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Workflow Activities

Infoveave’s workflow activities are designed to simplify data workflows. Infoveave workflows are built around the Extract, Transform, and Load (ETL) framework. Each activity fits into one of these categories based on its role in managing data. Extract activities bring in the raw data. Transform activities clean and refine it. Load activities ensure it reaches the right destination. This structured approach makes data management efficient and effective in Infoveave.

Infoveave offers a comprehensive suite of activities to support the Extract, Transform, and Load (ETL) framework. Below is an overview of these activities and their role in the ETL process.

  • Execute Query: Fetches data from databases using SQL queries. This activity enables users to extract structured data directly from relational databases for analysis.

  • Download Email: Retrieves emails based on filters like unread status, subject keywords, or specific timeframes. It helps automate email data collection for further processing.

  • Download from FTP: Collects files from remote FTP servers using defined patterns and directories. It ensures efficient file extraction for workflows.

  • Download from Azure, S3, and OneDrive: Downloads files from cloud storage platforms like Microsoft Azure, Amazon S3, and OneDrive, bringing external data into the workflow.

  • Execute API (GET): Fetches data from APIs by sending GET requests, allowing integration with external systems for real-time or historical data.

  • Transform Using JavaScript: Allows custom transformations using JavaScript for cleaning and reshaping data.

  • Transform Using SQL: Performs SQL-based transformations, including filtering, joining, and aggregating datasets.

  • Split PDF: Breaks a PDF document into smaller parts, making it easier to process specific sections of a file.

  • Update Files: Modifies existing files with new or updated information, keeping data current.

  • Rename Files: Renames files to maintain organizational standards or align with naming conventions.

  • Encrypt File Using PGP / Decrypt File Using PGP: Ensures secure handling of sensitive data by encrypting or decrypting files using PGP public keys during workflows.

  • Zip Files / Unzip Files: Compresses or extracts files to optimize storage and accessibility.

  • Write to File: Outputs transformed data into specific file formats for subsequent use.

  • Read Barcode: Extracts information from barcodes scanned from images or documents, digitizing physical data.

  • Merge Excel Sheets: Combines data from multiple Excel sheets into a single dataset for unified analysis.

  • Read CSV/Excel Files: Extracts data from structured files to bring it into the workflow for transformation.

  • Simplified Data Merge: Consolidates data from various sources into a unified dataset for further analysis.

  • Clear Cache: Removes outdated data from cache, ensuring workflows use the latest data for processing.

  • Execute Data Quality: Validates data for inconsistencies or anomalies, improving accuracy and usability.

  • Column Mapping and Grouping: Maps and organizes data into logical structures, preparing it for further use.

  • Concatenate Columns: Combines multiple columns into one, simplifying datasets for analysis.

  • Drop Columns / Select Columns: Removes unnecessary columns or selects relevant ones to streamline datasets.

  • Insert into Database: Adds transformed data to a database table, ensuring it is stored in a structured format for analysis or reporting.

  • Update Database: Updates existing records in the database with new or modified data, maintaining synchronization with the latest information.

  • Execute API (POST/PUT): Sends processed data to external APIs for integration with other systems, such as updating resources or creating new records.

  • Upload to FTP: Transfers the processed files to FTP servers for storage or further use.

  • Upload to Azure, S3, and OneDrive: Upload data to cloud storage platforms, making it accessible for reporting, analysis, or collaboration.

  • Upload to Datasource: Stores transformed data into a datasource, such as a data warehouse or a reporting platform, completing the pipeline.