This post is a part of the DP-900: Microsoft Azure Data Fundamentals Exam Prep Hub.
This topic falls under these sections:
Describe an analytics workload (25–30%)
--> Describe considerations for real-time data analytics
--> Describe the difference between Batch and Streaming data
Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.
Understanding the difference between batch data and streaming data is fundamental for designing modern analytics solutions. These two approaches define how data is ingested, processed, and analyzed.
What Is Batch Data?
Batch data refers to data that is:
- Collected over a period of time
- Processed in large chunks (batches)
- Handled at scheduled intervals
Key Characteristics of Batch Data
- High latency (minutes, hours, or days)
- Processes large volumes at once
- Typically scheduled (e.g., nightly jobs)
- Efficient and cost-effective
Common Use Cases
- Daily sales reports
- Monthly financial summaries
- Historical data analysis
- Data warehousing workloads
Azure Services for Batch Processing
- Azure Data Factory → batch ingestion and orchestration
- Azure Synapse Analytics → batch processing and analytics
What Is Streaming Data?
Streaming data refers to data that is:
- Generated continuously
- Processed in real time (or near real time)
- Handled as individual events or small micro-batches
Key Characteristics of Streaming Data
- Low latency (seconds or milliseconds)
- Continuous data flow
- Enables real-time insights
- Often requires more complex processing
Common Use Cases
- IoT sensor monitoring
- Fraud detection
- Live dashboards
- Website activity tracking
Azure Services for Streaming
- Azure Event Hubs → event ingestion
- Azure Stream Analytics → real-time processing
Batch vs Streaming — Key Differences
| Feature | Batch Processing | Streaming Processing |
|---|---|---|
| Data Flow | Periodic | Continuous |
| Latency | High | Low |
| Data Size | Large chunks | Small events |
| Complexity | Simpler | More complex |
| Cost | Lower | Higher |
| Use Case | Historical analysis | Real-time insights |
When to Use Batch Processing
Choose batch when:
- Real-time data is not required
- You are working with large historical datasets
- Cost efficiency is important
- Processing can occur on a schedule
When to Use Streaming Processing
Choose streaming when:
- You need real-time or near real-time insights
- Data is generated continuously
- Immediate action is required
Hybrid Approaches (Lambda / Modern Architectures)
Many modern systems use both:
- Batch layer → historical analysis
- Streaming layer → real-time insights
✔ Example:
- Real-time dashboard + nightly aggregated reports
Why This Matters for DP-900
On the exam, you may be asked to:
- Distinguish between batch and streaming scenarios
- Choose the appropriate processing method
- Identify Azure services for each approach
- Understand trade-offs (latency, cost, complexity)
Summary — Exam-Relevant Takeaways
✔ Batch processing
- Processes data in chunks
- Higher latency
- Lower cost
- Best for historical analysis
✔ Streaming processing
- Processes data continuously
- Low latency
- Enables real-time insights
- More complex
✔ Azure services:
- Batch → Azure Data Factory, Azure Synapse Analytics
- Streaming → Azure Event Hubs, Azure Stream Analytics
✔ Exam tip:
👉 Real-time requirement → Streaming
👉 Scheduled / historical → Batch
Go to the Practice Exam Questions for this topic.
Go to the DP-900 Exam Prep Hub main page.
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