March 06, 2021
1 Comment

Build and Optimize Data Warehouses with BigQuery

Build and Optimize Data Warehouses with BigQuery Challenge Lab, GSP340, Qwiklab

Challenge Lab

Qwiklabs [GSP340]

@prakashfoundation YouTube

click the card to watch the video.

Subscribe Immediately.

@codeGarry Github

Don't forget to follow on GitHub

click me

GSP340

Task 1:

Create a table partitioned by date

code (A):

CREATE OR REPLACE TABLE ecommerce.sample 
PARTITION BY date 
OPTIONS ( partition_expiration_days=90,
description="COVID 19 data"
) AS 
SELECT * 
FROM bigquery-public-data.covid19_govt_response.oxford_policy_tracker 
WHERE alpha_3_code != 'USA' AND alpha_3_code != 'GBR'

Task 2: Add new columns to your table

code (B)


ALTER TABLE ecommerce.sample
ADD COLUMN IF NOT EXISTS population INT64,
ADD COLUMN IF NOT EXISTS country_area FLOAT64,
ADD COLUMN IF NOT EXISTS mobility STRUCT<
avg_retail FLOAT64,
avg_grocery FLOAT64,
avg_parks FLOAT64,
avg_transit FLOAT64,
avg_workplace FLOAT64,
avg_residential FLOAT64
>

Task 3: Add country population data to the population column

code (C):

UPDATE `<PROJECT_ID>.ecommerce.sample` count
SET count.population = count1.pop_data_2019
FROM `bigquery-public-data.covid19_ecdc.covid_19_geographic_distribution_worldwide` count1
WHERE count.date = count1.date AND count.alpha_3_code=count1.country_territory_code

Task 4: Add country area data to the country_area column

code (D):

UPDATE `<PROJECT_ID>.ecommerce.sample` count
SET count.country_area = count1.country_area
FROM `bigquery-public-data.census_bureau_international.country_names_area` count1
WHERE count.country_name = count1.country_name

Task 5: Populate the mobility record data

code (E)

UPDATE `<PROJECT_ID>.ecommerce.sample` count
SET count.mobility = STRUCT<
avg_retail FLOAT64, avg_grocery FLOAT64, avg_parks FLOAT64, avg_transit FLOAT64, avg_workplace FLOAT64, avg_residential FLOAT64
>
(count1.avg_retail, count1.avg_grocery, count1.avg_parks, count1.avg_transit, count1.avg_workplace, count1.avg_residential)
FROM ( SELECT country_region, date, 
      AVG(retail_and_recreation_percent_change_from_baseline) as avg_retail,
      AVG(grocery_and_pharmacy_percent_change_from_baseline)  as avg_grocery,
      AVG(parks_percent_change_from_baseline) as avg_parks,
      AVG(transit_stations_percent_change_from_baseline) as avg_transit,
      AVG( workplaces_percent_change_from_baseline ) as avg_workplace,
      AVG( residential_percent_change_from_baseline)  as avg_residential
      FROM `bigquery-public-data.covid19_google_mobility.mobility_report`
      GROUP BY country_region, date) AS count1
WHERE count.country_name = count1.country_region
AND count.date = count1.date

Task 6: Query missing data in population & country_area columns

code (F):

SELECT DISTINCT country_name
FROM `<			>.ecommerce.sample`
WHERE population is NULL 
UNION ALL
SELECT DISTINCT country_name
FROM `<			>.ecommerce.sample`
WHERE country_area IS NULL 
ORDER BY country_name ASC

March 06, 2021
1 Comment

Ensure-Access-Identity-in-Google-Cloud-Challenge-Lab

Challenge Lab

Qwiklabs [GSP342]

@prakashfoundation YouTube

click the card to watch the video.

Subscribe Immediately.

@codeGarry Github

Don't forget to follow on GitHub

click me

GSP342

Task 1:

Create a custom security role.

