Connect.
   

   Power BI – Get data from ODBC connection

4. Select your Airtable DSN from the data source list.
5. Optionally enter a custom SQL query in Advanced options.
   

   Airtable – Power BI select driver

6. Click OK, review the preview, and click Load.
   

   Airtable – Power BI preview

7. In the Navigator, select fields or tables you want to import into your model.

Query Airtable Data with SQL

Use custom SQL queries in the ODBC driver or Power BI to shape data before loading, depending on your database; here are some examples for our table.

Example 1: Select Common Airtable Fields

SELECT
    id,
    createdTime,
    fields_Name AS Name,
    fields_Status AS Status,
    fields_Owner AS Owner,
    fields_Amount AS Amount
FROM records

Example 2: Filter Active Records

SELECT
    id,
    fields_Name,
    fields_Status,
    fields_Priority
FROM records
WHERE fields_Status = 'Done'

Example 3: Recent Records (Last 7 Days)

SELECT
    id,
    fields_Name,
    createdTime
FROM records
WHERE createdTime >= <<today-7d,FUN_TO_DATE>>

Example 4: Group by Status

SELECT
    fields_Status AS Status,
    COUNT(*) AS RecordCount
FROM records
GROUP BY fields_Status
ORDER BY RecordCount DESC

Conclusion

Connecting Airtable to Power BI using the ZappySys ODBC Driver gives you a fast, SQL-friendly way to build analytics on top of Airtable API data. You can centralise operational data, model it in Power BI, and publish dashboards for your team without building custom connectors.

Explore the full capabilities of the ZappySys ODBC Drivers to integrate Airtable and other APIs into your reporting workflow.

The post How to get data from Airtable into Power BI appeared first on ZappySys Blog.

Airtable is a cloud-based platform that combines spreadsheet simplicity with database capabilities. Teams use Airtable to manage projects, operations, CRM workflows, and content pipelines. Many organisations need Airtable data in SQL Server for reporting, analytics, and downstream data integration.

In this tutorial, you will learn how to connect to Airtable APIs using SQL Server Integration Services (SSIS) with the ZappySys SSIS PowerPack. This approach helps you build repeatable ETL pipelines without writing custom API integration code.

Steps

Get Airtable Personal Access Token

1. Sign in to your Airtable account.
2. Go to your developer hub and create a Personal Access Token.
3. Grant scopes required for reading records (for example, data.records:read and schema.bases:read based on your use case).
4. Allow access to the target base(s) and securely copy the token.
   

   Airtable – Create a token

Get Airtable Base ID and Table ID

1. Open the target table in Airtable.
2. Copy the Base ID and Table ID from the URL or API documentation page.
3. Keep these IDs handy because you will use them in the API URL, for example:
   https://api.airtable.com/v0/{BASE_ID}/{TABLE_ID}

Create an HTTP Connection in SSIS

1. Create or open your SSIS project and package.
2. In Connection Managers, right-click and select New Connection….
3. Select ZS-HTTP from the SSIS connectors list.
   

   Create a new HTTP connection

4. Set Credentials Type to Static token/API key.
5. Paste your Airtable token in the token value field.
6. Click OK to save the connection.
   

   Airtable –  Connection configuration

Create and Configure JSON Source

1. In your SSIS package, navigate to the Variables section and create the following variables:
   1. BaseID(String) — Enter the Base ID.
   2. TableID (String) — Enter the Table ID.
2. Drag a Data Flow Task into Control Flow and open it.
   

   Dragging and dropping Data Flow Task into Control Flow

3. In Data Flow, drag and drop the ZS JSON Source.
   

   SSIS JSON Source – Drag and Drop

4. Open the JSON Source editor and enable Use credentials.
5. Select the Airtable HTTP connection you created earlier.
6. Set the API URL. Example to extract records from a table:
   

   https://api.airtable.com/v0/{{User::BaseID}}/{{User::TableID}}

7. Set HTTP Request Method to GET.
8. Set JSON Path Filter to:
   

   $.records[*]

9. Click Preview to confirm rows are returned, then click OK.
   

   Airtable – JSON Source configuration

Configure Destination Component

1. Add an Upsert destination (or another destination of your choice, such as a Trash destination or a JSON/CSV/XML destination).
2. Connect the output arrow from JSON Source to the destination component.
3. Configure your SQL Server connection and target table.
4. Check the options you want to use: Insert or/and Update
5. Map the source columns to destination columns in the Mappings section.
   

