What are the steps involved in a streaming data workflow?

The correct answer outlines a practical and common sequence utilized in streaming data workflows:

1. Ingest: This step involves capturing or receiving data in real-time from various sources, such as sensors, social media feeds, or transaction systems. Effective ingestion is crucial, as it ensures that the data flow into the system is uninterrupted and can be immediately processed.

2. Process: Once the data is ingested, it undergoes processing. This involves transforming, cleaning, enriching, or aggregating the data on-the-fly to prepare it for the next steps. Processing can include filtering out irrelevant data, applying algorithms to extract important features, or running real-time computations to derive insights.

3. Visualize: Finally, the processed data is visualized, allowing users to interpret the results easily. Visualization tools and dashboards can represent data in graphical formats, enabling stakeholders to make informed decisions quickly based on the streaming data insights.

This sequence effectively covers the lifecycle of streaming data from arrival to actionable insights, making it an essential workflow for applications that require real-time analysis and response.