Execute this command on cloud shell to create yaml file for custom role:

code (A):

nano role-definition.yaml

Replace the below content in the file:

code (B)

title: "orca_storage_update"
description: "Permissions"
stage: "ALPHA"
includedPermissions:
- storage.buckets.get
- storage.objects.get
- storage.objects.list
- storage.objects.update
- storage.objects.create

Ctrl+x --> Y--> Enter

code (C):

gcloud iam roles create orca_storage_update --project $DEVSHELL_PROJECT_ID \
--file role-definition.yaml

commands for task2 & task3:

Another Method in video if you get error:

code (D):

gcloud iam service-accounts create orca-private-cluster-sa --display-name "my service account"

gcloud alpha projects add-iam-policy-binding $DEVSHELL_PROJECT_ID --member serviceAccount:orca-private-cluster-sa@$DEVSHELL_PROJECT_ID.iam.gserviceaccount.com --role "projects/<projectid>/roles/orca_storage_update"

gcloud projects add-iam-policy-binding $DEVSHELL_PROJECT_ID --member serviceAccount:orca-private-cluster-sa@$DEVSHELL_PROJECT_ID.iam.gserviceaccount.com --role “roles/monitoring.viewer”

gcloud projects add-iam-policy-binding $DEVSHELL_PROJECT_ID --member serviceAccount:orca-private-cluster-sa@$DEVSHELL_PROJECT_ID.iam.gserviceaccount.com --role “roles/logging.logWriter” 

gcloud projects add-iam-policy-binding $DEVSHELL_PROJECT_ID --member serviceAccount:orca-private-cluster-sa@$DEVSHELL_PROJECT_ID.iam.gserviceaccount.com --role “roles/monitoring.metricWriter”

Task 4: Create and configure a new Kubernetes Engine private cluster

code (E)

gcloud container clusters create orca-test-cluster --num-nodes 1 --master-ipv4-cidr=172.16.0.64/28 --network orca-build-vpc --subnetwork orca-build-subnet --enable-master-authorized-networks  --master-authorized-networks 192.168.10.2/32 --enable-ip-alias --enable-private-nodes --enable-private-endpoint --service-account orca-private-cluster-sa@<Project ID>.iam.gserviceaccount.com --zone us-east1-b

Task 5: Deploy an application to a private Kubernetes Engine cluster.

code 5(a):

gcloud container clusters get-credentials orca-test-cluster --internal-ip --zone us-east1-b --project <Project ID>

code 5(b):

kubectl create deployment hello-server --image=gcr.io/google-samples/hello-app:1.0

March 06, 2021
2 Comments

Insights from Data with BigQuery

Challenge Lab

Qwiklabs [GSP787]

@prakashfoundation YouTube

click the card to watch the video.

Subscribe Immediately.

Task 1:

Total Confirmed Cases

Build a query that will answer "What was the total count of confirmed cases on Apr 15, 2020?" The query needs to return a single row containing the sum of confirmed cases across all countries. The name of the column should be total_cases_worldwide.

code (A):

SELECT sum(cumulative_confirmed) as total_cases_worldwide FROM `bigquery-public-data.covid19_open_data.covid19_open_data` where date='2020-04-15'

Query 2: Worst Affected Areas

Build a query for answering "How many states in the US had more than 100 deaths on Apr 10, 2020?" The query needs to list the output in the field count_of_states. Hint: Don't include NULL values.

code (B)

with deaths_by_states as (
SELECT subregion1_name as state, sum(cumulative_deceased) as death_count
FROM `bigquery-public-data.covid19_open_data.covid19_open_data` 
where country_name="United States of America" and date='2020-04-10' and subregion1_name is NOT NULL
group by subregion1_name
)

select count(*) as count_of_states
from deaths_by_states
where death_count > 100

Query 3: Identifying Hotspots

Build a query that will answer "List all the states in the United States of America that had more than 1000 confirmed cases on Apr 10, 2020?" The query needs to return the State Name and the corresponding confirmed cases arranged in descending order. Name of the fields to return state and total_confirmed_cases.

