influxdb_iox VS faunadb-js

Watch out for the AGPL minio <https://github.com/metrico/iox-community/blob/155a14bb5e8e32...> the almost certainly AGPL grafana <https://github.com/grafana/grafana/blob/v10.1.1/LICENSE> and always eye anyone who uses :latest images with healthy suspicion

That said, influx_iox itself appears to be Apache 2 (and/or MIT?) https://github.com/influxdata/influxdb_iox/blob/main/LICENSE...

have you looked at https://github.com/influxdata/influxdb_iox ? that's where the development for the new version is done.

As I understand, InfluxDB 3 is just a re-branding of InfluxDB IOx. Then its' performance can be not very good comparing to Prometheus-like systems.

woah

InfluxDB iox

Checked the pulse and seems like a dead project when you look at the actual commits. Nothin but config files and grpc. https://github.com/influxdata/influxdb_iox/pulse/monthly

Just want to say congratulations to the team!

2 years and 9,500+ commits is a hell of a feat.

https://github.com/influxdata/influxdb_iox

Already is: https://github.com/influxdata/influxdb_iox Just still a work in progress.

It looks like this crate is the workaround for that. But there's a PR on SQLX opened a couple days ago that will fix the issue.

Fauna is another serverless database created by ex-Twitter engineers. It's kind of like MongoDB, but with native JOIN operations, many document databases miss. They have their own language, FQL, and also a GraphQL API. Here's a quick overview of their free tier:

NoSQL is a term that we have become very familiar with in recent times and it is used to describe a set of databases that don't make use of SQL when writing & composing queries. There are loads of different types of NoSQL databases ranging from key-value databases like the Reddis to document-oriented databases like MongoDB and Firestore to graph databases like Neo4J to multi-paradigm databases like FaunaDB and Cassandra.

Fauna

Fauna – A serverless database with built in global replication and strong consistency.

Some of the largest websites, driven by the requirements of serving billions of users, have led to the maturation of new database architecture alternatives, which motivate modernizing applications to leverage cloud-native, serverless, highly-distributed database options. Fauna strengthens relational data consistency among these newer alternatives while leveraging a cloud-native, serverless, flexible document model that abstracts most operational complexities from application developers.

Multi-model: FaunaDB, MongoDB, Redis among others.

Head over to fauna.com and create a new account if you haven’t done already. Next create a new database.

FaunaDB

Great question! With Seafowl, the idea is different from what the modern data stack addresses. It's trying to simplify public-facing Web-based visualizations: apps that need to run analytical queries on large datasets and can be accessed by users all around the world. This is why we made the query API easily cacheable by CDNs and Seafowl itself easy to deploy at the edge, e.g. with Fly.io.

It's a fairly different use case from DuckDB (query execution for Web applications vs fast embedded analytical database for notebooks) and the rest of the modern data stack (which mostly is about analytics internal to a company). Just to clarify, we're not related to IOx directly (only via us both using Apache DataFusion).

If we had to place Seafowl _inside_ of the modern data stack, it'd be mostly a warehouse, but one that is optimized for being queried from the Internet, rather than by a limited set of internal users. Or, a potential use case could be extracting internal data from your warehouse to Seafowl in order to build public applications that use it.

We don't currently ship a Web front-end and so can't serve as a replacement to Superset: it's exposed to the developer as an HTTP API that can be queried directly from the end user's Web browser. But we have some ideas around a frontend component: some kind of a middleware, where the Web app can pre-declare the queries it will need to run at build time and we can compute some pre-aggregations to speed those up at runtime. Currently we recommend querying it with Observable [0] for an end-to-end query + visualization experience (or use a different viz library like d3/Vega).

Re: the second question about Splitgraph for a data lake, the intention behind Splitgraph is to orchestrate all those tools and there the use case is indeed the modern data stack in a box. It's kind of similar to dbt Labs's Sinter [1] which was supposed to be the end-to-end data platform before they focused on dbt and dbt Cloud instead: being able to run Airbyte ingestion, dbt transformations, be a data warehouse (using PostgreSQL and a columnar store extension), let users organize and discover data at the same time. There's a lot of baggage in Splitgraph though, as we moved through a few iterations of the product (first Git/Docker for data, then a platform for the modern data stack). Currently we're thinking about how to best integrate Splitgraph and Seafowl in order to build a managed pay-as-you-go Seafowl, kind of like Fauna [2] for analytics.

Hope this helps!

[0] https://observablehq.com/@seafowl/interactive-visualization-...

[1] https://www.getdbt.com/blog/whats-in-a-name/

[2] https://fauna.com/