What’s new in Azure Data Catalog


The Azure Data Catalog (aka previously PowerBI Data Catalog) has released in public preview on last monday(July 13th) @WPC15, which typically reveals a new world of storing & connecting #Data across on-prem & azure SQL database. Lets hop into a quick jumpstart on it.

Connect through Azure Data Catalog through this url  https://www.azuredatacatalog.com/ by making sure you are logging with your official id & a valid Azure subscription. Currently , it’s free for first 50 users & upto 5000 registered data assets & in standard edition, upto 100 users & available upto 1M registered data assets.

Provision

 

Lets start with the signing of the official id into the portal.

Signin

Once it’s provisioned, you will be redirected to this page to launch a windows app of Azure Data Catalog.

AzureDC

 

It would start downloading the app from clickonce deployed server.

ADCapp

 

After it downloaded & would prompt to select server , at this point it has capacity to select data from SQL Server Analysis service, Reporting Service, on-prem/Azure SQL database & Oracle db.

Servers

For this demo, we used on-prem SQL server database to connect to Azure Data Catalog.

Catalog

We selected here ‘AdventureWorksLT’ database & pushed total 8 tables like ‘Customer’, ‘Product’, ‘ProductCategory’, ‘ProductDescription’,’ProductModel’, ‘SalesOrderDetail’ etc. Also, you can tags to identify the datasets on data catalog portal.

metadata-tag

Next, click on ‘REGISTER’ to register the dataset & optionally, you can include a preview of the data definition as well.

Object-registration

 

Once the object registration is done, it would allow to view on portal. Click on ‘View Portal’ to check the data catalogs.

Portal

Once you click , you would be redirected to data catalog homepage where you can search for your data by object metaname.

Search

 

SearchData

in the data catalog object portal, all of the registered metadata & objects would be visible with property tags.

Properties

You can also open the registered object datasets in excel to start importing into PowerBI.

opendata

Click on ‘Excel’ or ‘Excel(Top 1000)’ to start importing the data into Excel. The resultant data definition would in .odc format.

SaveCustomer

 

Once you open it in Excel, it would be prompted to enable custom extension. Click on ‘Enable’.

Security

From Excel, the dataset is imported to latest Microsoft PowerBI Designer Preview app to build up a custom dashboard.

ADC-PowerBI

Login into https://app.powerbi.com & click to ‘File’ to get data from .pbix file.

PowerBI

Import the .pbix file on ‘AdventureWorks’ customer details & product analytics to powerbi reports & built up a dashboard.Uploading

The PowerBI preview portal dashboard has some updates on tile details filter like extension of custom links.

PowerBI-filter

 

The PowerBI app for Android is available now, which is useful for quick glance of real-time analytics dashboards specially connected with Stream analytics & updating  real time.

WP_20150715_14_07_48_Pro

WP_20150715_14_13_33_Pro

AdventureWorks-ADC

 

 

 

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:

ASA-blog

 

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.

BUILD-Kevin-thumbnail-IoT

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.

ASAQuery

 

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.SensorsPowerBI

 

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

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