code (C):

SELECT * FROM (
SELECT subregion1_name as state, sum(cumulative_confirmed) as total_confirmed_cases
FROM `bigquery-public-data.covid19_open_data.covid19_open_data`
WHERE country_code="US" AND date='2020-04-10' AND subregion1_name is NOT NULL
GROUP BY subregion1_name
ORDER BY total_confirmed_cases DESC ) WHERE total_confirmed_cases > 1000

Query 4: Fatality Ratio

Build a query that will answer "What was the case-fatality ratio in Italy for the month of April 2020?" Case-fatality ratio here is defined as (total deaths / total confirmed cases) * 100. Write a query to return the ratio for the month of April 2020 and containing the following fields in the output: total_confirmed_cases, total_deaths, case_fatality_ratio.

code (D):

SELECT sum(cumulative_confirmed) as total_confirmed_cases, sum(cumulative_deceased) as total_deaths, (sum(cumulative_deceased)/sum(cumulative_confirmed))*100 as case_fatality_ratio 
FROM `bigquery-public-data.covid19_open_data.covid19_open_data` 
where country_name="Italy" AND date BETWEEN '2020-04-01'and '2020-04-30'

Query 5: Identifying specific day

Build a query that will answer: "On what day did the total number of deaths cross 10000 in Italy?" The query should return the date in the format yyyy-mm-dd.

code (E)

SELECT date
FROM `bigquery-public-data.covid19_open_data.covid19_open_data` 
where country_name="Italy" and cumulative_deceased>10000
order by date asc
limit 1

Query 6: Finding days with zero net new cases

The following query is written to identify the number of days in India between 21 Feb 2020 and 15 March 2020 when there were zero increases in the number of confirmed cases. However it is not executing properly.

code (F):

WITH india_cases_by_date AS (
  SELECT
    date,
    SUM( cumulative_confirmed ) AS cases
  FROM
    `bigquery-public-data.covid19_open_data.covid19_open_data`
  WHERE
    country_name ="India"
    AND date between '2020-02-21' and '2020-03-15'
  GROUP BY
    date
  ORDER BY
    date ASC 
 )
 
, india_previous_day_comparison AS 
(SELECT
  date,
  cases,
  LAG(cases) OVER(ORDER BY date) AS previous_day,
  cases - LAG(cases) OVER(ORDER BY date) AS net_new_cases
FROM india_cases_by_date
)

select count(*)
from india_previous_day_comparison
where net_new_cases=0

Query 7: Doubling rate

Using the previous query as a template, write a query to find out the dates on which the confirmed cases increased by more than 10% compared to the previous day (indicating doubling rate of ~ 7 days) in the US between the dates March 22, 2020 and April 20, 2020. The query needs to return the list of dates, the confirmed cases on that day, the confirmed cases the previous day, and the percentage increase in cases between the days. Use the following names for the returned fields: Date, Confirmed_Cases_On_Day, Confirmed_Cases_Previous_Day and Percentage_Increase_In_Cases.

code (G):

WITH us_cases_by_date AS (
  SELECT
    date,
    SUM(cumulative_confirmed) AS cases
  FROM
    `bigquery-public-data.covid19_open_data.covid19_open_data`
  WHERE
    country_name="United States of America"
    AND date between '2020-03-22' and '2020-04-20'
  GROUP BY
    date
  ORDER BY
    date ASC 
 )
 
, us_previous_day_comparison AS 
(SELECT
  date,
  cases,
  LAG(cases) OVER(ORDER BY date) AS previous_day,
  cases - LAG(cases) OVER(ORDER BY date) AS net_new_cases,
  (cases - LAG(cases) OVER(ORDER BY date))*100/LAG(cases) OVER(ORDER BY date) AS percentage_increase
FROM us_cases_by_date
)

select Date, cases as Confirmed_Cases_On_Day, previous_day as Confirmed_Cases_Previous_Day, percentage_increase as Percentage_Increase_In_Cases
from us_previous_day_comparison
where percentage_increase > 10

