A data engineering team is evaluating the Databricks Lakehouse Platform for their organization’s analytics workloads. They are particularly interested in understanding how the platform’s Unified Analytics Engine can support their diverse data workloads, including batch and real-time processing, while also considering factors like cost, compliance, and scalability. Which of the following statements best describes the architecture and advantages of the Unified Analytics Engine in Databricks Lakehouse, especially in the context of supporting diverse data workloads with these constraints in mind? (Choose one correct answer)
A
The Unified Analytics Engine in Databricks is designed to process only batch workloads, achieving higher throughput by leveraging distributed computing, but lacks native support for real-time processing, potentially increasing complexity and cost for organizations requiring both workload types.
B
The Unified Analytics Engine in Databricks is architected to natively support both batch and real-time (streaming) data processing within a single engine, offering improved performance, scalability, and efficiency. This unified approach reduces the need for separate systems, thereby lowering complexity and cost, and is compliant with data governance standards.
C
The Unified Analytics Engine in Databricks separates batch and streaming processing into different subsystems, similar to traditional architectures, but integrates them through a unified API layer. This approach may introduce latency and additional costs for organizations with high-volume, real-time data needs.
D
The Unified Analytics Engine in Databricks is optimized exclusively for real-time analytics, and while it can process batch data, it does so less efficiently than traditional batch engines, potentially leading to higher operational costs for batch-heavy workloads.
Explanation:
The Unified Analytics Engine in the Databricks Lakehouse Platform is designed to natively handle both batch and real-time (streaming) data processing within a single engine. This architecture not only simplifies the data processing pipeline by eliminating the need for separate systems but also enhances performance, scalability, and cost-effectiveness. It supports diverse workloads efficiently, adheres to compliance standards, and is scalable to meet the organization's growing data needs, making it the best choice for organizations looking to optimize their analytics workloads under the given constraints.
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