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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Ive found polars quite intuitive, though for python, I lean more towards [ibis](https://ibis-project.org/). The interface is nearly identical, but ibis has the benefit if building sql queries before pulling any actual data (like dbplyr) — whereas polars requires the data to be in-memory (at least for rdb’s, though correct me if Im wrong)
this to me seems like a good argument for only using ibis, but Im happy to be convinced otherwise
It's got type annotations and mypy has a discussion about it here as well: https://github.com/python/mypy/issues/1282
There is something I don't get about the Polars DataFrame API.
https://docs.pola.rs/user-guide/migration/spark/
Look at the examples on this page of the Spark vs. Polars DataFrame APIs. (Disclaimer: I contributed this documentation. [1])
Having used SQL and Spark DataFrames heavily, but not Polars (or Pandas, for that matter), my impression is that Spark's DataFrame is analogous to SQL tables, whereas Polars's DataFrame is something a bit different, perhaps something closer to a matrix.
I'm not sure how else to explain these kinds of operations you can perform in Polars that just seem really weird coming from relational databases. I assume they are useful for something, but I'm not sure what. Perhaps machine learning?
[1]: https://github.com/pola-rs/polars-book/pull/113