Query 8: Recovery rate

Build a query to list the recovery rates of countries arranged in descending order (limit to 10) upto the date May 10, 2020. Restrict the query to only those countries having more than 50K confirmed cases. The query needs to return the following fields: country, recovered_cases, confirmed_cases, recovery_rate.

code (H):

WITH cases_by_country AS (
  SELECT
    country_name AS country,
    sum(cumulative_confirmed) AS cases,
    sum(cumulative_recovered) AS recovered_cases
  FROM
    bigquery-public-data.covid19_open_data.covid19_open_data
  WHERE
    date = '2020-05-10'
  GROUP BY
    country_name
 )
 
, recovered_rate AS 
(SELECT
  country, cases, recovered_cases,
  (recovered_cases * 100)/cases AS recovery_rate
FROM cases_by_country
)

SELECT country, cases AS confirmed_cases, recovered_cases, recovery_rate
FROM recovered_rate
WHERE cases > 50000
ORDER BY recovery_rate desc
LIMIT 10

Query 9: CDGR - Cumulative Daily Growth Rate

The following query is trying to calculate the CDGR on May 10, 2020(Cumulative Daily Growth Rate) for France since the day the first case was reported. The first case was reported on Jan 24, 2020.

code (I):

WITH
  france_cases AS (
  SELECT
    date,
    SUM(cumulative_confirmed) AS total_cases
  FROM
    `bigquery-public-data.covid19_open_data.covid19_open_data`
  WHERE
    country_name="France"
    AND date IN ('2020-01-24',
      '2020-05-10')
  GROUP BY
    date
  ORDER BY
    date)
, summary as (
SELECT
  total_cases AS first_day_cases,
  LEAD(total_cases) OVER(ORDER BY date) AS last_day_cases,
  DATE_DIFF(LEAD(date) OVER(ORDER BY date),date, day) AS days_diff
FROM
  france_cases
LIMIT 1
)
select first_day_cases, last_day_cases, days_diff, POW((last_day_cases/first_day_cases),(1/days_diff))-1 as cdgr
from summary

Create a Datastudio report

Create a Datastudio report that plots the following for the United States

code (J):

SELECT
  date, SUM(cumulative_confirmed) AS country_cases,
  SUM(cumulative_deceased) AS country_deaths
FROM
  `bigquery-public-data.covid19_open_data.covid19_open_data`
WHERE
  date BETWEEN '2020-03-15'
  AND '2020-04-30'
  AND country_name ="United States of America"
GROUP BY date

March 06, 2021
0 Comments

Explore Machine Learning Models with Explainable AI

step-by-step instructions, you will use the skills learned from the labs in the quest to figure out how to complete the tasks on your own! An automated scoring system (shown on this page) will provide feedback on whether you have completed your tasks correctly.

Challenge Lab

Qwiklabs [GSP324]

@prakashfoundation YouTube

click the card to watch the video.

Subscribe Immediately.

Task 1:

TODO (1):

# ---- TODO ---------

model = Sequential()

model.add(layers.Dense(200, input_shape=(input_size,), activation='relu'))

model.add(layers.Dense(50, activation='relu'))

model.add(layers.Dense(20, activation='relu'))

model.add(layers.Dense(1, activation='sigmoid'))

model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])

model.fit(train_data, train_labels, epochs=10, batch_size=2048, validation_split=0.1)

TODO (2):

