JSON Connector for Python In this article you will learn how to integrate JSON data in Python (live / bi-directional connection to JSON). JSON Connector can be used to extract and output JSON data coming from REST API web service calls (Web URL) or direct JSON String (variables or DB columns) or local JSON files data. JSON Connector also supports JSONPath to filter data from nested array/sub-documents. This Connector is optimized to work with very large JSON string.. Using JSON Connector you will be able to connect, read, and write data from within Python. Follow the steps below to see how we would accomplish that. Download Documentation The driver mentioned above is part of ODBC PowerPack which is a collection of high-performance Drivers for various API data source (i.e. REST API, JSON, XML, CSV, Amazon S3 and many more). Using familiar SQL query language you can make live connections and read/write data from API sources or JSON / XML / CSV Files inside SQL Server (T-SQL) or your favorite Reporting (i.e. Power BI, Tableau, Qlik, SSRS, MicroStrategy, Excel, MS Access), ETL Tools (i.e. Informatica, Talend, Pentaho, SSIS). You can also call our drivers from programming languages such as JAVA, C#, Python, PowerShell etc. If you are new to ODBC and ZappySys ODBC PowerPack then check the following links to get started. ODBC PowerPack - System Requirements and Installation Getting Started - Using ODBC PowerPack ODBC API Driver Help File

Connect to JSON in other apps Connect to JSON in SSIS Connect to JSON in SQL Server Connect to JSON in Power BI Connect to JSON in SSRS Connect to JSON in Informatica Connect to JSON in MS Access Connect to JSON in MS Excel Connect to JSON in SSAS Connect to JSON in C# Connect to JSON in JAVA Connect to JSON in Tableau Connect to JSON in SAP Crystal Reports Connect to JSON in Azure Data Factory (Pipeline) Connect to JSON in Azure Data Factory (SSIS) Connect to JSON in Talend Studio Connect to JSON in UiPath Connect to JSON in PowerShell Connect to JSON in ODBC

Create ODBC Data Source (DSN) based on ZappySys JSON Driver

Step-by-step instructions

To get data from JSON using Python we first need to create a DSN (Data Source) which will access data from JSON. We will later be able to read data using Python. Perform these steps:

1. Install ZappySys ODBC PowerPack.

2. Open ODBC Data Sources (x64):
   

3. Create a User Data Source (User DSN) based on ZappySys JSON Driver

   ZappySys JSON Driver

   
   You should create a System DSN (instead of a User DSN) if the client application is launched under a Windows System Account, e.g. as a Windows Service. If the client application is 32-bit (x86) running with a System DSN, use ODBC Data Sources (32-bit) instead of the 64-bit version.

4. Select Url or File and paste the following Url for this example OR you can load existing connection string as per this article.
   

   NOTE: Here for demo, We are using odata API, but you need to refer your own API documentation and based on that you need to use your own API URL and need to configure connection based on API Authentication type

   https://services.odata.org/V3/Northwind/Northwind.svc/Customers?$format=json

5. Now enter JSONPath expression in Array Filter textbox to extract only specific part of JSON file as below ($.value[*] will get content of value attribute from JSON document. Value attribute is array of JSON documents so we have to use [*] to indicate we want all records of that array)
   

   NOTE: Here, We are using our desired filter, but you need to select your desired filter based on your requirement.

   
   Click on Test Connection button to view whether the Test Connection is SUCCESSFUL or Not.
   $.value[*]
   

6. Once you configured a data source, you can preview data. Hit Preview tab, and use similar settings to preview data:
   

7. Click OK to finish creating the data source

8. That's it; we are done. In a few clicks we configured the call to JSON API using ZappySys JSON Connector.

Video instructions

Read data in Python

Using ODBC DSN

1. Python code to get the data:
   

   JsonDSN')

   

2. When you run the code it will make the API call and read the data:
   

3. Here is Python program's code in text format:

   
       import pyodbc
       conn = pyodbc.connect('DSN=JsonDSN')
   
       cursor = conn.cursor()
   
       #execute query to fetch data from API service
       cursor.execute("SELECT id,title FROM products")
   
       row = cursor.fetchone()
       while row:
           print(row)
           row = cursor.fetchone()
   
