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…

The correct answer is C. Switch from batch processing to stream processing. Stream processing ingests and analyzes data continuously as it is generated, enabling near real-time insights and faster decision-making. Batch processing, by contrast, accumulates data and processes it at scheduled intervals (e.g., monthly), introducing significant latency…

Data Analytics Strategy

Question

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?

Options

AChange from a relational database to a NoSQL database.
BFilter the data so reports are generated faster.
CSwitch from batch processing to stream processing.
DProcess batch reports weekly instead of monthly.

How the community answered

(29 responses)

A
3% (1)
B
10% (3)
C
83% (24)
D
3% (1)

Explanation

Stream processing ingests and analyzes data continuously as it is generated, enabling near real-time insights and faster decision-making. Batch processing, by contrast, accumulates data and processes it at scheduled intervals (e.g., monthly), introducing significant latency. Switching to stream processing directly addresses the requirement for improved accuracy and near real-time decisions. Changing database types, filtering data, or shortening the batch interval to weekly all still introduce delays and do not achieve near real-time processing.

Topics

#Stream Processing#Batch Processing#Real-time Analytics#Data Processing Patterns

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