nebula VS dtale

we use our in-house baked engine - open sourced here https://github.com/varchar-io/nebula

Yeah, Tinybird has lots of similarities, I will do more research on it, thanks for the reference.

Take a look at this open source project - it may be helpful - https://github.com/varchar-io/nebula

cool - I also hand crafted a CSV parser following RFC4180 a while ago, not sure if you have a repeatable way to benchmark the performance difference?

https://github.com/varchar-io/nebula/blob/master/src/storage...

- if you look for hosting solution by yourself, bringing up a nebula cluster (even a single node) is simple, check out https://github.com/varchar-io/nebula

Many modern data systems are designed in a similar way - extract, index, and query in including many low-latency real-time analytical systems, such as clickhouse, druid, pinot, nebula. Take nebula (https://github.com/varchar-io/nebula) as an example - it connects real-time storage engines like Kafka, cloud storage, or pubsub systems, extracts and index data from message queue into its own distributed system, providing low latency query on top of it for business use cases.

[D-Tale](https://github.com/man-group/dtale) has recently released version 3.2.0 on pypi & conda-forge: ``` pip install -U dtale conda install dtale -c conda-forge ``` But if you want to take it one step further you can now integrate it with [ArcticDB](https://github.com/man-group/ArcticDB): ``` pip install -U dtale[arcticdb] ``` This allows you the ability to navigate your libraries of datasets saved to your ArcticDB database! But the best part is that all the reads are occuring directly against ArcticDB so some of the memory constraints you may have been hit with before are now a thing of the past. Here's a full write up how to use this functionality along with a quick demo: https://github.com/man-group/dtale/blob/master/docs/arcticdb/ARCTICDB\_INTEGRATION.md Hope this helps & please support open-source by throwing your star on the [repo](https://github.com/man-group/dtale). Thanks! 🙏

I've looked into external tooling, libs such as dtale, which feel overly complicated for my use case (but I'm open to alternatives). What I would like to have instead is something akin to Spyder's variable viewer, which allows sorting by column. VSCode goes a step further and also provides the ability to filter the dataframe.

D-Tale: A Python library that provides an interactive web-based interface for data exploration and analysis.

Try this: https://github.com/man-group/dtale

https://github.com/man-group/dtale

I find that I'm actually a lot faster using basic Pandas methods to get the data I want in exactly the form I want it.

If I really want to show everything, I just use:

'''

Just added "Time Series Analysis" in v1.60.1 of D-Tale on pypi & conda-forge: pip install -U dtale conda install dtale -c conda-forge This feature provides a quick and easy way to visualize the usage of the following time series analysis function in statsmodels:

Your data is stored in memory so the size of your dataframe is limited to the memory of your machine. That being said we’ve allowed users to swap out the machanism which stores the data so you can use something like Redis or Shelve to allieviate memory. Here’s some documentation: https://github.com/man-group/dtale/blob/master/docs/GLOBAL_STATE.md