Examples:

1. Check for duplicate messages is true and suspend duplicate messages is false
   • Agreement:
   • AS2 Decode output:
2. Check for duplicate messages is true and suspend duplicate messages is true
   • Agreement:
   • AS2 Decode output: Note that isAS2MessageDuplicate property gets set to false here. That's because whenever there's a failure in the message, all of the message metadata like isAS2MessageSigned, isAS2MessageCompressed, etc. are set to false. The full failure message looks like this:
     "failed/failure: general-ediint-error-encountered Error: Message with AS2-From: Contoso, AS2-To: Fabrikam and MessageId: <RD00155D5C270B_CEDA56D1-FD2A-427B-83E7-918BFE42998A> is suspended as duplicate per party "Contoso" configuration settings"

Note:

• Duplicate check is done only for received messages while decoding.
• Message Id is used as the unique identifier of the message to identify duplicates.

1. Go to your Integration Account's overview blade and click on Maps.
2. Click on Add. Provide a name for the map, select liquid for Map type and upload the map file.

1. JSON to JSON

   • Map (AddressMap.liquid) { "name": "{{content.name}}", "address": "{{content.streetAddress | Append: ', ' | Append: content.city | Append: ', ' | Append: content.state | Append: '.'}}" }
   • Action inputs
   • Output

2. JSON to Text

   • Map (SimpleJsonToText.liquid) {{content.firstName | Append: ' ' | Append: content.lastName}}
   • Action inputs
   • Output

3. XML to JSON

   • Map (ProductXmlToJson.liquid) { "name": "{{content.product.name}}", "price": {{content.product.price}}, "transactionReferenceNumber": {{content.product.transactionReferenceNumber}} }
   • Action inputs
   • Output

4. XML to Text

   • Map (FullNameXmlToText.liquid ) {{content.student.firstName | Append: ' ' | Append: content.student.lastName}}
   • Action inputs
   • Output

1. • Similarly, I added two more blades which shows orders categorized by city and source platform.
     • Queries:
       • Orders by city (Donut and list) AzureDiagnostics | where Category == "WorkflowRuntime" | where OperationName == "Microsoft.Logic/workflows/workflowActionCompleted" | where workflowId_s == "/SUBSCRIPTIONS/<subscriptionID>/RESOURCEGROUPS/<resourceGroupName>/PROVIDERS/MICROSOFT.LOGIC/WORKFLOWS/<logicAppName>" | where resource_actionName_s == "Register_order" | project TimeGenerated, trackedProperties_customerCity_s | summarize count() by trackedProperties_customerCity_s
       • Orders by platform (Donut and list) AzureDiagnostics | where Category == "WorkflowRuntime" | where OperationName == "Microsoft.Logic/workflows/workflowActionCompleted" | where workflowId_s == "/SUBSCRIPTIONS/<subscriptionID>/RESOURCEGROUPS/<resourceGroupName>/PROVIDERS/MICROSOFT.LOGIC/WORKFLOWS/<logicAppName>" | where resource_actionName_s == "Register_order" | project TimeGenerated, trackedProperties_platform_s | summarize count()  by trackedProperties_platform_s
       • You can download this view here: OrderTracking.omsview and import in your workspace using View Designer -> Import. Tip: Use Log Search to test your queries before creating the view.
   • Click Save.
   • Now, you’d be able to see the overview tile in your Log analytics overview dashboard. Click on the overview tile to navigate to the dashboard that we just created. You can also pin the overview tile to your Azure portal dashboard.

1. SendOrder Logic App definition: SendOrder.json_
2. ProcessOrder Logic App definition: ProcessOrder.json_
3. Log analytics dashboard’s JSON:  OrderTracking.omsview
4. Log analytics search query reference: https://docs.loganalytics.io/docs/Language-Reference

• Internet access at events is usually very poor or limited and despite almost all our mobile devices have data access, if we are outside our country, data roaming it is still quite expensive;
• Alternatively, we can send/receive an email later to evaluate the event but more likely most of the attendees will ignore it and we will end up receiving little or no feedback at all.

