Store semi-structured data readable by T-SQL, KQL, and Spark in an eventhouse

Ingest and transform batch data

Answer Correct answer: B — An eventhouse is the only listed Fabric store that can be read with KQL, T-SQL, and Spark, meeting the multi-engine read requirement.

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
an eventhouse Correct Answer
a datamart
a warehouse

fabric eventhouse kql tsql spark fabric data store decision guide semi-structured data eventhouse

Community Votes

57%

43%

57% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

KQL query capability is unique to eventhouses in Fabric; any requirement to read data via KQL rules out lakehouse, warehouse, and datamart as the sole store.

When data must be read with T-SQL, KQL, and Apache Spark and is written only by Spark, an eventhouse is the suitable Fabric data store because it is the only option among the choices that natively supports KQL reads alongside T-SQL and Spark.

Choosing a lakehouse solely because semi-structured data fits there, while overlooking that native KQL reads require an eventhouse (a lakehouse would need integration with Real-Time Intelligence).

Community Discussion (20 comments)

38578c4 👍 11 Selected: B

KQL is avaialble in eventhouse only https://learn.microsoft.com/en-us/fabric/fundamentals/decision-guide-data-store

fffsssggg 👍 7 Selected: B

Eventhouse: Read operations: KQL, Spark and T-SQL Write operations: KQL, Spark https://learn.microsoft.com/en-us/fabric/get-started/decision-guide-data-store

zxc01 👍 1 Selected: B

I think eventhouse should be correct answer because it can support KQL, Spark and T-SQL. And can support semi-structured data event not good like lakehouse. The problem if we keep semi-structured data in lakehouse, we still need eventhouse to execute KQL on them. However, we just can build shortcut in KQL database to point delta tables in lakehouse(by OneLake), we cannot point to semi-structured files.

DataConsultant 👍 1 Selected: A

A Lakehouse in Microsoft Fabric is the best choice for storing semi-structured data while allowing access using T-SQL, KQL, and Apache Spark

CMDev 👍 1 Selected: B

Check this link and choose an answer, it must be B. https://learn.microsoft.com/en-us/fabric/fundamentals/decision-guide-data-store

Adriel_1996 👍 1 Selected: B

Based on documentation read on eventhouse allows SQL,T-SQL and Spark. https://learn.microsoft.com/en-us/fabric/fundamentals/decision-guide-data-store

Pierrejul 👍 1 Selected: B

https://learn.microsoft.com/en-us/fabric/fundamentals/decision-guide-data-store

ayushc 👍 2 Selected: A

By default you cannot query a lakehouse with kql but if you integrate it with Real time analytics you are able to query the lakehouse with kql

pierre314 👍 1 Selected: B

Eventhouse: Read operations: KQL, Spark and T-SQL Write operations: KQL, Spark

JensQ 👍 1 Selected: B

Out of the available options, only eventhouse can be read using KQL

Pierrejul 👍 1 Selected: A

lakehouse permit to read Spark data We are not in KQL

SannaLH 👍 2 Selected: B

Like 38578c4 link says: Eventhouse support read with: KQL, T-SQL, Spark & write with: KQL, Spark, connector ecosystem

568f95c 👍 3 Selected: A

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.

GLG 👍 3 Selected: A

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.

basene 👍 1 Selected: A

You are deal with semi-structured data and we would like to use only spark.

chimuelo69 👍 4 Selected: A

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.

mmanrik 👍 3 Selected: B

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).

QAZdbarhate12345678 👍 3 Selected: A

lakehouse is the most appropriate choice for this scenario

Tuki93 👍 3 Selected: A

You have semi-structured data. The data will only be written by using Spark.

kryper_reg 👍 2 Selected: A

Semi structured data should be stored in a lakehouse

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Expert Analysis

Why the Answer Is Correct

The Fabric data store decision guide shows that an eventhouse supports read operations via KQL, T-SQL, and Spark, while a lakehouse does not natively serve KQL queries without integrating Real-Time Intelligence. Because the requirement explicitly includes reading the data with KQL, the eventhouse is the only listed store that fully satisfies all three read engines, and it also accepts Spark writes.

Why the Other Options Are Wrong

Option A (lakehouse) stores semi-structured data and supports Spark and T-SQL via the SQL endpoint, but not native KQL reads. Option C (datamart) is for self-service downstream reporting, not multi-engine analytical reads. Option D (warehouse) is relational and supports T-SQL, not KQL.

Community Comment Notes

Community voting is close (B 57 vs A 43). The highest-voted comment (38578c4, 11 likes) states KQL is available only in eventhouses, and several others link the Fabric decision guide confirming eventhouse read engines include KQL, T-SQL, and Spark.

Official Reference