Hello, Arnaldo! thanks for your response, oh I didn't! Okay I'll try. Thank you! 🙂

Newbie here. Can SageMaker be used in Kedro for generic processing (not fitting models)?

Yes - but you need to package the project or use Kedro in IPython mode

we also have docs here! https://kedro.readthedocs.io/en/latest/10_deployment/09_aws_sagemaker.html

also docs on the AWS side https://aws.amazon.com/blogs/opensource/using-kedro-pipelines-to-train-amazon-sagemaker-models/

Hi everyone! We have a workflow that seems to be a little tricky to achieve: Every night, we combine raw data from today and intermediate data from yesterday to produce (the same) intermediate data for today. For this, we use two PartionedDataSet data catalog entries that point to the same path - one so we can access the old data and one so we can then write out the updated data. While it feels hacky it seems to work, however we do have an issue when we run this for the very first time: Since there is no old data present, the PartionedDataSet crashes while loading. We could workaround this by using an IncrementalDataSet, however then we always load all the partitions. This would lead to us loading a years worth of data when we only need a day. We found this issue https://github.com/quantumblacklabs/kedro/issues/394 that seems to be related to what we want to do.

Hello - I think as it stands this isn't possible out of the box. I need to check with the team why we made this a hard design requirement, but I would recommend you subclass PartitionedDataSet and override it for your own purposes.

I want to load data from a database and then save to multiple formats locally (csv, parquet). Is there a way to do this nicely? I tried putting a list of outputs in a

node

but threw an error. I know that transcoding allows the same dataset to be loaded in multiple ways, I want to go the other way around.

so out of the box you need to create two catalog entry 'targets', transcoding is really designed for outputs that share the same filepath i.e. pandas and spark pointing to the same parquet file. There is a 3rd party plugin called Kedro Wings. It's not officially supported but it includes a TeePlugin that I think allows you to do what you want https://github.com/deepyaman/kedro-accelerator

Hello, I am starting to use kedro, and I was wondering if it could help me with some Kaggle challenge. Is there a "Kaggle DataCatalog" where I could get the data to feed a pipeline? I am actually trying to find a way to develop a pipeline which I could run both in my IDE and as Kaggle notebook...

Hello - I’m not aware of any Kathleen integrations with Kedro but it’s a cool idea!

In theory you can run Kedro in ipython mode in most notebooks https://kedro.readthedocs.io/en/stable/11_tools_integration/02_ipython.html#ipython-extension

Thanks for the very quick answer 😄 Was worth asking. I am always struggling to code in the notebooks since I am so used to my IDE. How ever if I copy my code on a notebook, all the data are not located in the same place so I need to update everything, would be awesome to have a way to abstract the Kaggle data to a catalog or sth llike that. However, I understand that it is not a feature every one would use on the job 😆

I will check if it can help!

It’s something for us to think about I think - that being said the Kedro team strongly believe notebooks are for prototyping and communication and the IDE is for pipelines

I don´t like notebook myself, I find them too messy, my colleagues are desperated to see me do everything in script/functions 🤣 . I am still trying to understand how to use Kaggle GPUs, at the moment I have only managed through notebooks :sigh going back trying:

If there are a lot of columns that could be processed separately, e.g. imputation, mapping values, etc, then it would be better to have separate nodes for these right? The upside is modularity (as you mentioned), which also means the nodes could be run in parallel. The downside is that there would be a lot of nodes;

process_col_A

,

process_col_B

, ...,

process_col_ZZ

.

Ah good question - in this case If it’s the same content over and over again I’d look to keep into node for readability or use a loop to create many nodes

Hello- I'm looking to migrate an existing dask data science project into Kedro to help with structuring the code and to help with transparency for non-technical folks- are there any known best practices for this use case?

What are the practical differences between the

ThreadRunner

and

ParallelRunner

. 1. I know that the

ParallelRunner

won't take lambda functions. In this case how to deal with the nodes with identify lambda functions (

lambda x: x

)? 2. I tried running using

ThreadRunner

and it did run the nodes at the same time and shortened the end-to-end runtime. Are there situations in which I shouldn't use

ThreadRunner

?

is it possible to use kedro viz to write to png file without opening the browser interface. I have issues opening the browser url localhost:port in my dev environment. I just would like it to dump to a png file from command or programmatically

How to give a json as parameters instead of parameters.yml

Parallel vs Thread runner

Viz write PNG as CLI command

JSON params instead of YAML

Best practice for Kedrofying an existing project

what is the best approach where you read same tables from two different kind of databases, oracledb , hive etc. All the data prep steps are same except the sql that initially reads into dataframe. how to make this configurable with kedro?

You would likely have to define multiple

pandas.SQLTableDataSet

catalog references with different config

Two things that can help you stop repeating yourself: - https://support.atlassian.com/bitbucket-cloud/docs/yaml-anchors/ - Jinja https://kedro.readthedocs.io/en/latest/04_kedro_project_setup/02_configuration.html#jinja2-support