steampipe VS metriql

Steampipe is a tool for querying cloud APIs and other data sources using SQL in a zero-ETL manner.

Readers may also enjoy Steampipe [1], an open source CLI to live query Google Sheets [2] and 140+ other services with SQL (e.g. AWS, GitHub, etc). It uses Postgres Foreign Data Wrappers under the hood and supports joins etc across the services. (Disclaimer - I'm a lead on the project.)

1 - https://github.com/turbot/steampipe

be mindful of its AGPLv3 https://github.com/turbot/steampipe/blob/v0.21.8/LICENSE (AFAIK v0.4.3 is the last MIT release https://github.com/turbot/steampipe/blob/v0.4.3/LICENSE ) and the actual providers are Apache 2 <https://github.com/turbot/steampipe-plugin-aws/blob/v0.131.0...> (but I don't know if provider drift makes them compatible with 0.4 or not)

iasql seems to be AWS only, but good for them for taking this on:

In a prior post I showed how to install Steampipe in AWS CloudShell to instantly query over 460+ resource types from your AWS APIs using SQL, and another post on how to use the Steampipe AWS Compliance mod to assess over 25+ security benchmarks across your AWS accounts.

Readers may find Steampipe's [1] AWS Thrifty Mod [2] useful. It will automatically scan multiple accounts and regions for 50 cost saving opportunities - many of which are looking for over-provisioned or unused resources. For example, it's crazy how much you can save by doing things like just converting your EBS volumes to the newer gp3 type. Combine with Flowpipe [3] to automate checks and actions. It's all open source and extensible.

1 - https://github.com/turbot/steampipe

Steampipe [1] is an open source project [2] that includes an embedded Postgres to instantly query cloud, code & more with SQL. This release expands our plugin ecosystem [3] to be a full Zero-ETL platform. Steampipe plugins can now run natively in your own Postgres as Foreign Data Wrappers [4], as SQLite extensions [5] or as simple data export tools [6]. Please give it a try, we'd love your feedback and contributions!

1 - https://steampipe.io

Some of the companies that operate in space are Cube Dev; Transform(currently acquired by dbt); metriql. See more companies at https://www.moderndatastack.xyz/companies/metrics-store.

Presto is pretty successful but its focus is to be distributed query engine, not a proxy layer for the existing query engines. We use Trino ( formerly Presto) as our query layer and do something similar to Hydra at Metriql [1] with a fairly different use-case. Data people provide a semantic layer with the mecrics and expose them to 18+ downstream tools.

[1]: https://metriql.com

This is why metrics store tooling have started appearing recently (e.g. TransformData, SuperGrain, Metriql, dbt Metrics) - to solve the problem of this table / metric disorganization across an org's data landscape.

We're using Superset to enable our analysts to explore our clients' SEM/SEO/analytics data. It also posts alerts to Slack when, say, the daily session count of a website isn't what was expected given the historical data.

Yeah, it's a little rough to get going, but once it is, we've found it to be a really powerful (and actively developed!) BI tool. It's even better with dbt + MetriQL [0], which can automatically sync Superset's dataset metadata directly with properties you set up in dbt.

Adding custom visualizations is much harder than it should be, but they're very much aware of that, and working to address it. Their Slack community is super-helpful, too.

[0]: https://metriql.com

As a current Looker power-user, this looks really solid.

One thing I’m not sure about though: can you use the metrics outside of the native tool, and if so how?

That is, I see Looker as a BI tool, not a metrics layer, since you mainly use the metrics you define inside Looker, not in other tools. On the other hand, something like MetriQL[0] is a pure metrics layer that can supposedly be used anywhere.

Is this both? If so, some better documentation around how to use the metrics layer would be helpful (or maybe I just didn’t look in the right place).

[0] https://metriql.com/

3. Define metrics in one place on top of your data models and expose the metrics to all the data tools. (This layer is new, and we're tapping it at https://metriql.com)

We use BSL license and metriql is free with a single database target. If you want to connect multiple dbt projects in a single deployment, you need to go through the sales cycle.

We work with ETL vendors that use metriql to make revenue with our BI tool integrations so we picked BSL license to be able to structure our business model in a way that you should be required to pay only if you're reselling metriql to your customers.

You can find the license here: https://github.com/metriql/metriql