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Companion workflow for Spreadsheet File node docs

Export WordPress Posts to Spreadsheet and download .csv to your local machine.

Read data from Postgres Converting it to XLS Save it to disk

This workflow demonstrates how easy it is to export SQL query to CSV automatically! Before running the workflow please make sure you have access to a local or remote MSSQL server with a sample AdventureWorks database. The detailed process is explained in the tutorial https://blog.n8n.io/sql-export-to-csv/

This workflow extends n8n and uses R language graphic capabilities. This is a Telegram bot which fetches weather data via the openweathermap.org API, plots an image using ggoplot2 package from R and sends the image to the Telegram user.

This workflow is a supporting automation to a common Airtable situation, that as of this writing, has no direct solution but has great demand. Interfaces are your secret weapon for managing a variety of tasks – from sales funnels and task tracking to creating dynamic dashboards. But here's a common situation: how do you efficiently bulk upload records (like contacts, leads, or clients) from an interface with just a click? Once set up, you'll be able to upload CSV files directly to your tables from the Interfaces with ease. Workflow Key Points: 1. Bulk Upload Functionality: Say goodbye to the limitations of standard Airtable interfaces. Now, you can upload multiple leads or contacts simultaneously, making your work swift and efficient. 2. Customizable Fields: Tailor the base to meet your specific data needs. This ensures seamless integration with your existing systems and simplifies data management. ​ Perfect for teams in e-commerce, CRM, or any sector where managing a high volume of leads or contacts is key. Our Airtable Base is designed to eliminate the tediousness of importing contacts. It makes large-scale data management straightforward, saving you precious time and hassle. ​ Get ready to streamline your operations and boost your productivity! 🚀💡

A workflow to send personalized emails with respective attachment. The workflow needs a pre-formatted CSV with file names and email IDs.

This workflow gets data from an API and exports it into Google Sheets and a CSV file.

This workflow demonstrates the conversion of a CSV file to Excel format. First, an example CSV file is downloaded via a direct link. The source file is taken from the European Open Data Portal: https://data.europa.eu/data/datasets/veranstaltungsplaetze-potsdam-potsdam?locale=en The binary data is then imported via the Spreadsheet File node and converted to Excel format. N.B. Note that as of version 1.23.0 n8n, the Spreadsheet File node has been redesigned and is now called Convert to File node. Learn more on the release notes page: https://docs.n8n.io/release-notes/#n8n1230

This workflow automatically downloads a CSV from the web, and parses it in a format that n8n can access. It then ensures that the data from the CSV is matched to the names of the columns in the database, and inserts this data as new rows in Snowflake. Prerequisites: A CSV with data A Snowflake account and credentials A Snowflake database to upload your CSV to Nodes: A HTTP Request node to download the CSV file A Spreadsheet File node to access the data from the CSV A Set node to ensure the data from the CSV is mapped to the column names of the Snowflake database A Snowflake node to insert these new rows into the database.

This workflow demonstrates how CSV file can be automatically imported into existing MySQL database. Before running the workflow please make sure you have a file on the server: /home/node/.n8n/concerts-2023.csv And the content of the file is the following: Date,Band,ConcertName,Country,City,Location,LocationAddress, 2023-05-28,Ozzy Osbourne,No More Tours 2 - Special Guest: Judas Priest,Germany,Berlin,Mercedes-Benz Arena Berlin,"Mercedes-Platz 1, 10243 Berlin-Friedrichshain", 2023-05-08,Elton John,Farewell Yellow Brick Road Tour 2023,Germany,Berlin,Mercedes-Benz Arena Berlin,"Mercedes-Platz 1, 10243 Berlin-Friedrichshain", 2023-05-26,Hans Zimmer Live,Europe Tour 2023,Germany,Berlin,Mercedes-Benz Arena Berlin,"Mercedes-Platz 1, 10243 Berlin-Friedrichshain", 2023-07-07,Depeche Mode,Memento Mori World Tour 2023,Germany,Berlin,Olympiastadion Berlin,"Olympischer Platz 3, 14053 Berlin-Charlottenburg", The detailed process is explained in the tutorial https://blog.n8n.io/import-csv-into-mysql

Summary Onfleet is a last-mile delivery software that provides end-to-end route planning, dispatch, communication, and analytics to handle the heavy lifting while you can focus on your customers. This workflow template loads in a spreadsheet from your local storage and automatically creates Onfleet tasks on a one-time basis upon workflow trigger. You can use this workflow as a task importer. Configurations Update the Read Binary File node with the absolute file path to the local spreadsheet of interest Update the Onfleet node with your own Onfleet credentials, to register for an Onfleet API key, please visit https://onfleet.com/signup to get started You can easily change how the Onfleet task is created by mapping to additional data in the spreadsheet For import templates, visit Onfleet Support to learn more 👍

This workflow automatically imports data from a CSV file located at a specific URL and then updates the Google Sheets document with the provided data. Below is a step-by-step description of what this workflow does: The workflow is started manually using the "When you click 'Execute Workflow'" node. The CSV file is then uploaded from the specified URL "https://opendata.ecdc.europa.eu/covid19/testing/csv/data.csv" using the "Upload CSV" node. The "Import CSV" node accepts the uploaded CSV file and converts it into JSON formatted data. The "Add Unique Field" node generates a unique key by combining the 'country_code' and 'year_week' fields from the JSON data, which will be further used in the Google Sheets document. The 'Keep only DACH in 2023' node filters the data to keep only records where 'country_code' is either 'DE', 'AT', or 'CH' and 'year_week' starts with '2023'. Google's API has limitations on the speed of read and write operations, so only a subset of the data is taken. The filtered data is loaded into the specified Google Sheets document via the 'Load to Spreadsheet' node. The operation is set to 'appendOrUpdate' and the document ID and sheet name are specified. Also, the previously generated 'unique_key' key is set as the key to match the columns.