       ##For loop example
       #for row in cursor:
       #    print(row)
   

Using a full ODBC connection string

If you want to avoid being dependent on a DSN and creating multiple DSNs for each platform (x86, x64), then you can use a fully qualified connection string. Simply go to your DSN and copy the Connection String:

1. Open ODBC data source configuration and click Copy settings:
   

   ZappySys JSON Driver - JSON

   JSON Connector can be used to extract and output JSON data coming from REST API web service calls (Web URL) or direct JSON String (variables or DB columns) or local JSON files data. JSON Connector also supports JSONPath to filter data from nested array/sub-documents. This Connector is optimized to work with very large JSON string.

   JsonDSN

   
2. The window opens, telling us the connection string was successfully copied to the clipboard:

3. Then in your Python code use Connection String when initializing OdbcConnection object, for example:

   conn = pyodbc.connect('DRIVER={ZappySys JSON Driver};ServiceUrl=https://yourservices.provider.com/api/xxxx....;AuthName=Http;')

How to install `pyodbc` in the Python?

Configuring pagination in the JSON Driver

ZappySys JSON Driver equips users with powerful tools for seamless data extraction and management from REST APIs, leveraging advanced pagination methods for enhanced efficiency. These options are designed to handle various types of pagination structures commonly used in APIs. Below are the detailed descriptions of these options:

1. Page-based Pagination: This method works by retrieving data in fixed-size pages from the Rest API. It allows you to specify the page size and navigate through the results by requesting different page numbers, ensuring that you can access all the data in a structured manner.

2. Offset-based Pagination: With this approach, you can extract data by specifying the starting point or offset from which to begin retrieving data. It allows you to define the number of records to skip and fetch subsequent data accordingly, providing precise control over the data extraction process.

3. Cursor-based Pagination: This technique involves using a cursor or a marker that points to a specific position in the dataset. It enables you to retrieve data starting from the position indicated by the cursor and proceed to subsequent segments, ensuring that you capture all the relevant information without missing any records.

4. Token-based Pagination: In this method, a token serves as a unique identifier for a specific data segment. It allows you to access the next set of data by using the token provided in the response from the previous request. This ensures that you can systematically retrieve all the data segments without duplication or omission.

Utilizing these comprehensive pagination features in the ZappySys JSON Driver facilitates efficient data management and extraction from REST APIs, optimizing the integration and analysis of extensive datasets.

For more detailed steps, please refer to this link: How to do REST API Pagination in SSIS / ODBC Drivers

Authentication

ZappySys offers various authentication methods to securely access data from various sources. These authentication methods include OAuth, Basic Authentication, Token-based Authentication, and more, allowing users to connect to a wide range of data sources securely.

ZappySys Authentication is a robust system that facilitates secure access to data from a diverse range of sources. It includes a variety of authentication methods tailored to meet the specific requirements of different data platforms and services. These authentication methods may involve:

1. OAuth: ZappySys supports OAuth for authentication, which allows users to grant limited access to their data without revealing their credentials. It's commonly used for applications that require access to user account information.

2. Basic Authentication: This method involves sending a username and password with every request. ZappySys allows users to securely access data using this traditional authentication approach.

3. Token-based Authentication: ZappySys enables users to utilize tokens for authentication. This method involves exchanging a unique token with each request to authenticate the user's identity without revealing sensitive information.

By implementing these authentication methods, ZappySys ensures the secure and reliable retrieval of data from various sources, providing users with the necessary tools to access and integrate data securely and efficiently. For more comprehensive details on the authentication process, please refer to the official ZappySys documentation or reach out to their support team for further assistance.

For more details, please refer to this link: ZappySys Connections

Advanced topics

Create Custom Stored Procedure in ZappySys Driver

You can create procedures to encapsulate custom logic and then only pass handful parameters rather than long SQL to execute your API call.

