How can we ensure data quality in real-time analytics?
Ensuring data quality in real-time analytics involves several key practices, such as data validation, monitoring, and cleansing.
What tools can help with data validation?
Tools like Apache Kafka, Talend, and Informatica can assist in validating data as it flows through your analytics pipeline.
How often should we monitor data quality?
It's best to monitor data quality continuously, especially in real-time systems, to catch issues as they arise.
What are common data quality issues in real-time analytics?
Common issues include data duplication, incomplete data, and latency in data updates, which can affect analysis accuracy.
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