This workflow template shows how to load JSON data into a workflow and push that data into an App or convert it into a Spreadsheet file. Specifically, this workflow shows how to make a generic API request that returns JSON. It then shows how to load that into a Google Sheets spreadsheet, or convert it to .CSV file format. However, you can use the general pattern to load data into any app or convert to any spreadsheet file format (such as .xlsx).

This workflow gets leads' contacts from a CSV file and adds it to the Pipedrive CRM by creating an organization and a person. The CSV file in this workflow serves as a universal connector allowing you to export contacts from any platform like LinkedIn, Facebook, etc. Prerequisites Google account and Google credentials Pipedrive account and Pipedrive credentials How it works The Google Drive Trigger node starts the workflow when a new CSV file is uploaded to a specific folder in Google Drive. Google Drive node downloads the CSV file. Spreadsheet File node reads data from the CSV file and sends the output to the Merge node. This Spreadsheet File's output becomes the input 1 for the Merge node. Meanwhile, the Pipedrive node gets the same list of contacts from the CSV file. IF node checks if Pipedrive has these contacts already created previously and sends the checked results to the Merge node. These results arrive at the Merge node as input 2. Merge node compares two inputs via email and removes the matches. Pipedrive node creates new contacts based on the data provided by the Merge node with necessary details such as organization and notes.

Overview This n8n workflow is designed to extract specific interests from messages in a Telegram chat and retrieve related information using the Facebook Graph API. It aims to provide a streamlined solution for parsing and analyzing user-provided interests within the Telegram platform. Features Interest Extraction:** Automatically identifies and extracts interests from messages that start with the hashtag "#interest". Data Retrieval:** Utilizes the Facebook Graph API to retrieve information related to the extracted interests. Structured Outputs:** Presents the retrieved data in an organized format for further analysis and review. Requirements Operational instance of n8n (self-hosted or cloud version). Basic understanding of n8n workflows and nodes. Setup and Configuration Import Workflow: Load the provided JSON workflow into your n8n instance. Configure Telegram Trigger Node: Ensure the Telegram trigger node is set up with the appropriate credentials and webhook ID. Configure and Test Nodes: Adjust node parameters as necessary and test the workflow to ensure proper functionality. How it Works Telegram Trigger: Listens for incoming messages in a specified Telegram chat. Check Message Contents: Verifies if the message begins with the specified hashtag and is from the designated chat ID. Extract Message: Extracts the content of the message for further processing. Split Message: Splits the extracted message to identify the interest and remaining content. Connect to Graph API: Utilizes the Facebook Graph API to search for information related to the extracted interest. Split Interests into a Table: Organizes the retrieved data into a structured table format. Get Variables: Maps the retrieved data to create a new JSON object containing specific fields related to the interest. Create a Spreadsheet: Generates a spreadsheet file in CSV format based on the retrieved and formatted data. Send the Spreadsheet File: Sends the generated spreadsheet file back to the original Telegram chat. Customization Modify the filtering conditions and fields to suit specific requirements. Adjust the frequency of the trigger node based on preference. Best Practices Regularly test the workflow to ensure consistent performance. Stay informed about any changes to external APIs that might affect the workflow's functionality. Contributing Your feedback and contributions are highly valued. Feel free to adapt, modify, and share enhancements with the n8n community.

Convert PostgreSQL table to CSV CSV is a super useful and universal way to transfer data between different tools. This workflow gives an example of how to take data from PostgreSQL and convert it easily into a CSV. What you need Before running the workflow, please make sure you have access to a remote PostgreSQL server and have table data: book_title,book_author,read_date Demons,Fyodor Dostoyevsky,2022-09-08 Ulysses,James Joyce,2022-05-06 Catch-22,Joseph Heller,2023-01-04 The Bell Jar,Sylvia Plath,2023-01-21 Frankenstein,Mary Shelley,2023-02-14 How it works Trigger the workflow on click Declare the name of the Excel file and sheet names Remotely connect to the PostgreSQL database and specify query execution Write the query data to CSV The detailed process is explained further in the tutorial: https://blog.n8n.io/postgres-export-to-csv/

This workflow shows a no code approach to creating a HTML table based on Google Sheets data. To run the workflow: Make sure you have a Google Sheet with a header row and some data in it. Grab your sheet ID: Add it to the Google Sheets node: Activate the workflow or execute it manually Visit the URL provided by the webhook node in your browser (production URL if the workflow is active, test URL if the workflow is executed manually)

This workflow shows a no code approach to creating Salesforce accounts and contacts based on data coming from an Excel file. For Excel 365 (the online version of Microsoft Excel) check out this workflow instead. To run the workflow: Make sure your Salesforce account is authenticated with n8n. Have a Microsoft Excel workbook with contacts and their account names ready. The workflow uses this example file, but you probably want to use your own data instead. Hit the Execute Workflow button at the bottom of the n8n canvas. Here is how it works: The workflow first searches for existing Salesforce accounts by name. It then branches out depending on whether the account already exists in Salesforce or not. If an account does not exist yet, it will be created. The data is then normalised before both branches converge again. Finally the contacts are created or updated as needed in Salesforce.

