Microsoft Azure Storage data output

This article provides an introduction to Microsoft Azure Storage along with a guide on how to create a Microsoft Azure Storage data output using Upsolver.

What is Microsoft Azure Blob storage?
Azure Blob storage is Microsoft's object storage solution for the cloud. Blob storage is optimized for storing massive amounts of unstructured data, such as text or binary data.
Core storage services offer a massively scalable object store for data objects, disk storage for Azure virtual machines (VMs), a file system service for the cloud, a messaging store for reliable messaging, and a NoSQL store. These services are durable and highly available, secure, scalable, managed and accessible.
Create a Microsoft Azure Storage data output
1. Go to the Outputs page and click New.
2. Select Microsoft Azure Storage as your output type.
3. Name your output and select whether the output should be Tabular or Hierarchical.
After adding your Data Sources, click Next.
Click Properties to review this output's properties.
See: Output properties​
4. Click the information icon
in the fields tree to view information about a field. 
The following will be displayed:
Density in Events
Density in Data
Distinct Values
Total Values
First Seen
Last Seen
How many of the events in this data source include this field, expressed as a percentage (e.g. 20.81%).
The density in the hierarchy (how many of the events in this branch of the data hierarchy include this field), expressed a percentage.
How many unique values appear in this field.
The total number of values ingested for this field.
The first time this field included a value, for example, a year ago.
The last time this field included a value, for example, 2 minutes ago.
Value Distribution
Field Content Samples Over Time
Selected
The percentage distribution of the field values. These distribution values can be exported by clicking Export.
A time-series graph of the total number of events that include the selected field.
The most recent data values for the selected field and columns. You can change the columns that appear by clicking Choose Columns.
5. Click the information icon
next to a hierarchy element (such as the overall data) to review the following metrics:
# of Fields
# of Keys
# of Arrays
Fields Breakdown
Fields Statistics
The number of fields in the selected hierarchy.
The number of keys in the selected hierarchy.
The number of arrays in the selected hierarchy.
A stacked bar chart (by data type) of the number of fields versus the density/distinct values or a stacked bar chart of the number of fields by data type.
A list of the fields in the hierarchy element, including Type, Density, Top Values, Key, Distinct Values, Array, First Seen, and Last Seen.
6. Click the plus icon
in the fields tree to add a field from the data source to your output. This will be reflected under the Data Source Field in the Schema tab. 
If required, modify the Output Column Name.
Toggle from UI to SQL at any point to view the corresponding SQL code for your selected output.
You can also edit your output directly in SQL.
See: Transform with SQL​
7. Add any required calculated fields and review them in the Calculated Fields tab.
 See: Adding calculated fields​
8. Add any required lookups and review them under the Calculated Fields tab. 
Adding Lookups
​from data sources​
​from lookup tables​
​from reference data​
9. Through the Filters tab, add a filter like WHERE in SQL to the data source.
See: Adding filters​
10. Click Make Aggregated to turn the output into an aggregated output. 
Read the warning before clicking OK and then add the required aggregation. 
This aggregation field will then be added to the Schema tab. 
See: Aggregation functions​
11. In the Aggregation Calculated Fields area under the Calculated Fields tab, add any required calculated fields on aggregations. 
See: Functions, Aggregation functions​
Click Preview at any time to view a preview of your current output.
12. Click Run and fill out the following fields:
Output Format
Microsoft Azure connection: How to create an Azure Blob Storage connection​
See: Running an output​
13. Click Next and complete the following:
Compute Cluster
Processing Time Range
Select the compute cluster to run the calculation on.
Alternatively, click the drop-down and create a new compute cluster.
The range of data to process. 
This can start from the data source beginning, now, or a custom date and time. This can never end, end now or end at a custom date and time.
14. Finally, click Deploy to run the output. 
 It will show as Running in the output panel and is now live in production and consumes compute resources.
You have now successfully created an output to Microsoft Azure Storage.

Google Storage data output

Qubole data output

Last modified 2yr ago

Copy link