Data Workspace is a Symend-provided data warehouse that you can utilize in Data Source Mappings. At the core, it is a Snowflake Data Warehouse built on the data within the Behavioral Engagement Platform™️. This provides you with a powerful capability: You can perform data engineering tasks such as combining data sources or calculating engineered Custom Attributes. After any manipulation you want to do is completed, this information can be re-imported back into the Platform as a Data Source directly to your Customer Accounts.

Table of Contents

Motivation

Assume you are importing data to the Platform using a standard File Import and you want to calculate risk for a customer within the Platform. How would you create a solution to this? The only answer today would be to create Playbooks that modify your data for you. This works for a simple solution, but as the complexity grows, so does the number of Playbooks, Segments, and Custom Attributes. This complexity can lead to overall slower Playbook execution, increased administrative overhead, and increased probability of human error.

Though valid, this issue can be seen as more theoretical. Instead, assume that you want to calculate the average number of days that a particular Customer takes to cure and create a strategy based on that number. This case would not be possible to do directly within the application without Data Workspace.

The power of Data Workspace is that you can manipulate your source data in conjunction with engagement Event Data captured by Symend. This provides flexibility to ensure that your data can support your engagement use cases.

Initial Setup and Access

Data Workspace Enablement

Before logging in to Data Workspace, this must be enabled for your Organization within the Platform. If you are unsure if Data Workspace is enabled, then please read the How do I import into Data Workspace? tutorial. If you are unable to see Data Workspace as an option when creating a Data Source Mapping, then Data Workspace is not active.

If Data Workspace is not enabled, then please reach out to your Symend contact and request that we turn on this feature.

For Symend Employees, please read the How do I request Data Workspace enablement? or How do I enable Data Workspace? tutorial articles depending on your permissions.

Obtaining Login Credentials

If you have not already obtained login credentials for your Data Workspace, you will need to do so before login as these permissions are currently controlled externally from the Platform. If you are unsure, then please attempt to log in by following the How do I log in to Data Workspace? tutorial and you will quickly find your answer.

If you do not have login credentials, please reach out to your Symend contact.

For Symend Employees please read How do I request my Data Workspace login? or How do I request a Data Workspace login for a client? depending on the situation.

Login URL

Once Data Workspace has been enabled, you will need to navigate to the appropriate URL as determined by the region that your Organization has been configured in. If you are unsure which region this is, please reach out to your Symend contact. Once you know which region this is in, then use the appropriate URL for logging in to your Data Workspace.

Schemas

When you first access Data Workspace, you will see at least two schemas. This table will highlight what each is and how they are used:

These schemas are the ones that exist by default, but if you are going to be doing data manipulation, you will have to create your own. Please read the How do I create a schema in my Data Workspace? tutorial for instructions and additional information.

Warnings Regarding Editing

There are two warnings regarding the editing of either your Data Workspace or Data Source Mappings. Neither are catastrophic, but they are worth being aware of to avoid potential complications. In both instances, remember that you can not edit an active Data Source Mapping.

Editing Your Data Source Mapping

The Data Source Mapping that you will need to create for mapping your file to your Data Workspace impacts what will be created in the Workspace in three ways. First, the name of your file will be the name of the table created in the SYMEND_DATA_MAPPER schema. Second, the names of the columns in your file will be the names of the columns in the table. Third, the data type will be reflected in the data type of the column.

In order to edit your Data Source Mapping, you will need to duplicate it, make your modifications, and activate your new one. When you do this, your old mapping will be deactivated. If you make modifications to the fields, file name, or data types, then a new table and columns will also be recreated. If you have a new file name, then a new table will be created. If your file name is the same, your old table will be renamed to FILENAME_ddMMyyyyHHmmss_ARCHIVE. In either case, if you want to preserve the data in the old table, you will need to manually migrate the data to your new table. The data will not be deleted when it is archived, but it will not be present in the new table.

If your Data Source Mapping from Data Workspace to Customer Attributes relies on a table in SYMEND_DATA_MAPPER then you will also want to read the next section on Editing Your Data Workspace Tables.

Editing Your Data Workspace Tables

The Data Source Mapping you create from your Data Workspace will likely rely on a table that you have created yourself. This also means you have the ability to edit this table.

When editing a table, if you have an active Data Source Mapping from your Data Workspace to Customer Attributes, then you will break the associated Data Source Mapping. You will then need to update that Data Source Mapping if you have made changes to the table it relies on.

Limitations

Compute Credits

Data Workspace operates on compute credits. As mentioned previously, Data Workspace runs on Snowflake. Therefore, for the most complete understanding please read the Snowflake Compute Cost documentation for the best information.

All Data Workspaces are created as an X-Small warehouse with 1000 compute credits. Because there is a maximum of 744 hours in a month, it is impossible to run out of credits. However, you may run into issues with your warehouse size if you find data transformation operations are taking longer than desired. To increase your warehouse tier, reach out to your Symend contact.

Data Source Mapping Quantity

When creating a Data Source Mapping without Data Workspace enabled. You can only have one active mapping at a time. When you enable Data Workspace, you can have two active mappings. One which maps to your Data Workspace, and one which maps to your Customer Attributes.

This limitation exists because your data will be imported by our Platform and sent directly to your Data Workspace. The first mapping is to determine how the file should be stored in your Data Workspace database. Then, your manual or automated modifications are made. Finally, your Data Workspace database needs to be mapped back to your Customer Attributes, which is what necessitates the second mapping.

You can have multiple files that map into your Data Workspace, but they must all be in the Data Source Mapping responsible for determining how the file data will be inserted into your Data Workspace.

Open

SYMEND_CORE Update Schedule

The SYMEND_CORE schema contains the data that has actively been used by the Platform such as your Non-PII Customer Attributes, or Events. This schema will only update or refresh the underlying data every two hours.

In the event that you are utilizing this data, you will likely need to combine it with the data imported into your Data Workspace. However, if your file comes in at 8 AM, and there is a new Customer, they will not yet be available in SYMEND_CORE. This has the potential to impact your automated tasks. The frequency will be increased in the future. In the meantime, please consider something such as importing your file into Data Workspace at 4 AM, running an automated task at 6 AM, and importing from Data Workspace at 7 AM.

External References

As mentioned in the summary of this article, Data Workspace has been built on Snowflake. As a result, their documentation should be able to provide you with the most detailed and up-to-date information. The ones that you will likely find most useful are:

• Snowflake API Reference: This contains every way you can connect to your Data Workspace except browser instructions

• Managing Data: This contains information on schemas, Tables, and Tasks

• SQL Command Reference: You will likely do the majority of your Data Warehouse interaction using SQL

• Monitoring Activity: This contains information on how to view Queries that have been run

• Viewing Task History: You will likely have tasks that run on a regular schedule, this contains information on how to monitor those tasks

Required Roles

Tutorials