# ---- TODO ---------

# create the limited_model = Sequential()

# limited_model.add (your layers)

# limited_model.compile

# limited_model.fit



limited_model = Sequential()

limited_model.add(layers.Dense(200, input_shape=(input_size,), activation='relu'))

limited_model.add(layers.Dense(50, activation='relu'))

limited_model.add(layers.Dense(20, activation='relu'))

limited_model.add(layers.Dense(1, activation='sigmoid'))

limited_model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])

limited_model.fit(limited_train_data, limited_train_labels, epochs=10, batch_size=2048, validation_split=0.1)

Fill out this information:

GCP_PROJECT = '# TODO'

MODEL_BUCKET = 'gs:// #TODO'

MODEL_NAME = 'complete_model' #do not modify

LIM_MODEL_NAME = 'limited_model' #do not modify

VERSION_NAME = 'v1'

REGION = 'us-central1'

TODO:

# ---- TODO ---------

!gcloud ai-platform models create $MODEL_NAME --regions $REGION

Now create a version. This will take a couple of minutes to deploy..

TODO:

# ---- TODO ------

!gcloud ai-platform versions create $VERSION_NAME \

--model=$MODEL_NAME \

--framework='TENSORFLOW' \

--runtime-version=2.1 \

--origin=$MODEL_BUCKET/saved_model/my_model \

--staging-bucket=$MODEL_BUCKET \

--python-version=3.7

Create your second AI Platform model: limited_model

TODO:

# ---- TODO ---------

!gcloud ai-platform models create $LIM_MODEL_NAME --regions $REGION

TODO :

# ---- TODO ------

!gcloud ai-platform versions create $VERSION_NAME \

--model=$LIM_MODEL_NAME \

--framework='TENSORFLOW' \

--runtime-version=2.1 \

--origin=$MODEL_BUCKET/saved_limited_model/my_limited_model \

--staging-bucket=$MODEL_BUCKET \

--python-version=3.7

March 06, 2021
2 Comments

Deploy and Manage Cloud Environments with Google Cloud

Deploy and Manage Cloud Environments with Google Cloud: Challenge Lab

Task 1: Create Production Environment


cd /work/dm


sed -i s/SET_REGION/us-east1/g prod-network.yaml

  gcloud deployment-manager deployments create prod-network
  --config=prod-network.yaml



gcloud config set compute/zone us-east1-b


gcloud container clusters create kraken-prod \
          --num-nodes 2 \
          --network kraken-prod-vpc \
          --subnetwork kraken-prod-subnet\
          --zone us-east1-b


gcloud container clusters get-credentials kraken-prod


cd /work/k8s


for F in $(ls *.yaml); do kubectl create -f $F; done

Task 2: Configure the admin host






Create kraken-admin




gcloud config set compute/zone us-east1-b



  gcloud compute instances create kraken-admin
  --network-interface="subnet=kraken-mgmt-subnet"
  --network-interface="subnet=kraken-prod-subnet"





Create alert:
Open monitoring


Create an alert



  Configure the policy to email your email when jumphost is cpu utilization is
  above 50% for 1 min.

Task 3: Verify the Spinnaker deployment




Use cloudshell and run


gcloud config set compute/zone us-east1-b


gcloud container clusters get-credentials spinnaker-tutorial



  DECK_POD=$(kubectl get pods --namespace default -l "cluster=spin-deck" -o
  jsonpath="{.items[0].metadata.name}")




  kubectl port-forward --namespace default $DECK_POD 8080:9000 >>
  /dev/null &











#Go to cloudshell webpreview and go to applications->sample

#Open pipelines and manually run the pipeline if it has not already running. Approve the deployment to production. Check the production frontend endpoint (use http, not the default https)

#Back in cloudshell run these commands to push a change












gcloud config set compute/zone us-east1-b


gcloud source repos clone sample-app


cd sample-app


touch a


git config --global user.email "$(gcloud config get-value account)"


git config --global user.name "Student"


git commit -a -m "change"


git tag v1.0.1


git push --tags

March 06, 2021
0 Comments

Build and Deploy a Docker Image to a Kubernetes Cluster

GSP304 | Build and Deploy a Docker Image to a Kubernetes Cluster f.txt


  
  
gsutil cp
gs://sureskills-ql/challenge-labs/ch04-kubernetes-app-deployment/echo-web.tar.gz
.



