A data organization leader is upset about the data analysis team’s reports being different from the data engineering team’s reports. The leader believes the siloed nature of their organization’s data engineering and data analysis architectures is to blame. Which of the following describes how a data lakehouse could alleviate this issue?

The core problem described is data inconsistency between teams due to siloed data architectures. A data lakehouse directly addresses this by unifying the data lake (typically used by data engineers for raw data and ETL) and the data warehouse (used by analysts for BI and reporting) into a single platform. This consolidation eliminates redundant data copies and synchronization issues. By having both the data engineering and data analysis teams operate on the same, governed data repository, the lakehouse establishes a single source of truth, ensuring that reports are consistent and reliable across the organization.

A. Autoscaling is a compute performance and cost-optimization feature; it does not address the root cause of data inconsistency between separate architectural silos.
C. A data lakehouse is a technical architecture. It does not mandate or cause organizational changes like departmental restructuring.
D. While a unified platform enhances collaboration, the primary solution to report discrepancies is the unified data source, not the collaborative tooling itself.
E. Faster response to ad-hoc requests is a potential secondary benefit of a simplified architecture, but the direct solution to inconsistent reports is establishing a single source of truth. ---

1. Databricks Documentation

"What is a data lakehouse?": "A data lakehouse is a data management architecture that combines the flexibility

cost-efficiency

and scale of data lakes with the data management and ACID transactions of data warehouses... By building a single system

a data lakehouse eliminates the data silos that traditionally separate analytics

BI

data science

and machine learning." This directly supports the idea of eliminating silos to create a single system.

2. Armbrust

M.

Ghodsi

A.

Xin

R.

& Zaharia

M. et al. (2021). "Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics." CIDR 2021.: The paper states

"We argue that the solution is to implement a data warehouse's functionality directly on the data lake... This new design

the lakehouse

eliminates the main problems with the two-tier architecture [data lake + data warehouse]: the data is not split into two different systems

reducing staleness and management overhead

and there is direct access to the data for advanced analytics." (Section 1: Introduction). This highlights the elimination of the two-system problem

which is the root of the inconsistency.

3. Databricks Blog

"What Is a Data Lakehouse?": "The data lakehouse is what you get when you implement data warehouse structures and data management features on the low-cost storage used for data lakes. It’s a single platform to house all of your data... This eliminates the need to move or copy data for different workloads and breaks down data silos

providing a single source of truth." (Section: "What is a data lakehouse?"). This explicitly links breaking down silos with providing a single source of truth.