Steps to create Custom Stored Procedure in ZappySys Driver. You can insert Placeholders anywhere inside Procedure Body. Read more about placeholders here

1. Go to Custom Objects Tab and Click on Add button and Select Add Procedure:
   

2. Enter the desired Procedure name and click on OK:
   

3. Select the created Stored Procedure and write the your desired stored procedure and Save it and it will create the custom stored procedure in the ZappySys Driver:
   Here is an example stored procedure for ZappySys Driver. You can insert Placeholders anywhere inside Procedure Body. Read more about placeholders here
   

   CREATE PROCEDURE [usp_get_orders]
       @fromdate = '<<yyyy-MM-dd,FUN_TODAY>>'
    AS
       SELECT * FROM Orders where OrderDate >= '<@fromdate>';
   

   
   

4. That's it now go to Preview Tab and Execute your Stored Procedure using Exec Command. In this example it will extract the orders from the date 1996-01-01:
   

   Exec usp_get_orders '1996-01-01';

   
   

5. Let's generate the SQL Server Query Code to make the API call using stored procedure. Go to Code Generator Tab, select language as SQL Server and click on Generate button the generate the code.
   As we already created the linked server for this Data Source, in that you just need to copy the Select Query and need to use the linked server name which we have apply on the place of [MY_API_SERVICE] placeholder.
   

   SELECT * FROM OPENQUERY([MY_API_SERVICE], 'EXEC usp_get_orders @fromdate=''1996-07-30''')

   
   

6. Now go to SQL served and execute that query and it will make the API call using stored procedure and provide you the response.
   

Create Custom Virtual Table in ZappySys Driver

ZappySys API Drivers support flexible Query language so you can override Default Properties you configured on Data Source such as URL, Body. This way you don't have to create multiple Data Sources if you like to read data from multiple EndPoints. However not every application support supplying custom SQL to driver so you can only select Table from list returned from driver.

If you're dealing with Microsoft Access and need to import data from an SQL query, it's important to note that Access doesn't allow direct import of SQL queries. Instead, you can create custom objects (Virtual Tables) to handle the import process.

Many applications like MS Access, Informatica Designer wont give you option to specify custom SQL when you import Objects. In such case Virtual Table is very useful. You can create many Virtual Tables on the same Data Source (e.g. If you have 50 URLs with slight variations you can create virtual tables with just URL as Parameter setting.

1. Go to Custom Objects Tab and Click on Add button and Select Add Table:
   

2. Enter the desired Table name and click on OK:
   

3. And it will open the New Query Window Click on Cancel to close that window and go to Custom Objects Tab.

4. Select the created table, Select Text Type AS SQL and write the your desired SQL Query and Save it and it will create the custom table in the ZappySys Driver:
   Here is an example SQL query for ZappySys Driver. You can insert Placeholders also. Read more about placeholders here
   

   SELECT
     "ShipCountry",
     "OrderID",
     "CustomerID",
     "EmployeeID",
     "OrderDate",
     "RequiredDate",
     "ShippedDate",
     "ShipVia",
     "Freight",
     "ShipName",
     "ShipAddress",
     "ShipCity",
     "ShipRegion",
     "ShipPostalCode"
   FROM "Orders"
   Where "ShipCountry"='USA'

   
   

5. That's it now go to Preview Tab and Execute your custom virtual table query. In this example it will extract the orders for the USA Shipping Country only:
   

   SELECT * FROM "vt__usa_orders_only"

   
   

6. Let's generate the SQL Server Query Code to make the API call using stored procedure. Go to Code Generator Tab, select language as SQL Server and click on Generate button the generate the code.
   As we already created the linked server for this Data Source, in that you just need to copy the Select Query and need to use the linked server name which we have apply on the place of [MY_API_SERVICE] placeholder.
   

   SELECT * FROM OPENQUERY([MY_API_SERVICE], 'EXEC [usp_get_orders] ''1996-01-01''')

   
   

7. Now go to SQL served and execute that query and it will make the API call using stored procedure and provide you the response.
   

Conclusion

In this article we discussed how to connect to JSON in Python and integrate data without any coding. Click here to Download JSON Connector for Python and try yourself see how easy it is. If you still have any question(s) then ask here or simply click on live chat icon below and ask our expert (see bottom-right corner of this page).

Download JSON Connector for Python Documentation 

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