Course Summary
1. Course Summary
Mehran: Congratulations, you've made it to the end of the operations course. In the final installment of the data flow series, you're ready to build a modern data platform now. Before we do that, let's summarize the main concepts we covered in each of the modules in the operations course. We started this course with the walkthrough of the data flow monitoring experience. We learn how to use the jobs list page to filter for jobs that we want to monitor or investigate. We looked at how the job graph job info and job metrics tabs collectively provide a comprehensive summary of your Dataflow job. Lastly, we learn how we can use Dataflow integration with metrics Explorer for creating alerting policies for data flow metrics. We explored two important integrations in the data flow, operational toolkit, logging and error reporting. The logging panel helps you sift through job and work logs provides a diagnostics tab that surfaces errors, you can click through to the error reporting interface, investigate the frequency of these errors, and examine the full stack traces of these errors. We use the monitoring logging and error reporting capabilities and incorporated them into our recommended troubleshooting workflow, which leverages data flows integrated Error Reporting in jobs metrics tab. We then reviewed four common modes of failure for data flow, failure to build the pipeline, failure to start the pipeline and data flow, failure during pipeline execution, and performance issues. Performance is a key consideration for any data engineer operating a data processing system. In this module, we review how pipeline design can impact your performance that topology, coders, windows and logging that you implement can have adverse impacts on your pipeline performance if not taken into careful consideration the shape of your data, specifically if your keyspaces skewed can cause worker imbalances and cause under utilization for your pipeline. Your Dataflow pipeline will interact with sources, sinks and external systems. A well-tuned pipeline will take the limitations and constraints of these pieces into account. Lastly, shuffle and streaming engine can help offload data storage from worker attached disks onto a highly scalable back end that will deliver performance benefits to your pipeline. As your data requirements evolve, so do your data flow pipelines. A robust Dataflow architecture implements testing at various abstraction layers, starting with the do functions at the lowest level, then P transforms, then pipelines. And finally, for entire end to end systems. Dataflow's continuous integration continuous deployment model requires using the direct runner to validate your pipeline in a local environment, followed by testing it on a production runner before it is pushed to production. Beam provides helpful functions like p assert, test, pipeline and test stream to help implement this testing architecture. Dataflow offers features such as update, drain snapshots and cancel so that you can adjust the deployment of your streaming pipelines as needed. Next, we discussed how to implement reliability best practices for your Dataflow pipelines. Monitoring dashboards and alerts can help notify you when your system is encountering a bottleneck. And using dead letter queues and error logging can prevent pipelines from going down when corrupted data enters the pipeline. Protecting your pipelines from zonal and regional outages require thoughtfulness about how you specify the location of your sources, sinks, and Dataflow job, but data loss can be mitigated with pub sub and data flow snapshots. High Availability can be implemented by running redundant pipelines in different zones or regions. Our last module is covers flex templates, which makes it easy to share and standardized data flow pipelines for your organization. Templates allow you to call data flow pipelines by making an API call without the fuss of installing runtime dependencies in your development environment. Google offers a variety of templates directly in Cloud Console, which allows you to launch Dataflow job without writing a single line of code. Flex templates offers advantages over classic Dataflow templates, and are encouraged for all templating needs. To conclude, data flow offers a whole suite of features that makes it easy to manage your data processing system. This operational toolkit will help you focus your efforts on insights, not infrastructure, and ensure that you can spend your time creating value for your customers not keeping the lights on. We're excited to see what your organization builds on data flow.2. Let's practice!
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