Azure Stream Analytics & Machine Learning Integration With RealTime Twitter Sentiment Analytics Dashboard on PowerBI

Recently, it has been introduced the integration of ASA & AML available as preview update & it’s possible to add AML web service URL & API key as ‘custom function‘ with ASA input. In this demo, realtime tweets are collected based on keywords like ‘#HappyHolidays2016‘, ‘#MerryChristmas‘, ‘#HappyNewYear2016‘ & those are directly stored on a .csv file saved on OneDrive. Here goes the solution architecture diagram of the POC.

 

 

Now, add the Service Bus event hub endpoint as input to the ASA job, while deploy the ‘Twitter Predictive Sentiment Analytics Model‘  & click on ‘Open in Studio‘ to start deploy the model. Don’t forget to run the solution before deploying.

 

Once the model is deployed, open the ‘Web Service‘ dashboard page to get the model URL & API key, click on default endpoint -> download the excel 2010 or earlier apps. Collect the URL & API key to apply it to ASA function credentials for AML deployment.

Next, create an ASA job & add the event hub credentials where the real world tweets are getting pushed & click on ‘Functions‘ tab of ASA job to add the AML credentials. Provide model name, URL & API key of the model & Once, it’s added, click on Save.

 

Now, add the following ASA SQL to aggregate the realtime tweets sentiment scores coming out from predictive twitter sentiment model.

 

Provide the output as Azure Blob storage, add a container name & serialization type as CSV & start the ASA job. Also, start importing data into PowerBI desktop from the ASA output Azure blob storage account.

 

 

PowerBI desktop contains in-built power Query to start preparing the ASA output data & processing data types. Choose the AML model sentiment score datatype as decimal type & TweetTexts as Text(String) type.

 

Start building the ‘Twitter Sentiment Analytics‘ dashboard powered by @AzureStreaming & Azure Machine Learning API with realworld tweet streaming, there’re some cool custom visuals are available on PowerBI.  I’ve used some visuals here like ‘wordcloud‘ chart which depicts some of the highly scored positive sentiment contained tweets with most specific keywords like ‘happynewyear2016‘, ‘MerryChristmas‘,’HappyHolidays‘ etc.

 

While, in the donut chart, the top 10 tweets with most positive sentiment counts are portrayed with the specific sentiment scores coming from AML predictive model experiment integrated with ASA jobs.

~Wish you HappyHolidays 2016!

Pushing realtime Sensors data into ASA & visualize into Near Real-Time (NRT) PowerBI dashboard– frontier of IoT

As per as the last demo on IoT foundation stuffs, we’ve seen how it’s possible to leverage the real-time data insights from social media datasets like Twitter with some keywords. In this demo, we are trying to pushing realtime sensors data from Windows Phone device to Azure Stream Analytics (through Service Bus EventHub channels) & after processing in ASA hub publishing out to realtime PowerBI dashboard or near real-time analytics(NRT) on PowerView for Excel by pushing out ASA events to Azure SQL database through Excel PowerQuery.

An overview of n-tier architecture of  ASA on IoT foundation is like this:

 

While, IoT always enables customers to connect their own device on Azure cloud platform & bring out some real business value from it, whether it produces #BigData or #SmallData.

Another topic is pretty important is to get insights from Weblogs or telemetry data which can bring out good sentiment, click stream analytics values with machine learning.

Here goes a good high level discussion from IoT team.

Coming back to the demo, so, first implemented a sample app for generating Accelerometer 3D events (X, Y, Z) on Windows Phone & Windows Store devices(Universal app) & pushing the generated events as block blob to Azure Service Bus Event Hub.

