Learn the best data store for handling semi-structured data in Microsoft Fabric, supporting T-SQL, KQL, and Apache Spark. Ideal for DP-700 exam prep.
Table of Contents
Question
You have a Fabric workspace.
You have semi-structured data.
You need to read the data by using T-SQL, KQL, and Apache Spark. The data will only be written by using Spark.
What should you use to store the data?
A. a lakehouse
B. an eventhouse
C. a datamart
D. a warehouse
Answer
A. a lakehouse
Explanation
A lakehouse in Microsoft Fabric integrates the flexibility of data lakes with the structure of data warehouses, making it ideal for semi-structured and structured data. It supports reading data with T-SQL, KQL, and Apache Spark, while also enabling data ingestion and transformations through Spark. This compatibility ensures the lakehouse can handle Spark-written data seamlessly while providing advanced querying and analytics capabilities for diverse workloads.
Lakehouse Features:
- Combines data lake storage and warehouse capabilities.
- Supports multi-modal queries (T-SQL, KQL, Spark) critical for analytics in Fabric.
Compatibility with Apache Spark:
- Since the data is written using Spark, the lakehouse ensures seamless integration without additional transformations.
Eventhouse and Datamart:
- Eventhouse: Focuses on real-time event data, unsuitable for Spark-written semi-structured data.
- Datamart: Targets self-service analytics with structured data, not semi-structured or Spark-centric scenarios.
Warehouse:
- Optimized for highly structured data but lacks native support for semi-structured data and Spark-written formats.
This makes the lakehouse the most versatile and efficient choice for the described scenario.
Microsoft DP-700 certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Microsoft DP-700 exam and earn Microsoft DP-700 certification.