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Answer: Implementing SSL/TLS encryption for data in motion between the source and Spark Structured Streaming jobs
Implementing SSL/TLS encryption for data in motion between the source and Spark Structured Streaming jobs is the most suitable security mechanism to ensure that streaming data is protected end-to-end in real-time data processing with Apache Spark Structured Streaming in Azure Databricks. Here‘s why: 1. **SSL/TLS encryption**: Provides secure communication over the network by encrypting data in transit, protecting data from unauthorized access or interception during transmission. 2. **End-to-end protection**: Ensures data remains encrypted throughout the entire data processing pipeline, from ingestion to processing and output. 3. **Compliance with security best practices**: A common practice for securing data in transit, helping prevent data breaches and ensuring compliance with security standards. 4. **Ease of implementation**: Relatively easy to implement and configure in cloud environments like Azure Databricks, enhancing security without significant overhead.
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How can you ensure end-to-end security for real-time data processing with Apache Spark Structured Streaming in Azure Databricks?
A
Utilizing Azure Event Hubs Capture with encryption at rest and integrating with Databricks for secure data ingestion
B
Configuring Databricks clusters to use Transparent Data Encryption (TDE) for all structured streaming operations
C
Implementing SSL/TLS encryption for data in motion between the source and Spark Structured Streaming jobs
D
Enabling network isolation with Azure Private Link for all data sources and sinks involved in structured streaming
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