Attached sample code snippet.

private async void ReadingChanged(object sender, AccelerometerReadingChangedEventArgs e)
{

await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () =>
{
AccelerometerReading reading = e.Reading;
ScenarioOutput_X.Text = String.Format(“{0,5:0.00}”, reading.AccelerationX);
ScenarioOutput_Y.Text = String.Format(“{0,5:0.00}”, reading.AccelerationY);
ScenarioOutput_Z.Text = String.Format(“{0,5:0.00}”, reading.AccelerationZ);
i++;

//Coordinate_X = String.Format(“{0,5:00.00}”,Coordinate_X + ScenarioOutput_X.Text);
//Coordinate_Y = String.Format(“{0,5:00.00}”, Coordinate_Y + ScenarioOutput_Y.Text);
//Coordinate_Z = String.Format(“{0,5:00.00}”, Coordinate_Z + ScenarioOutput_Z.Text);
dataDetails = i +”,”+ reading.AccelerationX + “,” + reading.AccelerationY + “,” + reading.AccelerationZ;

NewDataFile += Environment.NewLine + dataDetails;

});
CloudStorageAccount storageAccount = CloudStorageAccount.Parse(“DefaultEndpointsProtocol=https;AccountName=yourazurestorageaccountname;

AccountKey=yourazurestorageaccountkey”);

CloudBlobClient blobClient = storageAccount.CreateCloudBlobClient();

CloudBlobContainer container = blobClient.GetContainerReference(“accelerometer”);
await container.CreateIfNotExistsAsync();
//if (x == false)
//{
// await container.CreateAsync();
//}

CloudBlockBlob blockBlob = container.GetBlockBlobReference(newFileName);
// bool y = await blockBlob.ExistsAsync();
//if (!blockBlob.Equals(newFileName))
//{
container.GetBlockBlobReference(newFileName);
// await blockBlob.UploadTextAsync(dataDetails);

await blockBlob.UploadTextAsync(Headers + Environment.NewLine+ NewDataFile);
}

 

You can download the whole visual studio solution on Github.

Next challenge as usual is to send real sensor events to event hubs with accurate consumer key & publish millions of events to event hub at a time.

Here goes sample code snippet.

class Program
{
static string eventHubName = “youreventhubname”;
static string connectionString = GetServiceBusConnectionString();
static string data = string.Empty;
static void Main(string[] args)
{

string csv_file_path = string.Empty;
install();
//string csv_file_path = @””;
string[] filePath = Directory.GetFiles(@”Your CSV Sensor Data file directory”, “*.csv”);
int size = filePath.Length;
for (int i = 0; i < size; i++)
{
Console.WriteLine(filePath[i]);
csv_file_path = filePath[i];
}

DataTable csvData = GetDataTableFromCSVFile(csv_file_path);
Console.WriteLine(“Rows count:” + csvData.Rows.Count);
DataTable table = csvData;
foreach (DataRow row in table.Rows)
{
// Console.WriteLine(“—Row—“);
foreach (var item in row.ItemArray)
{

data = item.ToString();
Console.Write(data);

var eventHubClient = EventHubClient.CreateFromConnectionString(connectionString, eventHubName);
//while (true)
//{

try
{
foreach (DataRow rows in table.Rows)
{
var info = new Accelerometer
{

ID = rows.ItemArray[0].ToString(),
Coordinate_X = rows.ItemArray[1].ToString(),
Coordinate_Y = rows.ItemArray[2].ToString(),
Coordinate_Z = rows.ItemArray[3].ToString()

};
var serializedString = JsonConvert.SerializeObject(info);
var message = data;
Console.WriteLine(“{0}> Sending events: {1}”, DateTime.Now.ToString(), serializedString.ToString());
eventHubClient.SendAsync(new EventData(Encoding.UTF8.GetBytes(serializedString.ToString())));

}
}
catch (Exception ex)
{
Console.ForegroundColor = ConsoleColor.Red;
Console.WriteLine(“{0} > Exception: {1}”, DateTime.Now.ToString(), ex.Message);
Console.ResetColor();
}
Task.Delay(200);
//}
}

}
// Console.ReadLine();

Console.WriteLine(“Press Ctrl-C to stop the sender process”);
Console.WriteLine(“Press Enter to start now”);
Console.ReadLine();

// SendingRandomMessages().Wait();