   Airtable – Upsert destination

6. Click OK and execute the package.
7. Validate that Airtable rows have been loaded into your SQL Server table.
   

   Airtable – Upsert Destination example

Conclusion

You now have a working SSIS integration to extract Airtable data using ZappySys SSIS PowerPack and Airtable APIs. With this pattern, you can automate extraction of operational records into SQL Server for reporting and analytics.

Explore the full capabilities of the ZappySys SSIS PowerPack to optimise your data integration tasks. By applying this pattern, you can extend your solution to include transformations, data validation, error handling, and scheduling through SQL Server Agent.

The post SSIS Tutorial: How to get data from Airtable appeared first on ZappySys Blog.

Birdeye is a customer experience and reputation management platform that helps businesses manage reviews, listings, messages, and customer interactions across channels. Many organisations need Birdeye data in Power BI to track review trends, location performance, customer sentiment, and service responsiveness.

Using the ZappySys ODBC Driver for REST APIs, you can connect to Birdeye APIs, query data with SQL, and load it directly into Power BI dashboards without writing custom connector code.

This tutorial shows how to create an ODBC DSN for the Birdeye API, preview data, connect Power BI, and build analytics using custom SQL queries.

Prerequisites

1. A Birdeye account with API access enabled.
2. Power BI Desktop installed on your machine.
3. Basic understanding of SQL and Power BI data modelling.
4. Finally, install the ZappySys ODBC Driver.

Steps

Get Birdeye API Key

1. Sign in to your Birdeye account.
2. Open API settings in your Birdeye admin portal.
3. Generate or copy your API key.
   

   Birdeye – Generate token

4. Store the API key securely and do not expose it in logs or shared files.

Create a new ODBC DSN using ZappySys JSON Driver.

1. Search for ODBC in the Windows Start menu and open ODBC Data Source Administrator.
   
2. In the User DSN or System DSN tab, click Add.
3. Select ZappySys JSON Driver and click Continue.
   

Configure the Connection and API Settings

1. Enter the base URL:
   https://api.birdeye.com/v1/review/businessid/{business_id}
2. In Connection Type, select HTTP and click Configure.
3. Select Static token/API key for Credential type.
4. Use x-api-key in the Auth header name.
5. In Auth Scheme, select {none}
6. Click OK to save the configuration.
   

   Birdeye – ODBC HTTP connection

7. Set HTTP Request Method to GET.
8. Set the following values in the HTTP headers:
   1. Accept: application/json
   2. Content-Type: application/json
9. Set JSON Path Filter to match the review array in the response, for example:
   

   $.hoursOfOperations[*] or $.serviceAreas[*]

10. Click Test Connection to verify connectivity, then click OK to save the DSN.
    

    Birdeye – the JSON Driver configuration

Preview Birdeye data in ODBC Driver

1. Go to the Preview tab.
2. Generate a query, then click Preview Data to confirm rows are returned.
3. If no data is returned, verify your API key, business ID, and JSON Path Filter.
   