gsutil cp gs://$DEVSHELL_PROJECT_ID/echo-web.tar.gz .
tar -xvf echo-web.tar.gz
gcloud builds submit --tag gcr.io/$DEVSHELL_PROJECT_ID/echo-app:v1 .



  gcloud container clusters create echo-cluster --num-nodes 2 --zone
  us-central1-a --machine-type n1-standard-2






  kubectl create deployment echo-web
  --image=gcr.io/qwiklabs-resources/echo-app:v1




  kubectl expose deployment echo-web --type=LoadBalancer --port=80
  --target-port=8000



kubectl get svc

March 06, 2021
0 Comments

Integrate with Machine Learning APIs

Integrate with Machine Learning APIs: Challenge Lab |
GSP329


  
  
 Task " 1 & 2 " 

export SANAME=challenge
gcloud iam service-accounts create $SANAME

  gcloud projects add-iam-policy-binding $DEVSHELL_PROJECT_ID
  --member=serviceAccount:$SANAME@$DEVSHELL_PROJECT_ID.iam.gserviceaccount.com
  --role=roles/bigquery.admin


  gcloud projects add-iam-policy-binding $DEVSHELL_PROJECT_ID
  --member=serviceAccount:$SANAME@$DEVSHELL_PROJECT_ID.iam.gserviceaccount.com
  --role=roles/storage.admin


  gcloud iam service-accounts keys create sa-key.json --iam-account
  $SANAME@$DEVSHELL_PROJECT_ID.iam.gserviceaccount.com

export GOOGLE_APPLICATION_CREDENTIALS=${PWD}/sa-key.json
gsutil cp gs://$DEVSHELL_PROJECT_ID/analyze-images.py .






############### Task 3 ###########################################




# DONT CHANGE ANYTHING


# Dataset: image_classification_dataset
# Table name: image_text_detail
import os
import sys


# Import Google Cloud Library modules

  from google.cloud import storage, bigquery, language, vision, translate_v2



if ('GOOGLE_APPLICATION_CREDENTIALS' in os.environ):

      if (not
  os.path.exists(os.environ['GOOGLE_APPLICATION_CREDENTIALS'])):


          print ("The GOOGLE_APPLICATION_CREDENTIALS file
  does not exist.\n")

        exit()
else:

      print ("The GOOGLE_APPLICATION_CREDENTIALS environment variable
  is not defined.\n")

    exit()


if len(sys.argv)<3:

      print('You must provide parameters for the Google Cloud project
  ID and Storage bucket')


      print ('python3 '+sys.argv[0]+ '[PROJECT_NAME] [BUCKET_NAME]')

    exit()


project_name = sys.argv[1]
bucket_name = sys.argv[2]


# Set up our GCS, BigQuery, and Natural Language clients
storage_client = storage.Client()
bq_client = bigquery.Client(project=project_name)
nl_client = language.LanguageServiceClient()


# Set up client objects for the vision and translate_v2 API Libraries
vision_client = vision.ImageAnnotatorClient()
translate_client = translate_v2.Client()


# Setup the BigQuery dataset and table objects
dataset_ref = bq_client.dataset('image_classification_dataset')
dataset = bigquery.Dataset(dataset_ref)
table_ref = dataset.table('image_text_detail')
table = bq_client.get_table(table_ref)



  # Create an array to store results data to be inserted into the BigQuery table

rows_for_bq = []


# Get a list of the files in the Cloud Storage Bucket
files = storage_client.bucket(bucket_name).list_blobs()
bucket = storage_client.bucket(bucket_name)


print('Processing image files from GCS. This will take a few minutes..')



