Google Cloud Dataflow is a unified streaming and batch data processing service that allows users to set up pipelines to process data in various patterns. It features both horizontal and vertical autoscaling, real-time monitoring, and integration with other frameworks. Getting started involves choosing a programming model like Java, Python, or SQL and running sample pipelines locally or on the Cloud Dataflow service. Use cases include stream analytics, real-time AI, log and sensor data processing. The documentation provides more in-depth information on features and usage.

1. GOOGLE CLOUD DATAFLOW

2. Agenda
• What is Google Cloud Dataflow?
• Features of Google Cloud Dataflow
• Getting started with Dataflow
• Use cases of Google Cloud Dataflow
• Learn more!

3. What is Google Cloud Dataflow?
• Google Cloud Dataflow executes a wide range of data processing patterns.
• You can set up pipelines and monitor their execution aspects using these
patterns.
• Google Cloud Dataflow service is well-known for unified stream and batch
data processing that includes serverless, fast, and cost-effective features.
• Google Cloud Dataflow imbricates with other software frameworks and
services. Some of them include Amazon Kinesis, Apache Spark, Apache
Storm, Facebook Flux, and others.

4. Features of Google Cloud Dataflow
Vertical autoscaling - new in
Dataflow Prime
Right fitting - new in Dataflow
Prime
Smart diagnostics - new in
Dataflow Prime
Streaming Engine
Horizontal autoscaling
Dataflow Shuffle
Dataflow SQL
Flexible Resource Scheduling
(FlexRS)
Dataflow templates
Notebooks integration
Real-time change data capture
Inline monitoring
Customer-managed encryption
keys
Dataflow VPC Service Controls
Private IPs

5. Create an account
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QuickStart using Java and Apache Maven
Get the pipeline code
Run the pipeline locally
Run the pipeline on the Dataflow service
View your results

6. Create an account
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QuickStart using Python
Set up your environment
Get the Apache Beam SDK
Run the pipeline locally
Run the pipeline on the Dataflow service

7. Create an account
QuickStart using SQL
Run the pipeline
View the results

8. Create an account
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QuickStart using Templates
Create a BigQuery dataset and table
Run the pipeline
View your results

9. Use cases of Google Dataflow
• Stream Analytics - Stream Analytics is built over the Cloud Dataflow, along with BigQuery to put
up a streaming solution. This allows the resources to undergo the processing, and analyzing sessions
of data for the insights in real-time.
• Real-Time Artificial Intelligence - You can enable the form of predictive analytics, real-time
personalization, and fraud detection.
• Log and Sensor Data Processing - You can unlock the business insights from a global device
network with the intelligent IoT platform.

10. Learn More!
It's important to go through the official Google Cloud Dataflow documentation and
articles that explain all the aspects in detail. Learn more about by visiting these links:
• What is Google Cloud Dataflow? – Whizlabs
• Google Cloud Dataflow Documentation
• Detailed information on QuickStart with Dataflow

11. THANK YOU