This workflow demonstrates how easy it is to export SQL query to Excel automatically! Before running the workflow please make sure you have access to a remote SQL server (MS SQL, MySQL, PostgreSQL etc.) with a sample table: Date,Band,ConcertName,Country,City,Location,LocationAddress, 2023-05-28,Ozzy Osbourne,No More Tours 2 - Special Guest: Judas Priest,Germany,Berlin,Mercedes-Benz Arena Berlin,"Mercedes-Platz 1, 10243 Berlin-Friedrichshain", 2023-05-08,Elton John,Farewell Yellow Brick Road Tour 2023,Germany,Berlin,Mercedes-Benz Arena Berlin,"Mercedes-Platz 1, 10243 Berlin-Friedrichshain", 2023-05-26,Hans Zimmer Live,Europe Tour 2023,Germany,Berlin,Mercedes-Benz Arena Berlin,"Mercedes-Platz 1, 10243 Berlin-Friedrichshain", 2023-07-07,Depeche Mode,Memento Mori World Tour 2023,Germany,Berlin,Olympiastadion Berlin,"Olympischer Platz 3, 14053 Berlin-Charlottenburg", The detailed process is explained in the tutorial https://blog.n8n.io/export-sql-to-excel

This workflow exports a local CSV file to a JSON file.

This workflow generates CSV files containing a list of 10 random users with specific characteristics using OpenAI's GPT-4 model. It then splits this data into batches, converts it to CSV format, and saves it to disk for further use. The execution of the workflow begins from here when triggered manually. "OpenAI" Node. This uses the OpenAI API to generate random user data. The input to the OpenAI API is a fixed string, which asks for a list of 10 random users with some specific attributes. The attributes include a name and surname starting with the same letter, a subscription status, and a subscription date (if they are subscribed). There is also a short example of the JSON object structure. This technique is called one-shot prompting. "Split In Batches" Node. This node is used to handle the OpenAI responses one by one. "Parse JSON" Node. This node converts the content of the message received from the OpenAI node (which is in string format) into a JSON object. "Make JSON Table" Node. This node is used to convert the JSON data into a tabular format, which is easier to handle for further data processing. "Convert to CSV" Node. This node converts the table format data received from the "Make JSON Table" node into CSV format and assigns a file name. "Save to Disk" Node. This node is used to save the CSV generated in the previous node to disk in the ".n8n" directory. The workflow is designed in a circular manner. So, after saving the file to disk, it goes back to the "Split In Batches" node to process the OpenAI output, until all batches are processed.

This workflow uses AI to analyze customer sentiment from product feedback. If the sentiment is negative, AI will determine whether offering a coupon could improve the customer experience. Upon completing the sentiment analysis, the workflow creates a personalized email templates. This solution streamlines the process of engaging with customers post-purchase, particularly when addressing dissatisfaction, and ensures that outreach is both personalized and automated. This workflow won the 1st place in our last AI contest. Note that to use this template, you need to be on n8n version 1.19.4 or later.

This workflow imports multiple CSV files and appends or updates them to a Google Sheets document. Here's a step-by-step breakdown: When clicked "Execute Workflow", the process starts. The "Read Binary Files" node reads all the '.csv' files from the specified directory. The files are then split into batches (one file in a batch) by the "Split In Batches" node. For each file, the "Read CSV" node reads the data from the CSV file. The "Assign source file name" node assigns the source file name to the data. The data is then processed by the "Remove duplicates" node. This removes any duplicate entries based on the 'user_name' field. The "Keep only subscribers" node filters the data to keep only those entries where the 'subscribed' field is set to 'TRUE'. The data is then sorted by the 'date_subscribed' field using the "Sort by date" node. Finally, the processed data is appended or updated to a specified Google Sheets document using the "Upload to spreadsheet" node. It checks for the 'user_name' field, if the data corresponding to that 'user_name' already exists, it updates the data, otherwise appends the new data.

This workflow imports a JSON file from an email attachment and saves the content into a spreadsheet.

Send a simple JSON array via HTTP POST and get an Excel file. The default filename is Export.xlsx. By adding the (optional) request ?filename=xyz you can specify the filename. NOTE: do not forget to change the webhook path!

This workflow will help guide you through obtaining a spreadsheet file, reading it, making a change then saving it to local or cloud storage.

This workflow illustrates how to convert data from JSON to binary format and import JSON data or files into Google Sheets or local spreadsheets.