  # Process files from Cloud Storage and save the result to send to BigQuery

for file in files:    

      if file.name.endswith('jpg') or  file.name.endswith('png'):

        file_content = file.download_as_string()
        

          # TBD: Create a Vision API image object called
  image_object 


          # Ref:
  https://googleapis.dev/python/vision/latest/gapic/v1/types.html#google.cloud.vision_v1.types.Image

        from google.cloud import vision_v1
        import io
        client = vision.ImageAnnotatorClient()





          # TBD: Detect text in the image and save the
  response data into an object called response


          # Ref:
  https://googleapis.dev/python/vision/latest/gapic/v1/api.html#google.cloud.vision_v1.ImageAnnotatorClient.document_text_detection


          image =
  vision_v1.types.Image(content=file_content)

        response = client.text_detection(image=image)
    

          # Save the text content found by the vision API
  into a variable called text_data


          text_data =
  response.text_annotations[0].description




          # Save the text detection response data in
  <filename>.txt to cloud storage

        file_name = file.name.split('.')[0] + '.txt'
        blob = bucket.blob(file_name)

          # Upload the contents of the text_data string
  variable to the Cloud Storage file 


          blob.upload_from_string(text_data,
  content_type='text/plain')




          # Extract the description and locale data from the
  response file

        # into variables called desc and locale

          # using response object properties e.g.
  response.text_annotations[0].description


          desc = response.text_annotations[0].description

        locale = response.text_annotations[0].locale
        

          # if the locale is English (en) save the
  description as the translated_txt

        if locale == 'en':
            translated_text = desc
        else:

              # TBD: For non EN locales pass the
  description data to the translation API


              # ref:
  https://googleapis.dev/python/translation/latest/client.html#google.cloud.translate_v2.client.Client.translate


              # Set the target_language locale to
  'en')


              from google.cloud import
  translate_v2 as translate

            
            client = translate.Client()

              translation =
  translate_client.translate(text_data, target_language='en')


              translated_text =
  translation['translatedText']

        print(translated_text)
        

          # if there is response data save the original text
  read from the image, 

        # the locale, translated text, and filename
        if len(response.text_annotations) > 0:

              rows_for_bq.append((desc, locale,
  translated_text, file.name))



print('Writing Vision API image data to BigQuery...')
# Write original text, locale and translated text to BQ

  # TBD: When the script is working uncomment the next line to upload results to
  BigQuery

errors = bq_client.insert_rows(table, rows_for_bq)


assert errors == []









  #############################################################################################################################3




  -------------->>>>>>>>>>>run this in cloud
  shell:



python3 analyze-images.py $DEVSHELL_PROJECT_ID $DEVSHELL_PROJECT_ID





  ################################################################################################################################3






  ------------------>>>>>>>>>>>>>>>>>>>>Go
  to BigQuery, run:


  SELECT locale,COUNT(locale) as lcount FROM
  image_classification_dataset.image_text_detail GROUP BY locale ORDER BY lcount
  DESC

March 06, 2021
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Build a Website on Google Cloud

Build-a-Website-on-Google-Cloud-Challenge-Lab

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Task 1: Download the monolith code and build your container


git clone https://github.com/googlecodelabs/monolith-to-microservices.git




cd ~/monolith-to-microservices
./setup.sh


cd ~/monolith-to-microservices/monolith
npm start


gcloud services enable cloudbuild.googleapis.com

  gcloud builds submit --tag gcr.io/${GOOGLE_CLOUD_PROJECT}/fancytest:1.0.0 .

