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Create and manage scheduled queries (e.g., BigQuery, Cloud Scheduler, Cloud Composer)

Scheduled queries let you run SQL statements automatically on a regular basis. In Google Cloud, you can use BigQuery’s built-in scheduler, Cloud Scheduler, or Cloud Composer to set up these tasks. Scheduled queries help you keep data fresh without manual intervention.

When you create a scheduled query, you choose the query text, the output table, and the frequency of execution. You can set schedules from minutes to days, depending on your needs. Automating queries reduces the chance of human error and ensures timely data updates.

To manage scheduled queries, you can:

• Monitor status and last run time in the BigQuery console
• Use Cloud Scheduler to trigger REST calls or Pub/Sub messages
• Employ Cloud Composer for more complex workflows
  These tools give you flexibility and visibility into your automated data tasks.

Monitor Dataflow pipeline progress using the Dataflow job UI

A Dataflow pipeline processes streaming or batch data using Apache Beam. The Dataflow job UI provides a graphical view of your pipeline’s state. Pipeline monitoring helps you spot delays or failures early.

In the job UI, you can view:

• Job status (running, succeeded, failed)
• Throughput metrics like elements per second
• Resource usage such as CPU and memory
  These insights let you check performance and health at a glance.

Using the job UI, you can drill down into individual steps of your pipeline. You can see where processing slows or errors occur and then take action to optimize or fix issues. Proactive monitoring helps keep your data workflows reliable.

Review and analyze logs in Cloud Logging and Cloud Monitoring

Cloud Logging captures logs from Google Cloud services and applications. Cloud Monitoring turns those logs into metrics and dashboards. Together, they form a central location for observing your systems.

With Cloud Logging, you can search and filter log entries to find errors or warnings. You can use the Logs Explorer to build queries and export results for further analysis. These logs give you a detailed record of what happened and when.

In Cloud Monitoring, you create metrics and set up alerts based on log data or system signals. Dashboards display charts of key metrics like latency or error rates. Setting alerts ensures you are notified when something goes wrong and can respond quickly.

Select a data orchestration solution (e.g., Cloud Composer, scheduled queries, Dataproc Workflow Templates, Workflows) based on business requirements

Data orchestration means managing and automating the sequence of data tasks. Google Cloud offers several options depending on your needs:

• Cloud Composer for Apache Airflow–based workflows
• Scheduled queries for simple, time-based BigQuery jobs
• Dataproc Workflow Templates for Hadoop and Spark jobs
• Workflows for serverless, step-by-step orchestration

Choosing the right tool depends on factors like task complexity, cost, and team skills. Simple tasks may only need scheduled queries, while complex pipelines with dependencies might use Cloud Composer.

Consider these questions:

• Do you need reusable workflows?
• Are dependencies complex or simple?
• Is real-time data processing required?
  Matching these needs to the right service ensures efficient, maintainable pipelines.

Identify use cases for event-driven data ingestion from Pub/Sub to BigQuery

Event-driven ingestion means data moves into your warehouse as soon as it’s produced. Pub/Sub is Google Cloud’s messaging service for events. BigQuery is the destination for large-scale analytics. Together, they enable real-time insights.

Common use cases include:

• Streaming application logs for monitoring
• IoT sensor data for instantaneous alerts
• User interaction events for personalized experiences

These scenarios benefit from the low latency of Pub/Sub and the scalability of BigQuery. By connecting them, you can run queries on fresh data without waiting for batch loads.

With event-driven ingestion, you reduce data lag and improve decision speed. You can set up simple pipelines using Cloud Functions or Dataflow to move messages from Pub/Sub into BigQuery. Real-time analytics becomes practical and powerful.

Use Eventarc triggers in event-driven pipelines (Dataform, Dataflow, Cloud Functions, Cloud Run, Cloud Composer)

Eventarc provides a way to route events from sources like Pub/Sub or Cloud Storage to various targets. A trigger in Eventarc listens for specific events and then invokes your chosen service. This allows you to build flexible and modular pipelines.

You can connect Eventarc triggers to:

• Dataflow for scalable data processing
• Cloud Functions for small, serverless workloads
• Cloud Run for containerized services
• Cloud Composer for orchestrating complex workflows
• Dataform for data transformation workflows

Using Eventarc helps you decouple components, making your architecture more maintainable. It also supports filters so only relevant events reach each service.

With Eventarc, you can build sophisticated event-driven solutions that handle errors, retries, and routing logic. Scaling is automatic, and you pay only for what you use, keeping costs predictable.

Conclusion

In this section, you learned how to schedule, automate, and monitor key data tasks in Google Cloud. Scheduled queries keep your data up to date, while the Dataflow job UI and Cloud Logging/Monitoring help you track pipeline health. You explored how to pick the right orchestration tool for your needs and how to build event-driven ingestion using Pub/Sub, BigQuery, and Eventarc. Together, these skills form a solid foundation for managing basic data processing tasks effectively.