Lets Fabric's T-SQL engines treat data files in Azure storage or OneLake (Parquet, CSV or JSONL) as a table. That is handy for peeking at a file, or for loading it with INSERT ... SELECT or CTAS.
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In the Ultra Transcenders books
Each book explains OPENROWSET in context, with comparison tables and the common traps.
Terms in this definition
- T-SQL
The dialect of SQL that Microsoft uses for Azure SQL and SQL Server. Azure Monitor logs are queried with KQL instead.
- Azure Storage
You reach this Azure data storage platform through a storage account; it covers blobs (Data Lake Storage included), file shares, queues and tables.
- OneLake
Built on Azure Data Lake Storage Gen2, it is the one logical data lake for an entire Microsoft Fabric tenant, provisioned automatically, and the place where every Fabric workload keeps its data.
- Parquet
Columnar file format, open source and aimed at analytics. Event Hubs Capture cannot produce it natively.
- Cluster Shared Volumes
Shared failover cluster storage that all nodes write to simultaneously, typically for Scale-Out File Server or Hyper-V. On SAN-backed storage, ReFS-formatted volumes work in redirected mode.
- JSONL
Format in which each line holds a single JSON object. Fine-tuning training data and batch input files must use it, so CSV, TSV or one big JSON array may be rejected; ordinary REST calls don't involve it.
- Event
Table in Log Analytics where entries from Windows event logs are kept.
- INSERT
Adds fresh rows to a table. You list the target columns and supply a value for each; it is part of Data Manipulation Language (DML).