Collections

Introduction

Collections in Robility refer to data structures or objects that allow bot developers to store, organize, and manipulate data efficiently during automation processes. Collections play a crucial role in managing data elements such as variables, arrays, lists, dictionaries, and other complex data types.

1. Variables: Collections can store individual data elements or variables, such as numbers, text strings, dates, and Boolean values. Variables are used to hold temporary or permanent data values that are used and modified throughout the automation workflow.
2. Arrays: Arrays are collections of variables or data elements of the same data type that are stored under a single name. They allow bot developers to store and access multiple related values using index-based referencing. Arrays are particularly useful for handling lists of items or data sets.
3. Lists: Lists are dynamic collections that can store a sequence of data elements of varying data types. Unlike arrays, lists can grow or shrink in size dynamically as data is added or removed. Lists are commonly used for processing and iterating through sets of data during automation tasks.
4. Dictionaries: Dictionaries, also known as associative arrays or maps, are collections that store key-value pairs. Each element in a dictionary consists of a unique key associated with a corresponding value. Dictionaries are ideal for storing structured data where quick access to values based on keys is required.
5. Collections Operations: RPA platforms and programming languages provide built-in functions and methods to perform various operations on collections. These operations include adding or removing elements, searching for specific values, sorting data, iterating through collection items, and merging or splitting collections as needed.
6. Data Management: Collections help bot developers efficiently manage and manipulate data during automation tasks. They enable data organization, retrieval, processing, and transformation, enhancing the automation workflow’s effectiveness and accuracy.

Benefits

1. Efficient Data Management: Collections allow for organized storage and retrieval of data elements, enabling efficient data management during automation processes.
2. Flexibility: Collections support various data types and structures, such as arrays, lists, dictionaries, and variables, providing flexibility in handling different types of data within the workflows.
3. Enhanced Data Processing: Collections facilitate data processing operations, including sorting, filtering, searching, and iterating through data elements, enhancing the automation workflow’s capabilities.
4. Improved Reusability: By storing data in collections, bot developers can create reusable components and modules that can be easily integrated into multiple automation tasks, reducing development time and effort.
5. Dynamic Data Handling: Collections like lists and dictionaries support dynamic resizing and modification of data, allowing for dynamic data handling based on changing requirements or input data sets.

Use Cases

1. Data Extraction and Processing: Collections are used to extract data from documents, emails, web pages, and other sources, process the extracted data, and store it in structured formats for further analysis or automation tasks.
2. Workflow Automation: Collections facilitate workflow automation by managing data inputs, intermediate results, and outputs, enabling seamless execution of complex automation processes.
3. Data Validation and Verification: Collections help in validating and verifying data against predefined criteria, performing data quality checks, and identifying discrepancies or errors during automation tasks.
4. Dynamic Data Handling: Collections like lists and dictionaries are used for dynamic data handling, such as managing customer records, processing transactional data, handling inventory information, and more.