• DevScope SmartDocumentor OCR that, not also allowed me to extracted the data from my documents and easily integrate with other systems, but also to intelligently set my OCR streams (flows), defining:
  • Different receive locations, like FTP, file or directly from scanner devices;
  • Create/test my recognition templates and review/ validate the data which is gathered;
  • But also, enabled me to connect and send the metadata, XML or JSON, through any kind of HTTP service, I could even extend it by using a PowerShell provider that would enable me to execute a PowerShell script.

• Azure Logic Apps that provided a way a very simplify and fast way to implement scalable integrations workflows in the cloud with an uncountednumber of connectors across the cloud and on-premises, the list is growing day by day, that will allow me to quickly integrate across services and protocols;
• Azure Functions to run custom snippets of C# to support Logic Apps flow and perform advanced JSON to JSON transformations;
• And finally, Power BI to create interactive data visualization (dashboards and reports)

The solution

• SmartDocumentor OCR flow will be listening in two receive locations: Share Folder and directly from the scanner device. We can specify if we want to review the documents but in this case, we will be skipping this step to allow you… yes you, the reader, to test this implementation.
• After receiving and extract the data from the documents (paper), SmartDocumentor will send the metadata to a Logic App HTTP endpoint.

• Select your 'Workspace à Datasets', and then on the top right corner click '+ Create' and then 'Streaming dataset'
• In the 'New streaming dataset', select 'API' and then click 'Next'
• In the second 'New streaming dataset', give a name to your dataset: “FeedbackForm” and then add the following elements:
  • SpeakerName (Text) – represents the name of the speaker that is obtained in the evaluation form according to the session.
  • ContentMatureFB (Number) – a value between 1 and 9 that is obtained in the evaluation form
  • GoodCommunicatorFB (Number) – a value between 1 and 9 that is obtained in the evaluation form
  • EnjoySessionFB (Number) – a value between 1 and 9 that is obtained in the evaluation form
  • MetExpectationsFB (Number) – a value between 1 and 9 that is obtained in the evaluation form
  • SpeakerAvrg (Number) – A simple average calculation (sum all the above metrics divide by 4)
  • WhoAmI (Text) – represents the type of attendee you are (developer, architect, …) that is obtained in the evaluation form
  • SpeakerPicture (Text) – picture of the speaker according to the session that is that is obtained in the evaluation form.
• And because we want to create interactive reports in order to have more insights from the event. We need to enable 'Historic data analysis' and then click 'Create'

#r "Newtonsoft.Json"

using System;
using System.Net;
using Newtonsoft.Json;
using Newtonsoft.Json.Linq;

public static async Task<object> Run(HttpRequestMessage req, TraceWriter log)
{
 log.Info($"Webhook was triggered!");

 string jsonContent = await req.Content.ReadAsStringAsync();
 dynamic data = JsonConvert.DeserializeObject(jsonContent);

 string speakerName = string.Empty;
 string speakerPicture = string.Empty;
 int pos = 0;
 for (int i = 5; i <= 8; i++)
 {
 if (!String.IsNullOrEmpty(data[i]["Value"].Value))
 {
 pos = i;
 break;
 }
 }

 switch (pos)
 {
 case 5:
 speakerName = "Ricardo Torre";
 speakerPicture = "http://blog.sandro-pereira.com/wp-content/uploads/2017/03/RicardoTorre.png";
 break;
 case 6:
 speakerName = "José António Silva e Pedro Sousa";
 speakerPicture = "http://blog.sandro-pereira.com/wp-content/uploads/2017/03/JosePedro.png";
 break;
 case 7:
 speakerName = "João Ferreira";
 speakerPicture = "http://blog.sandro-pereira.com/wp-content/uploads/2017/03/JoaoFerreira.png";
 break;
 case 8:
 speakerName = "Sandro Pereira";
 speakerPicture = "http://blog.sandro-pereira.com/wp-content/uploads/2017/03/Sandro-Pereira.png";
 break;
 default:
 speakerName = "Unknown";
 speakerPicture = "http://blog.sandro-pereira.com/wp-content/uploads/2017/03/devscope.png";
 break;
 }

 int result = 0;
 decimal avrg = (decimal)((int.TryParse(data[9]["Value"].Value, out result) ? result : 0) + (int.TryParse(data[10]["Value"].Value, out result) ? result : 0) + (int.TryParse(data[11]["Value"].Value, out result) ? result : 0) + (int.TryParse(data[12]["Value"].Value, out result) ? result : 0)) / 4;

