A clever repurposing of technologies but realistically only worthwhile for static datasets with range payloads small enough to fit into a web response.
your pipeline has to rebuild each customer’s file fast enough to meet the update cadence. ... data that updates on a coarse schedule rather than in realtime
It doesn't have to all live in the same Parquet file. you can have a Parquet file for all your historical data, plus one for the current week which is updated often cheaply, and then when the week is over you merge that into your big parquet file.
You're making it seem like there's hard limits to what can be done but while there definitely is, you can do incredible stuff.
Most reporting I've ever worked on is based on live data and users expect updates. Range requests over parquet cubes are a cool party trick, but you outgrow it quickly once you need to start regularly updating the dataset such as to avoid full recompuation.
The next step in this journey is Iceberg (and a proper incremental pipeline), which can also be read directly in the browser via WASM either via DuckDB or without. This is from the same author as the parquet library mentioned in the OP https://github.com/hyparam/icebird
I think it depends on (1) customer expectations for freshness and (2) scale (both for the cubes and for the customer data in toto). There are many types of customer facing dashboards where giving "live data" is a bad idea for them and for you. And recomputation is indeed a problem, but if the volume of data isn't that high to begin with, it's probably easier than setting up an incremental pipeline architecture vs. a grouping set query in DuckDB. But I am not really a data engineer, so perhaps this is naive.
I work at a household name tech company and pretty much all our "data" dashboards are running SQL queries against a Hive/Presto environment that's at least 24 hours behind. Real-time dashboards are limited to the service metrics time series database (so combinations of categorical variables, no individual records) or rare expensive Pinot tables.
I have seen this on the business side too. A lot of we need live data requests don’t really need live data. If the decision is made once a day, hourly data is more than enough. Yet we still build dashboards that refresh every few minutes just because people asked for it.
In this case, we do actually need live data! We use wide events for monitoring business events and it lets us understand our platform with utmost flexibility, which is important for us as a fintech company.
Traditional observability is ill-suited for observability around business events. What if you forget to instrument a counter or gauge for something? In my experience it's far easier to log wide events with as much context as possible instead of agonizing over anticipating the dimensionality of metrics upfront (you're going to miss something).
Yes this was also my experience working at a large tech co. I work in fintech now and data volumes are low enough to maintain 2-3 minute up to a few hour data freshness.
Comments
A clever repurposing of technologies but realistically only worthwhile for static datasets with range payloads small enough to fit into a web response.
It doesn't have to all live in the same Parquet file. you can have a Parquet file for all your historical data, plus one for the current week which is updated often cheaply, and then when the week is over you merge that into your big parquet file.
You're making it seem like there's hard limits to what can be done but while there definitely is, you can do incredible stuff.
And then you eventually just take the weekly files and combine them and you've reinvented data lakes with worse (no) metadata management
"static datasets with range payloads small enough to fit into a web response" fits a lot of workloads.
I expect that if your overall data is less than a GB this trick will work really well for you.
Most reporting I've ever worked on is based on live data and users expect updates. Range requests over parquet cubes are a cool party trick, but you outgrow it quickly once you need to start regularly updating the dataset such as to avoid full recompuation.
The next step in this journey is Iceberg (and a proper incremental pipeline), which can also be read directly in the browser via WASM either via DuckDB or without. This is from the same author as the parquet library mentioned in the OP https://github.com/hyparam/icebird
I think it depends on (1) customer expectations for freshness and (2) scale (both for the cubes and for the customer data in toto). There are many types of customer facing dashboards where giving "live data" is a bad idea for them and for you. And recomputation is indeed a problem, but if the volume of data isn't that high to begin with, it's probably easier than setting up an incremental pipeline architecture vs. a grouping set query in DuckDB. But I am not really a data engineer, so perhaps this is naive.
I work at a household name tech company and pretty much all our "data" dashboards are running SQL queries against a Hive/Presto environment that's at least 24 hours behind. Real-time dashboards are limited to the service metrics time series database (so combinations of categorical variables, no individual records) or rare expensive Pinot tables.
I have seen this on the business side too. A lot of we need live data requests don’t really need live data. If the decision is made once a day, hourly data is more than enough. Yet we still build dashboards that refresh every few minutes just because people asked for it.
In this case, we do actually need live data! We use wide events for monitoring business events and it lets us understand our platform with utmost flexibility, which is important for us as a fintech company.
Traditional observability is ill-suited for observability around business events. What if you forget to instrument a counter or gauge for something? In my experience it's far easier to log wide events with as much context as possible instead of agonizing over anticipating the dimensionality of metrics upfront (you're going to miss something).
Perhaps changing is a better word.
Yes this was also my experience working at a large tech co. I work in fintech now and data volumes are low enough to maintain 2-3 minute up to a few hour data freshness.