Migration agent

Let's talk about migration. Every services team in this room has lived the migration nightmare. A customer sends their data. It's messy.

It's in three different formats. Half the fields don't map to your schema. There are duplicates, missing values, formatting inconsistencies, and other surprises. The consultant spends how long?

A week, two weeks just cleaning and transforming the data before the actual migration can begin. And they have to do this every single time. The work is repetitive, but not simple. Every dataset is different enough that you can't automate it with scripts.

It's the tax your team pays on every single deal. Nitro's migration agents handle the full life cycle, data prep, cleaning, schema mapping, transformation, validation, and the incremental loads. What used to take a consultant a week before now becomes an afternoon's work.

So let's take that exact problem, messy customer data, and see how this works with an agent. Every migration follows the same pattern. Extract, transform, load. Almost all the effort is in transformation. A consultant is manually mapping fields across hundreds of columns, writing custom scripts to handle custom logic, figuring out what the data actually means.

That's where migrations really slow down and saps your team's energy.

So we built the migration agent to simplify this for you. You give it the customer's data and just describe what needs to happen. Map this to our schema, handle custom fields this way, apply these rules, validate everything. The agent takes it from there. It maps fields, applies transformations, validates formats, handles lookups, merges newer versions of datasets, and flags anything unclear.

Once it's clean, it is ready to load the data into your system. Let's take a look at how it works.

Is where you build a Nitro Migration Say Chargebee is migrating data from Stripe. Let's say that they're migrating customers, invoices, and subscriptions data. Each of them have their own schema and validation rules.

We upload the schema files. In this case, customer, subscription, and invoices schema. Nitro reads them and extracts every field. Now, we'll move on to schema definition.

Mark which fields are required. Flag pick lists and lock in their allowed values. For subscriptions, maybe the only valid statuses are active and inactive. For account tier, it's mid market, enterprise or SMB.

These provide the guardrails from imperfect data making its way to the final dataset.

Now onto mapping. Nitro handles the obvious mapping fields automatically.

For anything non obvious, we describe the relationship in plain English.

Like in this case, updatedAt fields mapped to the resource update timestamp.

Then transformations, TDS data cleaning rules written in plain English. Convert dates to the other formats or timestamps. Reformat currency codes to three letter ISO format.

Finally, validation rules. We define exactly what data like. Here in this case, invoice IDs must start with an INV underscore followed by a six digit number. The agent is configured. It is ready to run on any customer's data.

Nitro's migration agent lives right inside the project. The consultant opens it from the task and uploads the customer's raw data.

Here, we upload three files containing customers, invoices, subscriptions data, messy real world stuff.

Nitro automatically maps each file to the right schema. It asks us to confirm before moving forward.

Then it maps every field from the three files which is uploaded to our schema. It will work even if there are hundreds of fields. What used to take a consultant days is happening in under two minutes.

We can review the mappings and the options, adjust anything, and then start the migration.

Nitro transforms all three data sets, then validates them. Subscriptions, clean.

Customers and invoices data, issues flagged. Every invalid row is highlighted. The consultant doesn't have to hunt through the data. Nitro already has done the forensics. And here's the part that changes everything.

Instead of going back to a spreadsheet, the consultant just types the fix in plain English.

We can also chat with the data. Populate all missing email IDs with default at chargevi dot com. Nitro handles it right there.

I'm done. Invalid rows fixed in seconds. The data is clean, validated, and ready to load. No spreadsheet gymnastics, just the consultant, Nitro, and the work.