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Task 2: Create a kubernetes cluster and deploy the application


gcloud config set compute/zone us-central1-a
gcloud services enable container.googleapis.com
gcloud container clusters create fancy-cluster --num-nodes 3



  kubectl create deployment fancytest
  --image=gcr.io/${GOOGLE_CLOUD_PROJECT}/fancytest:1.0.0


  kubectl expose deployment fancytest --type=LoadBalancer --port 80
  --target-port 8080

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Task 3: Create a containerized version of your Microservices


cd ~/monolith-to-microservices/microservices/src/orders
gcloud builds submit --tag gcr.io/${GOOGLE_CLOUD_PROJECT}/orders:1.0.0 .


cd ~/monolith-to-microservices/microservices/src/products

  gcloud builds submit --tag gcr.io/${GOOGLE_CLOUD_PROJECT}/products:1.0.0 .

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Task 4: Deploy the new microservices



  kubectl create deployment orders
  --image=gcr.io/${GOOGLE_CLOUD_PROJECT}/orders:1.0.0


  kubectl expose deployment orders --type=LoadBalancer --port 80 --target-port
  8081




  kubectl create deployment products
  --image=gcr.io/${GOOGLE_CLOUD_PROJECT}/products:1.0.0


  kubectl expose deployment products --type=LoadBalancer --port 80 --target-port
  8082

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Task 5: Configure the Frontend microservice


cd ~/monolith-to-microservices/react-app

nano .env

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Task 6: Create a containerized version of the Frontend microservice


cd ~/monolith-to-microservices/microservices/src/frontend

  gcloud builds submit --tag gcr.io/${GOOGLE_CLOUD_PROJECT}/frontend:1.0.0 .

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Task 7: Deploy the Frontend microservice



  kubectl create deployment frontend
  --image=gcr.io/${GOOGLE_CLOUD_PROJECT}/frontend:1.0.0




  kubectl expose deployment frontend --type=LoadBalancer --port 80 --target-port
  8080

Challenge Lab

Qwiklabs [GSP340]

@prakashfoundation YouTube

@codeGarry Github

GSP340

Task 1:

Task 2: Add new columns to your table

Task 3: Add country population data to the population column

Task 4: Add country area data to the country_area column

Task 5: Populate the mobility record data

Task 6: Query missing data in population & country_area columns

Challenge Lab

Qwiklabs [GSP342]

@prakashfoundation YouTube

@codeGarry Github

GSP342

Task 1:

Replace the below content in the file:

Ctrl+x --> Y--> Enter

commands for task2 & task3:

Another Method in video if you get error:

Task 4: Create and configure a new Kubernetes Engine private cluster

Task 5: Deploy an application to a private Kubernetes Engine cluster.

Challenge Lab

Qwiklabs [GSP787]

@prakashfoundation YouTube

Task 1:

Query 2: Worst Affected Areas

Query 3: Identifying Hotspots

Query 4: Fatality Ratio

Query 5: Identifying specific day

Query 6: Finding days with zero net new cases

Query 7: Doubling rate

Query 8: Recovery rate

Query 9: CDGR - Cumulative Daily Growth Rate

Create a Datastudio report

Challenge Lab

Qwiklabs [GSP324]

@prakashfoundation YouTube

Task 1:

Deploy and Manage Cloud Environments with Google Cloud: Challenge Lab

Task 1: Create Production Environment

Task 2: Configure the admin host

Configure the policy to email your email when jumphost is cpu utilization is above 50% for 1 min.

Task 3: Verify the Spinnaker deployment

Use cloudshell and run

#Go to cloudshell webpreview and go to applications->sample

#Open pipelines and manually run the pipeline if it has not already running. Approve the deployment to production. Check the production frontend endpoint (use http, not the default https) #Back in cloudshell run these commands to push a change

GSP304 | Build and Deploy a Docker Image to a Kubernetes Cluster f.txt

Integrate with Machine Learning APIs: Challenge Lab | GSP329

Pages

Courses

Most Famous

Map

#Open pipelines and manually run the pipeline if it has not already running. Approve the deployment to production. Check the production frontend endpoint (use http, not the default https)

#Back in cloudshell run these commands to push a change

Integrate with Machine Learning APIs: Challenge Lab |
GSP329