This article provides a complete, practical walkthrough of the Filter Manual Import Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automating COVID-19 Data Import from CSV to Google Sheets Using n8n
   
   Meta Description:
   Discover how to automate the process of downloading COVID-19 testing data from a public CSV URL and updating a Google Sheet using n8n. This guide walks through filtering, transforming, and streaming region-specific data seamlessly.
   
   Keywords:
   n8n automation, Google Sheets API, import CSV to Google Sheets, COVID-19 data automation, ECDC data workflow, DACH region filter, data transformation, low-code automation, open data ingestion
   
   Third-Party APIs Used:
   
   - Google Sheets API (via n8n’s built-in Google Sheets integration)
   - ECDC COVID-19 CSV Download (Public open data endpoint: https://opendata.ecdc.europa.eu/covid19/testing/csv/data.csv)
   
   Article:
   
   Automating CSV Imports to Google Sheets for COVID-19 DACH Region Analytics with n8n
   
   In the realm of data analytics, automating repetitive tasks like downloading, transforming, and updating databases is key to unlocking productivity. One awesome example of this is a low-code n8n workflow that imports COVID-19 testing data from Europe’s public health authority, filters it for a region, and updates the relevant records into a Google Sheet — all with the click of a button.
   
   This article walks through how this specific n8n workflow is architected, why it’s efficient, and how it leverages public data sources and third-party APIs to produce near real-time insights automatically.
   
   🧩 Workflow Overview
   
   The workflow titled “Import CSV from URL to GoogleSheet” is composed of seven main nodes, executed in sequence when an operator manually triggers the automation. Here’s what it does:
   
   1. Downloads up-to-date COVID-19 testing data from the European Centre for Disease Prevention and Control (ECDC).
   2. Parses the CSV data.
   3. Adds a unique key per entry for easy deduplication during updates.
   4. Filters the data for Germany (DE), Austria (AT), and Switzerland (CH) for the year 2023.
   5. Appends or updates the filtered records in an existing Google Sheet named "COVID-weekly".
   
   Let’s dive deeper into each step.
   
   🔗 Step-by-Step Breakdown
   
   1. Manual Trigger: The workflow begins with a Manual Trigger node titled “When clicking 'Execute Workflow'”. This is useful for data engineers or analysts who prefer to run this task on-demand rather than on a schedule.
   
   2. Download CSV: The next node makes an HTTP request to the ECDC URL (https://opendata.ecdc.europa.eu/covid19/testing/csv/data.csv), which provides weekly COVID-19 testing data for European countries in CSV format. The response is treated as a file so it can be parsed in the next step.
   
   3. Import CSV: The “Import CSV” node parses the downloaded file into structured JSON records using header-based mapping. This allows the data to flow into other nodes seamlessly.
   
   4. Add Unique Field: To prevent duplicate entries when uploading to Google Sheets, we create a unique key for each record by combining country_code and year_week — for example, “DE-2023-15”. This is helpful when new data overlaps with previously uploaded entries.
   
   5. Keep Only DACH in 2023: Using a Filter node, the dataset is pared down to only the countries in the DACH region — Germany (DE), Austria (AT), and Switzerland (CH) — and only records from the year 2023. This minimizes the data volume and stays within Google Sheets API rate limits.
   
   6. Upload to Google Sheet: The refined dataset is appended or updated in a specific Google Sheet via the “Upload to spreadsheet” node, configured to use the OAuth credentials of the authenticated Google Sheets account. The target document is found at:
   https://docs.google.com/spreadsheets/d/13YYuEJ1cDf-t8P2MSTFWnnNHCreQ6Zo8oPSp7WeNnbY.
   
   The unique key created earlier ensures smooth updates rather than simply appending duplicate rows.
   
   7. Sticky Note (Optional Commentary Node): A thoughtful bonus in this workflow is a Sticky Note explaining why batching might be necessary. It reminds users that due to Google’s read/write rate limits, only a subset of data is processed. Users looking to process larger datasets can implement Split In Batches and Wait nodes to mitigate API failures due to quota hit.
   
   🎯 Functional Highlights
   
   - Robust Deduplication: The “unique_key” logic ensures that even if the workflow is executed multiple times, the same data doesn’t get duplicated.
   - Precise Filtering: Filtering data for a specific region and time range keeps the dataset clean and focused.
   - Error Minimization: Thoughtful usage of Google Sheets’ appendOrUpdate operation safeguards against overwrites and lost data.
   - Scalability: Though currently tailored to process a small subset, the workflow is easily extendable to handle large datasets with batching and delay logic.
   
   💡 Use Cases
   
   - Public Health Dashboards: Automate the backend ingestion of data into dashboards like Google Data Studio.
   - Academic Research: Simplify the data retrieval process for epidemiological studies limited by region or time.
   - Newsrooms and Media: Automate raw data collection for daily or weekly COVID trends in Central Europe.
   
   🤝 Third Party Tools
   
   This workflow leverages the following external services:
   
   - Google Sheets API: via n8n’s integration, allows write-and-update operations.
   - ECDC Open Data CSV: the European Centre for Disease Prevention and Control provides access to Europe-wide COVID testing data.
   
   ⚙️ Final Thoughts
   
   This workflow exemplifies the best of what n8n offers—automated data pipelines without needing to write custom code. Whether you're a data analyst, a public health researcher, or a no-code enthusiast, setting up pipelines like this can streamline your work, increase data accuracy, and free up time for analysis rather than collection and cleaning.
   
   Curious to experiment further? Extend this workflow by integrating email alerts, visual dashboards, or even an automated Slack report. The foundation is solid—now it’s time to build smarter.
   
   —
   
   By leveraging public data and a simple yet powerful low-code stack, automation like this sets the stage for greater data accessibility and smarter decision-making.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.