   Birdeye – the JSON source preview

Connect Power BI to Birdeye via ODBC

1. Open Power BI Desktop.
2. Click Get Data from the Home ribbon.
3. Click More…, search for ODBC, and click Connect.
   

   Power BI – Get data from ODBC connection

4. Select your Birdeye ODBC DSN from the dropdown list.
5. Optionally, enter a custom SQL query in Advanced options.
   

   Birdeye – Power BI select driver

6. Click OK, then click Load to import data.
   

   Birdeye – Power BI preview

7. In the Navigator window, select the columns you want to load.

Use Other Birdeye Endpoints

You can switch endpoints in DSN settings or custom queries to extract other modules:

• Businesses: https://api.birdeye.com/v1/businesses
• Locations: https://api.birdeye.com/v1/locations
• Messages: https://api.birdeye.com/v1/messages
• Listings: https://api.birdeye.com/v1/listings

Conclusion

Connecting Birdeye to Power BI using the ZappySys ODBC Driver makes it easy to build review and customer-experience analytics without custom connectors. With SQL-based access to Birdeye API responses, you can monitor rating trends, sentiment signals, and location performance in one place.

Explore the full capabilities of the ZappySys ODBC Drivers to integrate Birdeye and other APIs into your Power BI ecosystem.

Need Help?

If you encounter any issues, our support team is here to help:

1. Live Chat – Use the chat widget (bottom-right corner of this page)
2. Email – support@zappysys.com
3. Support center – Visit the ZappySys Support Portal

The post How to get data from Birdeye into Power BI appeared first on ZappySys Blog.

Introduction

Birdeye is a customer experience and reputation management platform that helps businesses manage reviews, listings, messages, and customer interactions across channels. Many organisations need Birdeye data in SQL Server for reporting, sentiment tracking, location performance analysis, and operational dashboards.

In this tutorial, you will learn how to connect Birdeye APIs to SQL Server Integration Services (SSIS) using ZappySys SSIS PowerPack. This approach helps you build repeatable ETL pipelines without writing custom API integration code.

Prerequisites

1. SQL Server Integration Services (SSIS) is installed.
2. Visual Studio with SSIS extension or SQL Server Data Tools (SSDT) is available.
3. A Birdeye account with API access enabled.
4. A valid Birdeye API key.
5. ZappySys SSIS PowerPack is installed on your machine.

Steps

Get Birdeye API Key

1. Sign in to your Birdeye account.
2. Open API settings in your Birdeye admin portal.
3. Generate or copy your API key.
   

   Birdeye – Generate token

4. Store the API key securely and do not expose it in logs or shared files.

Create an HTTP Connection in SSIS

1. Create or open your SSIS project and package.
2. In Connection Managers, right-click and select New Connection….
3. Select ZS-HTTP from the SSIS connectors list.
   

   Create a new HTTP connection

4. Set Credentials Type to Static token/API key.
5. Paste your token in the token value field.
6. Use x-api-key in the Auth header name.
7. In Auth Scheme, select {none}
8. Click OK to save the connection.
   

   Birdeye – HTTP connection

Create and Configure JSON Source

1. Drag a Data Flow Task into Control Flow and open it.
   

   Drag and drop Data flow

2. In Data Flow, drag and drop the ZS JSON Source.
   

   SSIS JSON Source – Drag and Drop

3. Open the JSON Source editor and enable Use credentials.
4. Select the Birdeye HTTP connection you created earlier.
5. Set the API URL. Example to extract reviews:
   

   https://api.birdeye.com/resources/v1/business/{{User::businnessID}}

6. Set HTTP Request Method to GET.
7. Set the following values in HTTP headers:
   1. Accept: application/json
   2. Content-Type: application/json
8. Set JSON Path Filter based on response shape (for example, $.hoursOfOperations[*] or $.serviceAreas[*]).
9. Click Preview to confirm rows are returned, then click OK.
   

   Birdeye – the JSON source configuration

Use Other Birdeye Endpoints (Optional)

You can reuse the same SSIS package pattern with other Birdeye endpoints:

• Businesses: https://api.birdeye.com/v1/businesses
• Locations: https://api.birdeye.com/v1/locations
• Reviews: https://api.birdeye.com/v1/review/businessid/{business_id}
• Messages: https://api.birdeye.com/v1/messages

Configure Destination Component

1. Add an Upsert destination (or another destination of your choice, such as a Trash destination or a JSON/CSV/XML destination).
2. Connect the output arrow from JSON Source to the destination component.
3. Configure your SQL Server connection and target table.
4. Check the options you want to use: Insert or/and Update
5. Map the source columns to destination columns in the Mappings section.
   

