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In this article, you learn the basic steps to load and analyze data with Data Explorer for Azure Synapse.
In Synapse Studio, on the left-side pane, select Manage > Data Explorer pools.
Select New, and then enter the following details on the Basics tab:
Setting | Suggested value | Description |
---|---|---|
Data Explorer pool name | contosodataexplorer | This is the name that the Data Explorer pool will have. |
Workload | Compute optimized | This workload provides a higher CPU to SSD storage ratio. |
Node size | Small (4 cores) | Set this to the smallest size to reduce costs for this quickstart |
Important
Note that there are specific limitations for the names that Data Explorer pools can use. Names must contain lowercase letters and numbers only, must be between 4 and 15 characters, and must start with a letter.
Select Review + create > Create. Your Data Explorer pool will start the provisioning process.
In Synapse Studio, on the left-side pane, select Data.
Select + (Add new resource) > Data Explorer database, and paste the following information:
Setting | Suggested value | Description |
---|---|---|
Pool name | contosodataexplorer | The name of the Data Explorer pool to use |
Name | TestDatabase | The database name must be unique within the cluster. |
Default retention period | 365 | The time span (in days) for which it's guaranteed that the data is kept available to query. The time span is measured from the time that data is ingested. |
Default cache period | 31 | The time span (in days) for which to keep frequently queried data available in SSD storage or RAM, rather than in longer-term storage. |
Select Create to create the database. Creation typically takes less than a minute.
Once your pool is deployed, in the Synapse Studio, on the left-side pane, select Develop.
Select + (Add new resource) > KQL script. On the right-side pane, you can name your script.
In the Connect to menu, select contosodataexplorer.
In the Use database menu, select TestDatabase.
Paste in the following command, and select Run to create a StormEvents table.
.create table StormEvents (StartTime: datetime, EndTime: datetime, EpisodeId: int, EventId: int, State: string, EventType: string, InjuriesDirect: int, InjuriesIndirect: int, DeathsDirect: int, DeathsIndirect: int, DamageProperty: int, DamageCrops: int, Source: string, BeginLocation: string, EndLocation: string, BeginLat: real, BeginLon: real, EndLat: real, EndLon: real, EpisodeNarrative: string, EventNarrative: string, StormSummary: dynamic)
Tip
Verify that the table was successfully created. On the left-side pane, select Data, select the contosodataexplorer more menu, and then select Refresh. Under contosodataexplorer, expand Tables and make sure that the StormEvents table appears in the list.
Paste in the following command, and select Run to ingest data into StormEvents table.
.ingest into table StormEvents 'https://kustosamples.blob.core.windows.net/samplefiles/StormEvents.csv' with (ignoreFirstRecord=true)
After ingestion completes, paste in the following query, select the query in the window, and select Run.
StormEvents
| sort by StartTime desc
| take 10
The query returns the following results from the ingested sample data.
Events
Mar 31, 11 PM - Apr 2, 11 PM
The biggest Fabric, Power BI, and SQL learning event. March 31 – April 2. Use code FABINSIDER to save $400.
Register todayTraining
Learning path
Data analysis in Azure Data Explorer with Kusto Query Language - Training
Learn how to analyze data in Azure Data Explorer using the Kusto Query Language
Certification
Microsoft Certified: Azure Data Engineer Associate - Certifications
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