Data Warehousing my Finances

Mon Apr 18 2022

Living in Australia, I have access to the Up Banking app. Apart from having a nice user experience, it also has a Developer API so I can query all my Account and Transaction data.

A sweet user experience is nice for day-to-day payments. It's not so great analysing hundreds of transactions over many months.

Luckily, their API allows me to sync my transactions into whatever data store I want. I could even sync the data into a SQL DB and build reports (or APIs) off that!

Tech Choices

To keep the scope of the project manageable I decided to focus a single use-case - "I want to view my Up Transactions in a Spreadsheet-like tool". Airtable was the Spreadsheet-like tool of choice because of its simple API to read and write records.

That's the data store sorted ✅

For compute I went with Cloudflare Workers. Again, I've used it in the past and it's very simple to get up an running. Most importantly, its free! (At least for my usage requirements).

More mature Cloud compute products like Azure Functions or AWS Lambda are definitely viable and are more feature-ful compared to Cloudflare Workers. Azure Functions provides telemetry with App Insights with little configuration for example, and environment secrets with Azure Key Vault. However, this needs an Azure Subscription (which has a monthly fixed cost), and each service costs $X a month. For all these reasons, I went with basic out-of-the-box Cloudflare Workers.

Cloudflare Workers

Massively simplifying - the Cloudflare Workers architecture runs JavaScript in a similar way to how your browser runs JS. Individual workers have no knowledge of other Workers, and have no way of sharing memory - similar to invididual tabs/windows with separate websites in a browser. So as long as you're using only the allowed runtime APIs, you can write your Cloudflare Worker app in any language that you can transpile to JS.

TypeScript seemed like the best choice given high likelihood that any external packages I require would come with typings. Ideally I'd go with a functional language that can be transpiled to JS - i.e. FSharp. But given subtle differences in the runtime's Fetch API, TypeScript with official Cloudflare Worker typings was a safer bet.

The Cloudflare Worker architecture is really interesting and I highly recommend reading it yourself. The Security model is especially interesting, and goes into great depth about a type of exploit known as a Spectre attack 👻.

Project Solution

The final source can be found here:

The main worker handler will effectively;

  1. Query last (100 - e) Up Transactions, where e is some small number to ensure already synced records fetched in point 2 fully overlaps this query
  2. Query last 100 Airtable records
  3. Find unsynced Up Transactions
  4. Create these unsynced transactions in Airtable

Every hour.

Values of 100 transactions being queried come from page size limitations. In practice, 100 transactions covers about a month of transactions and is actually massively redundant. I could get away with a count of 25. However, by going with 100 transactions, the chance of a missed transaction is very very low. It's also naturally tolerant against cases where the Up API or Airtable API is down for a prolonged period.

There is no additional state to keep a record of all synced transactions, the state IS Airtable. The worker handler is stateless, so if it fails at any point the worst outcome is it fails to sync rows into Airtable. Making it stateful increases the chance of the system getting into an inconsistent state, like records in Airtable not matching records in the internal state.

Making it stateless was an intentional decision for the scope of the transactions syncing task. If the scope was different, a state-ful approach may have been more viable.


The Airtable API does not provide means to guarantee idempotency.

Two concurrent runs can result in duplicate rows. There's no straight forward way to combat this with the current setup. Given that it can only happen if there is a bug with Cloudflare worker scheduling, or its run manually, it's not a huge risk. There is a way of detecting duplicate rows in Airtable, so on the off chance it does happen, it can be dealt with appropriately 💀.

No telemetry

Logs from completed Cloudflare Worker runs are not observable. Only currently running workers can be observed. There is very simple manual "logging" of the final result to Cloudflare KV, mainly for observality during development. Cloudflare Workers during development actually still run on the edge, and not your local machine, so they can't be debugged locally. Console logs are streamed to your localhost, so at least that's available.

If runs fail for whatever reason, the stateless nature of the worker helps to safely try to "debug" the issue locally without corrupting any internal state.