   Birdeye – Upsert destination

6. Click OK and execute the package.
7. Validate that Birdeye rows have been loaded into your SQL Server table.
   

   Birdeye – Upsert Destination example

Conclusion

You now have a working SSIS integration to extract Birdeye data using ZappySys SSIS PowerPack and Birdeye APIs. With this pattern, you can automate the extraction of reviews, location data, and customer interaction metrics into SQL Server for reporting and analytics.

Explore the full capabilities of the ZappySys SSIS PowerPack to optimise your data integration tasks. By applying this pattern, you can extend your solution to include transformations, data validation, error handling, and scheduling through SQL Server Agent.

Need Help?

If you encounter any issues, our support team is here to help:

1. Live Chat – Use the chat widget (bottom-right corner of this page)
2. Email – support@zappysys.com
3. Support center – Visit the ZappySys Support Portal

The post SSIS Tutorial: How to get data from Birdeye appeared first on ZappySys Blog.

Pipedrive is a CRM platform used by sales teams to manage leads, deals, contacts, activities, and pipelines. Many organisations need this data in Power BI to monitor sales performance, pipeline health, conversion rates, and team productivity.

Using ZappySys ODBC Driver for REST APIs, you can connect to Pipedrive APIs, query CRM data with SQL, and load it directly into Power BI dashboards without writing custom connector code.

This tutorial shows how to create an ODBC DSN for Pipedrive API, preview data, connect to Power BI, and build analytics using custom SQL queries.

Prerequisites

1. A Pipedrive account with API access enabled.
2. Power BI Desktop is installed on your machine.
3. Basic understanding of SQL and Power BI data modelling.
4. Finally, install the ZappySys ODBC Driver.

Steps

Get Pipedrive API token

1. Sign in to your Pipedrive account.
2. Go to Personal Preferences and open API.
3. Copy your API token and store it securely.
   

   Pipedrive – Personal token

Create a new ODBC DSN using ZappySys JSON Driver

1. Search for ODBC in the Windows Start menu and open ODBC Data Source Administrator.
   
2. In the User DSN or System DSN tab, click Add.
3. Select ZappySys JSON Driver and click Continue.
   

Configure the Connection and API Settings

1. Enter the base URL:
   https://api.pipedrive.com/v1/deals?api_token={your_api_token}
2. Set HTTP Request Method to GET.
3. Set JSON Path Filter to:
   

   $.data[*]

4. Click Test Connection to verify connectivity, then click OK to save the DSN.
   

   Pipedrive – JSON Driver configuration

Preview Pipedrive data in ODBC Driver

1. Go to the Preview tab.
2. Generate a query, then click Preview Data to confirm rows are returned.
3. If no data is returned, verify your API token, endpoint URL, and JSON Path Filter.
   

   Pipedrive – JSON Driver result

Connect Power BI to Pipedrive via ODBC

1. Open Power BI Desktop.
2. Click Get Data from the Home ribbon.
3. Click More…, search for ODBC, and click Connect.
   

   Power BI – Get data from ODBC connection

4. Select your Pipedrive ODBC DSN from the dropdown list.
5. Optionally, enter a custom SQL query in Advanced options.
   

   Pipedrive – Power BI select driver

6. Click OK, then click Load to import data.
   

   Pipedrive – Power BI preview

7. In the Navigator window, select the columns you want to load.

Query Pipedrive data with SQL

For advanced analytics, use custom SQL queries in Power BI or the ODBC tool.

Example 1: List All Deals

SELECT 
    id,
    title,
    value,
    currency,
    status,
    stage_id,
    add_time,
    update_time
FROM data

Example 2: Open Deals Only

SELECT 
    id,
    title,
    value,
    currency,
    stage_id,
    expected_close_date
FROM data
WHERE status = 'open'

Conclusion

Connecting Pipedrive to Power BI using the ZappySys ODBC Driver makes it easy to build CRM analytics dashboards without building custom connectors. With SQL-based access to Pipedrive API responses, you can monitor pipeline performance, deal progression, and sales activity in one place.

