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.

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.

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.

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.