This pipeline can be used to request and retrieve Excel files/folders (consisting similar structured Excel files). The transfer is enabled irrespective of whether Excel files are compressed or not.

Configuring the Credentials

Select the account credentials which has access to relevant Azure Blob Storage data from the dropdown menu & Click Next

Credentials not listed in dropdown ?

Click on + Add New for adding new credentials. Give your credentials a name, enter the Storage Account Name, Access Key, and Endpoint Suffix and click on Save.

Data Pipelines Details

Data Pipeline

Select EXCEL from the dropdown

azure blob storage excel list

Setting Parameters

Select the fields that are necessary as per the file or folder .

Parameter Description Values

Folder Path


Points to the path along which the files are present

String value (eg:folder/subfolder)

File Name


Specify the File Name. In cases where the user doesn’t remember complete name of file, specify file name match type using the operator which takes values as 'Exact, Startswith, Endswith, and Contains'.

String value (eg:abc.csv)

Default Value: Exact (For the operator)

Sheet Name


Specify the Sheet Name.

String value (eg:Sheet1)

Default Value: Sheet1

Process All Matching Files


Select Yes or No, depending on if all matching files are to be processed or not


Default Value: No

File Selection Criteria



(If Process All Files in Folder = NO)

Choose File’s creation or modification Date

{Date Created,Date Modified}

Default Value: Date Modified

Header Column Present


Choose Yes or No depending on if the file has a header column or not


Default Value: No

Header Row



(If Header Columns are Present = Yes)

Specify the row number at which header is present in the file

Integer value (eg:1)

Data Row


Specify the row number from which data starts

Integer value (eg:(2))

Attempt Schema Inference


If Yes then value types will be fetched as it is, eg: Float will be fetched as float. If No then everything will be fetched as string irrespective of its type.


Default Value: No

Insert Mode


Specifies the manner in which data will get updated in the data warehouse : Upsert will insert only new records or records with changes, Append will insert all fetched data at the end, Replace will drop the existing table and recreate a fresh one on each run.

{Upsert, Append, Replace}

Default Value: Replace




(If Upsert is chosen as the Insert Mode Type)

Enter the column name based on which data is to be upserted.

String value

Container Name


Enter the container name in lowercase.

String value



Choose Yes or No depending on the file compression


Default Value: No

Compression Type


Required (If Compressed = Yes)

Specify the file compression type


Default Value: Zip

Post Processing Action


Action to be performed once the file processing has been completed

{No Action,Move Files}

Default Value: No Action

Move File Destination



(If Post Processing Actions = Move Files)

Specify the folder where the files are to be moved

String value (eg:test_folder/)



Specify the row number containing the footer, data after this row will not be extracted

Integer value (eg:10)

File Encoding


Specify the encoding type of the file which will be used to decode the file

String value (eg:utf-8)

Include Source File Name


Set this parameter to 'YES' if you want to include source file name in the data warehouse.


Default Value: No

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Datapipeline Scheduling

Scheduling specifies the frequency with which data will get updated in the data warehouse. You can choose between Manual Run, Normal Scheduling or Advance Scheduling.

Manual Run

If scheduling is not required, you can use the toggle to run the pipeline manually.

Normal Scheduling

Use the dropdown to select an interval-based hourly, monthly, weekly, or daily frequency.

Advance Scheduling

Set schedules fine-grained at the level of Months, Days, Hours, and Minutes.

Detailed explanation on scheduling of pipelines can be found here

Dataset & Name

Dataset Name

Key in the Dataset Name(also serves as the table name in your data warehouse).Keep in mind, that the name should be unique across the account and the data source. Special characters (except underscore _) and blank spaces are not allowed. It is best to follow a consistent naming scheme for future search to locate the tables.

Dataset Description

Enter a short description (optional) describing the dataset being fetched by this particular pipeline.


Choose the events for which you’d like to be notified: whether "ERROR ONLY" or "ERROR AND SUCCESS".

Once you have finished click on Finish to save it. Read more about naming and saving your pipelines including the option to save them as templates here

Still have Questions?

We’ll be happy to help you with any questions you might have! Send us an email at info@datachannel.co.

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