An organization processes batch sales data at the end of every month to analyze sales trends and derive business insights. They want to improve accuracy and make near real-time decisions. What should the organization do?
A.
Change from a relational database to a NoSQL database.
B.
Filter the data so reports are generated faster.
C.
Switch from batch processing to stream processing.
D.
Process batch reports weekly instead of monthly.
C. Switch from batch processing to stream processing.
Stream processing processes data in real-time or near real-time as it arrives, enabling the organization to make timely decisions and improve accuracy by acting on the latest data.
This is a significant improvement over batch processing, which processes data at scheduled intervals (e.g., monthly or weekly) and may result in outdated insights.
Why not the other options?
A. Change from a relational database to a NoSQL database:
While NoSQL databases are suited for specific use cases (e.g., unstructured data, scalability), this change alone does not address the need for real-time decision-making.
B. Filter the data so reports are generated faster:
Filtering the data may speed up reporting but does not fundamentally address the organization's need for near real-time analysis.
D. Process batch reports weekly instead of monthly:
Reducing the batch interval may improve timeliness slightly, but it still doesn't achieve near real-time decision-making.
Conclusion:
To achieve near real-time decision-making and improve accuracy, the organization should switch from batch processing to stream processing, allowing them to analyze and act on data as it is generated.
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joshnort
2 weeks, 2 days agojoshnort
2 weeks, 2 days ago