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 Lakehouse in Microsoft Fabric is the best choice for storing semi-structured data while allowing access using T-SQL, KQL, and Apache Spark
B. Eventhouse - Designed for real-time event processing.
- Does not support T-SQL or Apache Spark writes.
C. Datamart - Built for self-service BI, supports T-SQL, but not Spark-based writing.
- Not optimized for semi-structured data.
D. Warehouse - Optimized for structured relational data (fact & dimension tables).
- Does not support KQL or Spark-based writes.
Lakehouse: data will only be written bij using Spark. You can use shortcuts to access the data from een Eventhouse for KQL queries. If real-time processing or KQL were requitements, Eventhouse would be more appropiate.
A lakehouse in Microsoft Fabric is a versatile storage option designed for semi-structured and unstructured data, combining the flexibility of a data lake with the structure of a data warehouse. It supports Apache Spark, T-SQL, and KQL queries, making it ideal for the requirements.
Why the other options are not suitable:
B. Eventhouse: Eventhouse is not a valid Microsoft Fabric storage option.
C. Datamart: A datamart is designed for structured data and primarily supports T-SQL, but not Spark or KQL.
D. Warehouse: A warehouse is optimized for structured data and supports T-SQL, but it is not designed for semi-structured data or Spark.
A lakehouse is the best choice for storing data in a format that allows Spark to write it while enabling T-SQL and KQL for querying.
The eventhouse is the only one out of the four options that allows the creation of a KQL Database, therefore it is the only option that fully meets the criteria (being able to read the date via KQL, T-SQL and Spark).
Semi structured data should be stored in a lakehouse
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