Thanks for asking. For some of the larger companies, takes a little bit of time to work with their marketing / legal teams :)

We do list some of them within the blog post:

- STE Energy, to back operational dashboards used to monitor 47 power plants in Italy, Albania, Colombia, Perú, Honduras, Costa Rica, and Chile, replacing Redis

- InTraffic, which monitors all of the roughly 5,000 remote controllable railroad switches of the Dutch railways, for use with Tableau as a Business Intelligence solution

- One of the largest American telecom companies, for storing server health data

- A top financial services firm, to replace Oracle for powering interactive analytics of market data

- A public lighting and semiconductor company, for deployment in a small-footprint IoT edge device collecting and analyzing sensor readings

Influx is a NoSQL, non-relational database, written from scratch, that optimizes for properties like higher compression and column scans for specific label sets. While it supports something SQL-ish, the query language is fairly limited, and I think they are moving further away to a more declarative, non-SQL language.

On the other hand, TimescaleDB has figured out how to re-engineer PostgreSQL to make it scale for time-series data. In doing so, it:

- Supports full SQL, including complex WHERE predicates, secondary indexes, non-time based aggregates, sub-queries, window functions, CTEs, etc.

- Allows you to store regular relational tables alongside your time-series data and perform arbitrary JOINS.

- Supports both full schema-tized and JSON/JSONB data models.

- Inherits the full ecosystem of Postgres (visualization like Tableau or Grafana, ORMs like Ruby and Django, connectors like Kafka, etc.). If it speaks to Postgres, it speaks to TimescaleDB.

- Also works with other Postgres extensions, including things like postgis for geo-spatial analysis.

- Enjoys the reliability of Postgres, including streaming replication for HA, clustered read replicas, point-in-time recovery, incremental backups, etc.

For more details: http://docs.timescale.com/

Our open source version already supports some forms of scaling/clustering today: e.g., read-only replicas for hot standbys and increasing query throughput. Timescale also supports hundreds of thousands of row inserts (millions of metrics) per second on a single node, and allows users to elastically add disks to scale up single-node capacity to ~100TB.

For scaling beyond those insert and storage limits, we're currently working on full, scale-out clustering. We'll have more to say about that soon.