Explore the full capabilities of the Zappysys ODBC Drivers to integrate multiple data sources and build comprehensive Power BI dashboards. By combining Pipedrive data with other sources, you can create unified views of your operational data.

Need help?

If you encounter any issues, our support team is here to help:

1. Live Chat – Use the chat widget (bottom-right corner of this page)
2. Email – support@zappysys.com
3. Support Center – Visit the ZappySys Support Portal

The post How to get data from Pipedrive into Power BI appeared first on ZappySys Blog.

Introduction

Pipedrive is a popular CRM platform used by sales teams to manage deals, leads, contacts, activities, and pipelines. It offers REST APIs that let you access CRM data programmatically. Many organisations extract Pipedrive data into SQL Server for operational reporting, sales dashboards, and analytics.

In this tutorial, you will learn how to connect Pipedrive APIs to SQL Server Integration Services (SSIS) using ZappySys SSIS PowerPack. This approach helps you build repeatable ETL pipelines without writing custom API integration code.

Prerequisites

1. SQL Server Integration Services (SSIS) is installed.
2. Visual Studio with SSIS extension or SQL Server Data Tools (SSDT) is available.
3. A Pipedrive account with API access.
4. A valid Pipedrive API token.
5. ZappySys SSIS PowerPack is installed on your machine.

Steps

Generate Pipedrive API Token

1. Sign in to your Pipedrive account.
2. Go to Personal Preferences > API.
3. Copy your API token and store it securely.
   

   Pipedrive – Personal token

Create and Configure JSON Source

1. In your SSIS package, navigate to the Variables section and create the following variables: PersonalToken (String) — Enter the Personal Token from the previous step.
2. Drag a Data Flow Task into Control Flow and open it.
   

   Drag and drop Data flow

3. In Data Flow, drag and drop the ZS JSON Source.
   

   SSIS JSON Source – Drag and Drop

4. Open the JSON Source
5. Set the API URL with the variable. Example to extract deals:
   

   https://api.pipedrive.com/v1/deals?api_token={{User::PersonalToken}}

6. Set HTTP Request Method to GET.
7. Set JSON Path Filter to:
   

   $.data[*]

8. Click Preview to confirm rows are returned, then click OK.
   

   Pipedrive – JSON source configuration

Use Other Pipedrive Endpoints (Optional)

You can reuse the same SSIS package pattern with other Pipedrive endpoints:

• Persons: https://api.pipedrive.com/v1/persons?api_token={your_api_token}
• Organizations: https://api.pipedrive.com/v1/organizations?api_token={your_api_token}
• Activities: https://api.pipedrive.com/v1/activities?api_token={your_api_token}
• Leads: https://api.pipedrive.com/v1/leads?api_token={your_api_token}
• Pipelines: https://api.pipedrive.com/v1/pipelines?api_token={your_api_token}

Configure Destination Component

1. Add an Upsert destination (or another destination of your choice, such as a Trash destination or a JSON/CSV/XML destination).
2. Connect the output arrow from JSON Source to the destination component.
3. Configure your SQL Server connection and target table.
4. Check the options you want to use: Insert or/and Update
5. Map the source columns to destination columns in the Mappings section.
   

   Pipedrive  – Upsert destination

6. Click OK and execute the package.
7. Validate that Pipedrive  rows have been loaded into your SQL Server table.
   

   Pipedrive  – Upsert Destination example

Conclusion

You now have a working SSIS integration to extract Pipedrive data using ZappySys SSIS PowerPack and Pipedrive REST APIs. With this pattern, you can automate extraction of deals, contacts, and activity data into SQL Server for reporting and analytics.

Explore the full capabilities of the ZappySys SSIS PowerPack to optimize your data integration tasks. By applying this pattern, you can extend your solution to include transformations, data validation, error handling, and scheduling through SQL Server Agent.

Need Help?

