hfleitas

// https://aka.ms/adx.blog

UsagePreliminary
| where Date == datetime(2023-08-01) 
| where MeterId == "2c57ed84-f939-4f5c-ba90-782349a367b8"
| where ResourceId == "/subscriptions/9ec51cfd-5ca7-4d76-8101-dd0a4abc5674/resourcegroups/mc_analyticsengine_analyticsengine_eastus/providers/microsoft.compute/virtualmachinescalesets/aks-secretagent-37798712-vmss"


UsagePreliminary
| summarize Quantity=sum(Quantity)

hfleitas

#connect @MyFreeCluster/MyDatabase

let data = dynamic(
```
<?xml version="1.0" encoding="UTF-8"?>
<duration>
    <value>118.0</value>
    <count>5.0</count>
    <min>100.0</min>
    <max>150.0</max>

hfleitas

//external
#connect cluster('kvc0wyc7zw8bafmkdc1bzv.southcentralus').database('MyDatabase')

.set-or-append async OpenAIQnA <|
let d= dynamic(
{
    "statusCode": 200,
    "headers": {
        "Transfer-Encoding": "chunked",
        "Vary": "Accept-Encoding",

hfleitas

let series_cosine_similarity_fl=(vec1:dynamic, vec2:dynamic, vec1_size:real=double(null), vec2_size:real=double(null))
{
    let dp = series_dot_product(vec1, vec2);
    let v1l = iff(isnull(vec1_size), sqrt(series_dot_product(vec1, vec1)), vec1_size);
    let v2l = iff(isnull(vec2_size), sqrt(series_dot_product(vec2, vec2)), vec2_size);
    dp/(v1l*v2l)
};
let s1=pack_array(0, 1);
let s2=pack_array(sqrt(2), sqrt(2));
print angle=acos(series_cosine_similarity_fl(s1, s2))/(2*pi())*360

hfleitas

// PLEASE TRY ON YOUR OWN FIRST! https://aka.ms/adxinaday
// Labs are subject to change in the future based on feedback.
// SPOILER ALERT: Answers below. 

//v2
.create-merge table logsRaw(Timestamp:datetime, Source:string, Node:string, Level:string, Component:string, ClientRequestId:string, Message:string, Properties:dynamic) 


//3.2
logsRaw

hfleitas

#connect cluster('kvc43f0ee6600e24ef2b0e.southcentralus.kusto.windows.net').database('MyDatabase')

// GET
let Uri = "https://prices.azure.com/api/retail/prices?$filter=serviceName eq 'Azure Purview' and location eq 'EU West'";
evaluate http_request(Uri)
| project ResponseBody.Items
| mv-expand ResponseBody_Items
| evaluate bag_unpack(ResponseBody_Items)

hfleitas

#connect cluster('adxpm10774.eastus.kusto.windows.net').database('IoTAnalytics')

.set-or-replace PlotlyTemplate <| cluster('help.kusto.windows.net').database('Samples').PlotlyTemplate

PlotlyTemplate


.create-or-alter function with (folder = "Packages\\Plotly", docstring = "Render 3D scatter chart using plotly template")
plotly_scatter3d_fl(tbl:(*), x_col:string, y_col:string, z_col:string, aggr_col:string='', chart_title:string='3D Scatter chart')
{

hfleitas

#connect cluster('https://demo11.westus.kusto.windows.net').database('ML')

demo_make_series1
| take 100

demo_make_series1
| summarize count() by bin(TimeStamp, 1h)
| render timechart 

demo_make_series1

hfleitas

#connect cluster('adxpm10774.eastus').database('IoTAnalytics')

//Data lands in the StageIoTRaw table so lets look at that first
.show table StageIoTRawData

StageIoTRawData
| limit 10 

//We use an update policy to flatten this data and append it to the Thermostat table
.show table Thermostats

hfleitas

#connect cluster('adxpm10774.eastus').database('IoTAnalytics')


//Data lands in the StageIoTRaw table so lets look at that first
.show table StageIoTRawData

StageIoTRawData
| limit 10 

//We use an update policy to flatten this data and append it to the Thermostat table