Get support for Datasets and lookup
Introduction
Datasets
A dataset is a set or collection of data. Every column is associated with a particular variable, and each row corresponds to a unique member of the data set. This process of the datasets is a part of data management. It helps to describe the value of each variable appropriately.
- 1 Introduction
- 1.1 Datasets
- 2 Overview
- 2.1 Datasets
- 2.2 Steps to use datasets
- 3 Introduction
- 3.1 Lookup
- 4 Overview
Overview
Datasets
Dataset is a combination of various variables for conducting effective data management considering a unique user. It helps to provide a logical collection of data for accessing the automation expressions easily. The frequently accessed data can be saved within the Sapper environment through the data set.
You can add data manually or by simply importing a CSV file. It will also allow you to eliminate the repetitive process of feeding data. To make the dataset process more simple you need to use Loop dataset action. It will help you to access the expressions of specific column numbers associated with a row of a datasets.
For more information please visit https://sappersoftware.atlassian.net/wiki/spaces/SD/pages/677445690
Steps to use datasets
To use the datasets you need to find the option from the Sapper home page
2. To upload the data set, click on the data set option from the account menu.
3. This page will appear on the screen, from here you can import dataset.
4. Click on the import dataset option to upload the data. Now, select the preferred location of the CSV file.
5. You can also upload the data manually. For manual process, click on create data set and the form will be appear on the screen.
6. Now, provide the name for the datasets. Add a brief description about it, and provide a header. Now, click on the save to complete the process.
Input | Description | Examples |
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Name | A dataset name can be from one to a series of twenty-two joined name segments combine with various alphabets. |
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Description | Provide a short description for the use of the datasets. |
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Header | A header necessarily stores the names or headings for each of the columns. It helps you to identify the role of the respective column in the data frame. |
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6. Click on the add button to create more headers and save the details. You can add as much as headers you want for the datasets. The headers can be deleted by clicking on the cross button from the right side of the header.
7. Once the datasets is created, you can view all the saved data set and modify them by clicking on it. You can also delete it by clicking on the delete action. To find the specific dataset you need to search it from the search option.
Input | Description | Examples |
|---|---|---|
Name | This section is describe about the name of the dataset. | Demo |
Description | In this section you will get the description of the dataset. |
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Number of column | This section describe about the number of column included in the specific dataset. | 3 |
Action | The action section is involved with a delete and edit option. |
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8. You can also add column and description in the created datasets if required. To do the same you need to click on the existing datasets and in the top of the left screen you will get the option for adding a description.
9. On the other hand, in the top of the right screen you will get the option for adding a column. Once you click on this option the form will be appear on the screen. Here you can add the name and can save it for further process.
10. You can also add a new record by clicking on the “Add new record” button. Once you click in this button the another section for adding dataset will be appear on the screen. In this section you can add the details and for saving the dataset you need to click on the save button appear on the right side of the new dataset field.
Introduction
Lookup
A lookup table consists of the value that is looked up, it’s also known as the returned value. The lookup table is associated with various formulas, that can be used in the automation based on the requirements.
Overview
Lookup Table and Formula
The lookup table is similar to the cross-reference tables that allows you cross check the frequently used look up data. Like the dataset the lookup tables are also associated with various rows and columns. You can easily match lookup entries in one or more columns of the lookup table.
Lookup table is included with the core three parts i.e., Value, Returned Value, and Similarity threshold.
You can add and update the lookup table entries by importing it into a CSV file, or by entering it manually. To import the lookup table in the CSV file you will be required details for team owner, admin or analyst system role.
Core types of Lookup table
Input | Description | Examples |
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Value | In lookup table a value that LOOKUP searches for in an array. It can be a number, text, a logical value, or a name. |
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Returned Value | The lookup return value is depends on the return value properties that you define, such as multiplicity or a lookup expression. |
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Similarity threshold | It is the degree of variation that can exist in the spelling or representation of the value. If you want the definition to affect values that are different from the value in the definition, you can set the similarity threshold lower than the default of 900. |
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The Advantages of Lookup Table
The core advantage of the lookup tables is their speed, you can quickly cross-check the data in a faster manner by looking up it from the list.
You can read the data from the specific Lookup database table faster.
The lookup table helps you to give a simple process of importing data.
The Disadvantage of Lookup Table
The core disadvantage of lookup tables is their memory usage. It has a maximum of 10 columns and you can do a maximum of 100,000 entries
Lookup Formula
The lookup table can be used by the actions or by using formulas. This table is associated with various formulas that can be used for different automation.
Example of lookup formula
lookup("TABLE_NAME", "REFERENCE_COLUMN": datapill)["LOOKUP_COLUMN"]
TABLE_NAME - The name of the lookup table.
REFERENCE_COLUMN - The name of the column in the lookup table to be used as a reference.
LOOKUP_COLUMN - The name of the column in the lookup table you want to look up data for.
Lets put an example of lookup("Account IDs", "Salesforce account ID": datapill)["Intacct account ID"]
This formula uses the Account IDs table. It will look up the Intacct account ID of the first entry that matches any given Salesforce account IDSteps to use Lookup formulas
The lookup formulas can be used in various automation based on the requirements.
Create or select an existing automation from the automation tab of sapper homepage.
2. To create a automation you need to click on the create automation option located in top right side of the screen.
3. Provide the name, description and save it for further process. If you want to use the existing automation then you need to select it from the provided automation list from the “My Automation” field. You can also search for a specific automation from the “search for automation field”.
4. Click on the specific application and the form will be open on the left side of the screen.
5. Here you can use the formulas from the right side of the provided option.
6. Suppose you want to add a formula in the “First Name”, then you need to click on the formula above this section.
7. Now search for the lookup formula, and you will get the available options. Select the required one.
8. The formula will be selected for the “First Name” section now you need to save it for completing the process.
9. You can add formulas in the various field, based on your requirements and options.
10. You can add multiple formula’s for one field. After adding one formula you need to add a coma then you can search and select the another formula for the specific field based on your requirements.