}

public static void install()
{
string url = @”https://…………blob.core.windows.net/accelerometer/AccelerometerSensorData.csv&#8221;;
WebClient wc = new WebClient();
wc.DownloadFileCompleted += new AsyncCompletedEventHandler(Completed);
wc.DownloadProgressChanged += new DownloadProgressChangedEventHandler(ProgressChanged);
// Console.WriteLine(“Download OnProgress……”);

ConsoleHelper.ProgressTitle = “Downloading”;
ConsoleHelper.ProgressTotal = 10;
for (int i = 0; i <= 10; i++)
{
ConsoleHelper.ProgressValue = i;
Thread.Sleep(500);
if (i >= 5)
{
ConsoleHelper.ProgressHasWarning = true;
}
if (i >= 8)
{
ConsoleHelper.ProgressHasError = true;
}
}
ConsoleHelper.ProgressTotal = 0;
try
{
wc.DownloadFile(new Uri(url), @”\ASA\Sensors\Accelerometer\AccelerometerSensorData.csv”);
}
catch (Exception ex)
{
while (ex != null)
{
Console.WriteLine(ex.Message);
ex = ex.InnerException;
}
}
}
public static void Completed(object sender, AsyncCompletedEventArgs e)
{
Console.WriteLine(“Download Completed!”);
}

public static void ProgressChanged(object sender, DownloadProgressChangedEventArgs e)
{
Console.WriteLine(“{0} Downloaded {1} of {2} bytes,{3} % Complete….”,
(string)e.UserState,
e.BytesReceived,
e.TotalBytesToReceive,
e.ProgressPercentage);
DrawProgressBar(0, 100, Console.WindowWidth, ‘1’);
}

private static void DrawProgressBar(int complete, int maxVal, int barSize, char ProgressCharacter)
{
Console.CursorVisible = false;
int left = Console.CursorLeft;
decimal perc = (decimal)complete / (decimal)maxVal;
int chars = (int)Math.Floor(perc / ((decimal)1 / (decimal)barSize));
string p1 = String.Empty, p2 = String.Empty;

for (int i = 0; i < chars; i++) p1 += ProgressCharacter;
for (int i = 0; i < barSize – chars; i++) p2 += ProgressCharacter;

Console.ForegroundColor = ConsoleColor.Green;
Console.Write(p1);
Console.ForegroundColor = ConsoleColor.DarkGreen;
Console.Write(p2);

Console.ResetColor();
Console.Write(“{0}%”, (perc * 100).ToString(“N2”));
Console.CursorLeft = left;
}
private static DataTable GetDataTableFromCSVFile(string csv_file_path)
{
DataTable csvData = new DataTable();
string data = string.Empty;
try
{
using (TextFieldParser csvReader = new TextFieldParser(csv_file_path))
{
csvReader.SetDelimiters(new string[] { “,” });
csvReader.HasFieldsEnclosedInQuotes = true;

//read column names
string[] colFields = csvReader.ReadFields();
foreach (string column in colFields)
{
DataColumn datecolumn = new DataColumn(column);
datecolumn.AllowDBNull = true;
csvData.Columns.Add(datecolumn);
}
while (!csvReader.EndOfData)
{
string[] fieldData = csvReader.ReadFields();

for (int i = 0; i < fieldData.Length; i++)
{
if (fieldData[i] == “”)
{
fieldData[i] = null;
}
}
csvData.Rows.Add(fieldData);

}
}
}
catch (Exception ex)
{

}
return csvData;
}

 

Now, built out ASA SQL query with specific window interval like in this demo, used ‘SlidingWindow(Second,no of interval)’ which generates computation on event hubs data based on the specific time interval mentioned in window.

 

Next, start implement the processed output visualization on PowerBI preview portal by selecting ‘Output’ tab of ASA job. Once, you provide all the dataset name of output & start the ASA job, on PowerBI portal, would be able to see the specific dataset is created with a small yellow star icon beside.

 

Here goes a step by step demonstration with video available on my Youtube channel.