ETL

Example: Informatica, created in 1993.

What is ETL?

ETL = Extract, Transform, Load

A fundamental data management process that has existed since the 1970s.

It is the backbone of how companies move and process data between systems.

Think of it as a data assembly line - taking raw materials (the data), processing them, and delivering finished products where they are needed.

E for Extract

Extracting data from various sources - databases, files, APIs, websites, or legacy systems.

Concrete examples:

T for Transform

Making Data Usable

Data is heterogeneous, in different formats, captured in different ways, so it is necessary to transform it into a coherent format to make it usable.

Concrete examples:

The goal is to have a quality dataset.

L for Load

Taking the cleaned and transformed data and placing it where it needs to go.

Concrete examples:

The Visual Workflow Concept

The fundamental approach (from the 1990s to today):

All ETL tools share the same basic design:

This hasn't changed in 30 years.

What has changed is what they are optimized for.

Classic ETL Tools

Traditional tools (1990s-2010s):

Key characteristics:

Modern Automation Platforms

New generation (2010s to today):

Key characteristics:

Same visual paradigm, different optimization targets.

Same promise of ease and no-code.

What Actually Changed?

Not the interface - the underlying assumptions:

Traditional ETLModern Automation
Batch: Process millions of rows overnightEvent: React to individual triggers in real-time
Database connections, file systemsREST APIs, webhooks
Complex transformations with SQL-like logicSimple field mappings with light processing
Data warehouses and reportingSaaS tool integration
Scheduled jobsEvent-driven workflows
Thousands to millions of rows per runOne to hundreds of records per event

The visual workflow paradigm stayed the same - the data world around it changed.

Pricing & Access Models

Traditional ETL:

Modern Automation:

Use Cases: Yesterday and Today

Traditional ETL:

Modern Automation:

Different data volumes and processing patterns.

Pain Points

From an individual perspective

For production deployment

Visual workflow tools have always promised "ease" but complexity emerges at scale.

This is true whether you're using Informatica or n8n.

The promise of ease with no-code runs into

  • The learning curve is too steep for one-off use.
  • You need dedicated support
  • It works well for simple use cases already implemented. As soon as you step off the beaten path, complexity and time investment explode

With Pieces of AI

If you add AI nodes to the workflow

But

AI helps

Focus on n8n

Useful

Examples:

[
  {
    "json": {
      "name": "John",
      "email": "[email protected]"
    }
  },
  {
    "json": {
      "name": "Jane",
      "email": "[email protected]"
    }
  }
]

Examples:

This makes workflows dynamic.

Introduction Tutorial to n8n

Hosted version

This gives a good idea of the possibilities and difficulties

https://docs.n8n.io/try-it-out/tutorial-first-workflow/

Example Workflow

Project progress tracking

Daily Schedule Trigger
    ↓
Google Calendar (get today's events)
    ↓
Notion (get active tasks per project)
    ↓
Gmail (get unread emails per project)
    ↓
Code Node (structure data)
    ↓
OpenAI/Claude (analyze and summarize)
    ↓
Send summary via Slack/Email
    ↓
OR update a Notion dashboard page

Multiple connections

Other n8n-like Tools

Make, Zapier, Airtable

MCP: Model Context Protocol

An open standard from Anthropic for connecting Claude (and other AIs) directly to tools and data.

The current problem:

The MCP promise:

=> Possibility to create your own connectors on proprietary databases

But

AI Platform - All in one

ToolBest ForKey StrengthTrade-Off / Limitation
Reclaim.aiCalendar & scheduling + focus timeSmart scheduling & meeting/break managementLess full project/task workflow compared to some others
TaskadeFull workflow + collaboration + AIFlexible views + automation + AI agentsMight have steeper learning curve for scheduling model
ClickUpAll-in-one project/task managementWide feature set for teams/tasks/projectsScheduling automation may not be as deep as Motion's AI
SunsamaDaily planning + time-blockingSimple, mindful daily workflowLess automation, more manual setup & planning
ProofHubTeam collaboration & project workflowsChat, tasks, shared workspacesLess emphasis on AI scheduling automation

Alternatives to n8n - Project Progress Tracking

Connect ChatGPT to External Services: Gmail, Notion, ...

Level of integration is very disparate.

Connecting ChatGPT to Gmail

Connecting ChatGPT to Notion

Not available as a connection on the global GPT

Potentially available on Pro and higher accounts via MCP and only on the ChatGPT application. Not on the web. Requires recent Mac

Other alternative: developer mode. But it doesn't remember chats.

=> Surely doable but depends on the subscription and machine and especially on the rollout of features.

Connect Claude.ai to External Services: Gmail, Notion, ...

The list of connectors looks promising.

And much more

Each connector has its own list of features

Claude + Gmail

The first connection didn't work. Although the settings showed Gmail was connected, Claude didn't have access

=> Disconnect + reconnect

This time received a "security alert" from Gmail indicating that Claude had access

But still no access in the Claude app

I verified that Google had properly authorized Claude to access Gmail in my Google account settings

And then I clicked on settings in the chat and saw that we could also authorize Gmail in the chat

And finally it works!

Claude + Notion

Access is enabled without issue

Claude seems to be the simplest solution in the current state of things

Alternatives

1. Notion AI (Built-in)

If he's already using Notion for project tracking:

2. Motion / Reclaim.ai

Purpose-built for freelancers/solo operators:

3. Custom GPT / ChatGPT with Plugins

4. Zapier Tables + AI

Zapier's native database with built-in AI:

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