This course teaches participants the following skills: Design and build data processing systems on Google Cloud Platform Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow Derive business insights from extremely large datasets using Google BigQuery Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML Leverage unstructured data using Spark and ML APIs on Cloud Dataproc Enable instant insights from streaming data

1. Google Cloud Platform
Data Engineer
Module 4
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2. Module 4
Cloud Endpoints
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3. Module Overview
Cloud Endpoints
Developing Endpoints
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4. Cloud Endpoints
API Gateway
Cloud Endpoints are used to
create a web backend.
Used for web and mobile
clients
Deploy with App Engine
Tools and Library

5. Cloud Endpoints
Oath 2.0 & Firebase
NGINX-based proxy &
distributed architecture
Access your App Engine
Instances
- Datastore
- Cloud Storage
- Task Queues
Reduces your development
cycle
Java and Python

6. Developing Endpoints
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7. Cloud Endpoints
Endpoints use Google
Protocol RPC for HTTP
service calls
Steps include at high
level
1. Configure your app
2. Define Message
classes
3. Write endpoint code
4. Run and test API

8. App.yaml
Create a root application folder and add a file named
app.yaml

9. API
gRPC
Similar to RESTful

10. Endpoint Review
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11. Cloud Endpoints
Endpoints can be
implemented in Java and
Python
Cloud Endpoints
generate data for the
client in JSON only

12. Java Cloud Endpoints
https://cloud.google.com/appengine/docs/java/endpoints/
Python Cloud Endpoints
https://cloud.google.com/appengine/docs/python/endpoints/
JavaScript clients
https://cloud.google.com/appengine/docs/python/endpoints/co
nsume_js
Cloud Endpoint Resources
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13. Google Cloud Platform https://cloud.google.com/
Console
https://console.cloud.google.com/
Documentation https://cloud.google.com/docs/
Pricing
https://cloud.google.com/pricing/
Free Tier https://cloud.google.com/free/
Resources
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