Course
Digicomp Code SDPDF
Serverless Data Processing with Dataflow («SDPDF»)
This training is intended for big data practitioners who want to further their understanding of Dataflow in order to advance their data processing applications.
Duration
3 days
Price
2'500.–
Course documents
Official Google Cloud courseware
Course facts
- Demonstrating how Apache Beam and Dataflow work together to fulfill your organization’s data processing needs
- Summarizing the benefits of the Beam Portability Framework and enabling it for your Dataflow pipelines
- Enabling Shuffle and Streaming Engine, for batch and streaming pipelines respectively, for maximum performance
- Enabling Flexible Resource Scheduling for more cost-efficient performance
- Selecting the right combination of IAM permissions for your Dataflow job
- Implementing best practices for a secure data processing environment
- Selecting and tuning the I/O of your choice for your Dataflow pipeline
- Using schemas to simplify your Beam code and improving the performance of your pipeline
- Developing a Beam pipeline using SQL and DataFrames
- Performing monitoring, troubleshooting, testing and CI/CD on Dataflow pipelines
Beginning with foundations, this training explains how Apache Beam and Dataflow work together to meet your data processing needs without the risk of vendor lock-in. The section on developing pipelines covers how you convert your business logic into data processing applications that can run on Dataflow.
This training culminates with a focus on operations, which reviews the most important lessons for operating a data application on Dataflow, including monitoring, troubleshooting, testing, and reliability.
1 Introduction
- Beam and Dataflow Refresher
- Demonstrate how Apache Beam and Dataflow work together to fulfill your organization’s data processing needs
2 Beam Portability
- Runner v2
- Container Environments
- Cross-Language TransformS
- Summarize the benefits of the Beam Portability Framework
- Customize the data processing environment of your pipeline using custom containers
- Review use cases for cross-language transformations
- Enable the Portability framework for your Dataflow pipelines
3 Separating Compute and Storage with Dataflow
- Dataflow Shuffle Service
- Dataflow Streaming Engine
- Flexible Resource Scheduling
- Enable Shuffle and Streaming Engine, for batch and streaming pipelines respectively, for maximum performance
- Enable Flexible Resource Scheduling for more cost-efficient performance
4 IAM, Quotas, and Permissions
- IAM
- Quota
- Select the right combination of IAM permissions for your Dataflow job.
- Determine your capacity needs by inspecting the relevant quotas for your Dataflow jobs
5 Security
- Data Locality
- Shared VPC
- Private IPs
- CMEK
- Select your zonal data processing strategy using Dataflow, depending on your data locality needs
- Implement best practices for a secure data processing environment
6 Beam Concepts Review
- Beam Basics
- Utility Transforms
- DoFn Lifecycle
- Review main Apache Beam concepts (Pipeline, PCollections, PTransforms, Runner, reading/writing, Utility PTransforms, side inputs), bundles and DoFn Lifecycle
7 Windows, Watermarks, Triggers
- Windows, Watermarks, Triggers
- Implement logic to handle your late data
- Review different types of triggers
- Review core streaming concepts (unbounded PCollections, windows)
8 Sources and Sinks
- Text IO and File IO
- BigQuery IO
- PubSub IO
- Kafka IO
- Bigable IO
- Avro IO
- Splittable DoFn
- Write the I/O of your choice for your Dataflow pipeline
- Tune your source/sink transformation for maximum performance
- Create custom sources and sinks using SDF
9 Schemas
- Beam Schemas
- Code Examples
- Introduce schemas, which give developers a way to express structured data in their Beam pipelines
- Use schemas to simplify your Beam code and improve the performance of your pipeline
10 State and Timers
- State API
- Timer API
- Summary
- Identify use cases for state and timer API implementations
- Select the right type of state and timers for your pipeline
11 Best Practices
- Schemas
- Handling unprocessable Data
- Error Handling
- AutoValue Code Generator
- JSON Data Handling
- Utilize DoFn Lifecycle
- Pipeline Optimizations
- Implement best practices for Dataflow pipelines.
12 Dataflow SQL and DataFrames
- Dataflow and Beam SQL
- Windowing in SQL
- Beam DataFrames
- Develop a Beam pipeline using SQL and DataFrames
13 Beam Notebooks
- Prototype your pipeline in Python using Beam notebook
- Launch a job to Dataflow from a notebook
14 Monitoring
- Job List
- Job Info
- Job Graph
- Job Metrics
- Metrics Explorer
- Navigate the Dataflow Job Details UI
- Interpret Job Metrics charts to diagnose pipeline regressions
- Set alerts on Dataflow jobs using Cloud Monitoring
15 Logging and Error Reporting
- Logging & Error Reporting
- Use the Dataflow logs and diagnostics widgets to troubleshoot pipeline issues
16 Troubleshooting and Debug
- Troubleshooting Workflow
- Types of Troubles
- Use a structured approach to debug your Dataflow pipelines
- Examine common causes for pipeline failures
17 Performance
- Pipeline Design
- Data Shape
- Source, Sinks, and External Systems
- Shuffle and Streaming Engine
- Understand performance considerations for pipelines
- Consider how the shape of your data can affect pipeline performance
18 Testing and CI/CD
- Unit Testing
- Integration Testing
- Artifact Building
- Deployment
- Testing approaches for your Dataflow pipeline
- Review frameworks and features available to streamline your CI/CD workflow for Dataflow pipelines
19 Reliability
- Monitoring
- Geolocation
- Disaster Recovery
- High Availability
- Implement reliability best practices for your Dataflow pipelines
20 Flex Templates
- Classic Templates
- Flex Templates
- Using Flex Templates
- Google-provided Templates
- Using flex templates to standardize and reuse Dataflow pipeline code
- Data Engineers
- Data Analysts and Data Scientists aspiring to develop Data Engineering skills
- Completed Building Batch Data Pipelines
- Completed Building Resilient Streaming Analytics Systems
Products
- Dataflow
- Cloud Operations