 JObject eval =
 new JObject(
 new JProperty("SpeakerName", speakerName),
 new JProperty("SpeakerPicture", speakerPicture),
 new JProperty("ContentMatureFB", data[9]["Value"].Value),
 new JProperty("GoodCommunicatorFB", data[10]["Value"].Value),
 new JProperty("EnjoySessionFB", data[11]["Value"].Value),
 new JProperty("MetExpectationsFB", data[12]["Value"].Value),
 new JProperty("SpeakerAvrg", avrg),
 new JProperty("WhoAmI", data[30]["Value"].Value));

 log.Info($"Webhook was Complete!");

 return req.CreateResponse(HttpStatusCode.OK, new
 {
 MsgEval = eval
 });
}

• Accept a JSON through an HTTP Post. For that, we use a 'Request / Response – Manual' trigger. And in this case, because we don’t need to have friendly tokens to access the elements of the message, we don’t specify a JSON Schema.
• Call an Azure Function to transform the original OCR JSON to the expected JSON message to send to Power BI. For that, we use an 'Azure Functions' action, specifying the function that we created previously.
• After that, we use a 'Parse JSON' action, only to allow us to parse JSON content into friendly tokens (something like a quick alias to the fields) for being easily consumed in other action of the process.
• In the last step of the Logic App: we push the data into the Power BI streaming dataset created earlier by using the new 'Power BI' Connector. To do this we need to:
  • Add a '+New step', 'Add an action', and then enter 'Power BI' into the search box. Select “Add row to streaming dataset” from the actions list.
  • Select the name of the workspace and then the name of the streaming dataset
  • The next step is to select the Table titled “RealTimeData”
  • And finally, map the input data fields with the friendly tokens generated in the 'Parse JSON' action

The end result

How can you test the solution?

• Download the form – PNG format – here: https://blog.sandro-pereira.com/wp-content/uploads/2017/04/SpeaketForm_2017-0.png
• Open the picture in your favorite editor (Paint or Paint.NET) and just paint your choices, something like this. Email your form attached to sandroasp+sd@gmail.com
• Wait a few minutes, because I have a simple Microsoft Flow listening to this mailbox that:
  • Extracts the attachment
  • Sends it to SmartDocumentor OCR
  • And notifies you that the file was received
• Check your email and access to this public link to see the results: https://app.powerbi.com/view?r=eyJrIjoiZmQyNDM4YmItN2ZkYS00NzEwLThlYWYtMGQxMjQ3ZDI5ZGE2IiwidCI6IjA5ZTI1MWRjLTVlODctNDhiZi1iNGQyLTcxYjAxYWRiOTg0YSIsImMiOjh9
  • Unfortunately, updates to reports or visuals to which a Publish to web link points can take approximately one hour to be visible to users – this is a Power BI limitation - because the link is for a public Power BI report you may only see the results reflected, in the worst scenario, after one hour.

Import logic apps from Azure

Performance improvements for the embedded designer

Support for Resource Template Parameters

View active logic app run history

What Can a Connector Do

Flow

• Send email, text and push notifications
• Copy files between data sources
• Automatically collect and organize business data
• Streamline approvals and send instant alerts

Logic Apps

PowerApps

Why should you build a connector?

Drive more usage

Expand the reach of your API

How to Get Started

• Business user scenario that fits well with Flow, PowerApps and Logic Apps
• Publicly available service with stable REST APIs

Build a Custom Connector

Submit for Certification