Data Engineering Associate with Databricks Practice Exam

What type of analytics can a data lakehouse perform that a traditional data warehouse may struggle with?

1. Ad-hoc reporting

2. Streaming analytics

3. Backup and recovery

4. Data archiving

The correct answer is: Streaming analytics

A data lakehouse combines the advantages of both data lakes and data warehouses, allowing for more flexible data management and analytics. One significant type of analytics that a data lakehouse can effectively perform is streaming analytics. This capability is crucial for analyzing real-time data feeds from various sources, enabling organizations to derive insights almost instantaneously. In contrast, traditional data warehouses are often optimized for batch processing and may face challenges when handling continuous data streams. They typically require data to be ingested, transformed, and loaded before analysis can begin, which can create latency that is not suitable for applications needing immediate insights. The inherent architecture of a data lakehouse supports high volumes of incoming data and facilitates real-time querying and analysis without the bottlenecks seen in conventional data warehouse systems. This streamlining allows businesses to leverage real-time analytics for dynamic decision-making processes, making the lakehouse a more versatile solution for modern data requirements compared to traditional data warehouses.