What is an important step to take after data ingestion and before visualization in a streaming data workflow?

In a streaming data workflow, data processing is a crucial step that follows data ingestion and precedes visualization. This is because, during the ingestion phase, raw data is collected and can be noisy, unstructured, or not yet in a usable format for analysis and visualization. Data processing includes tasks such as transformation, aggregation, and enrichment, which help to prepare the data for insightful analysis.

During data processing, one might perform operations like filtering out irrelevant or redundant information, converting data types to appropriate formats, and applying algorithms for aggregating results over time. These steps not only ensure the data is in a structured form that can be easily visualized but also enhance the quality and reliability of the insights we derive from it.

While data cleaning, data anonymization, and data archiving are all significant aspects of data management, they serve distinct purposes. Data cleaning focuses on correcting inaccuracies, data anonymization ensures compliance with privacy regulations, and data archiving deals with storing historical data. However, none of these steps directly sets the stage for effective visualization like data processing does, which is essential for analyzing real-time or near-real-time information.