If you encounter any issues, our support team is here to help:

1. Live Chat – Use the chat widget (bottom-right corner of this page)
2. Email – support@zappysys.com
3. Support Center – Visit the ZappySys Support Portal

The post SSIS tutorial: How export data from Pipedrive appeared first on ZappySys Blog.

WooCommerce is one of the most widely used eCommerce platforms for WordPress websites. Store owners and analysts often need to combine WooCommerce order, product, and customer data with other business metrics in Power BI for reporting and decision-making. Using Zappysys ODBC Driver for REST APIs, you can connect WooCommerce to Power BI and query API data using SQL without custom coding.

This tutorial demonstrates how to configure WooCommerce ODBC connectivity and load WooCommerce data into Power BI for interactive dashboards.

Prerequisites

1. A WordPress site with WooCommerce installed.
2. Microsoft Power BI Desktop is installed on your machine.
3. Basic knowledge of ODBC and SQL queries.
4. Finally, install the ZappySys ODBC Driver.

Steps

Generate WooCommerce API Credentials

1. Sign in to your WordPress admin dashboard.
2. Go to WooCommerce > Settings > Advanced > REST API.
3. Click Add key.
4. Enter a description (for example, Power BI Integration), select a user, and set permissions to Read or Read/Write as needed.
5. Click Generate API key.
6. Copy the Consumer Key and Consumer Secret, then store them securely.
   

   Woocommerce – Get API key

Create a new ODBC DSN using ZappySys JSON Driver

1. Search for ODBC in the Windows Start menu and open ODBC Data Source Administrator.
   
2. In the User DSN or System DSN tab, click Add.
3. Select ZappySys JSON Driver and click Continue.
   

Configure WooCommerce API Connection

1. In the DSN configuration window, enter the base endpoint in Base URL:
   

   https://your-website.com/wp-json/wc/v3/orders

2. Set Connection Type to HTTP and click to configure.
3. Select Basic Auth (Username/Password).
4. Set Username to your WooCommerce Consumer Key.
5. Set the password to your WooCommerce Consumer Secret.
6. Click OK to save the connection.
   

   Woocommerce – ODBC HTTP connection

7. Set HTTP Request Method to GET.
8. Set JSON Path Filter to:
   

   $.data[*]

9. Click Test Connection to verify connectivity, then click OK to save the DSN.
   

   Woocommerce – ODBC JSON Driver configuration

Preview WooCommerce Data in ODBC Driver

1. Open the DSN and go to the Preview tab.
2. Click Preview Data to validate that WooCommerce order rows are visible.
3. If rows are not returned, check the store URL, credentials, SSL, and API permissions.
   

   Woocommerce – ODBC Preview data

Connect Power BI to WooCommerce via ODBC

1. Open Power BI Desktop.
2. Click Get Data from the Home ribbon.
3. Click More…, search for ODBC, and click Connect.
   

   Power BI – Get data from ODBC connection

4. Select your WooCommerce ODBC DSN from the dropdown.
5. Optionally, enter a custom SQL query in Advanced options.
6. Click OK, then click Load to import data.
   

   Woocommerce – Power BI select connection

7. Verify the result and click Load.
   

   Woocommerce – Power BI loads the data

8. In the Navigator window, select the columns you want to load.
   

   Woocommerce – Power BI final result

Conclusion

By connecting WooCommerce to Power BI through the ZappySys ODBC REST/JSON driver, you can quickly turn raw store API data into interactive dashboards. This approach simplifies integration by allowing you to use SQL for data extraction and Power BI for modelling and visualisation.

Explore the full capabilities of the Zappysys ODBC Drivers to integrate multiple data sources and build comprehensive Power BI dashboards. By combining GitHub data with other sources, you can create unified views of your operational data.

Need Help?

If you encounter any issues, our support team is here to help:

1. Live Chat — Use the chat widget (bottom-right corner of this page)
2. Email — support@zappysys.com
3. Support Center — Visit the ZappySys Support Portal

The post How to read WooCommerce data in Power BI using ODBC appeared first on ZappySys Blog.