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Datasets quick fixes + SQLAlchemy addition + CFF updates #339

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merged 4 commits into from
Sep 27, 2024
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Datasets quick fixes + SQLAlchemy addition + CFF updates #339

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a59423d
datasets: fix layout issue + add page maintainers
egpbos Sep 25, 2024
a6ca254
datasets: added sqlalchemy mention
egpbos Sep 25, 2024
fdeca94
datasets: add authors to cff file
egpbos Sep 25, 2024
c830fa6
update rest of cff file with past 5 years of contributors
egpbos Sep 25, 2024
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43 changes: 43 additions & 0 deletions CITATION.cff
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Expand Up @@ -116,6 +116,49 @@ authors:
name-particle: van
given-names: Sander
orcid: "https://orcid.org/0000-0001-6159-041X"
- affiliation: "Journal of Open Source Software"
family-names: Niemeyer
given-names: Kyle
- affiliation: "Netherlands eScience Center"
family-names: Wehner
given-names: Jens
- affiliation: "Netherlands eScience Center"
family-names: Burg
name-particle: van der
given-names: Sven
- affiliation: "Netherlands eScience Center"
family-names: Siqueira
given-names: Abel
- affiliation: "Netherlands eScience Center"
family-names: Vreede
given-names: Barbara
- affiliation: "Netherlands eScience Center"
family-names: Schnober
given-names: Carsten
- affiliation: "Netherlands eScience Center"
family-names: Chandramouli
given-names: Pranav
- affiliation: "Utrecht University"
family-names: Oberman
given-names: Hanne
- affiliation: "Netherlands eScience Center"
family-names: Lüken
given-names: Malte
- affiliation: "Netherlands eScience Center"
family-names: Isazi
given-names: Alessio
- affiliation: "Datadog, Inc."
family-names: Lev
given-names: Ofek
- affiliation: "Netherlands eScience Center"
family-names: Cahen
given-names: Ewan
- affiliation: "Netherlands eScience Center"
family-names: Ali
given-names: Suvayu
- affiliation: "Netherlands eScience Center"
family-names: Hafner
given-names: Flavio
- affiliation: "Netherlands eScience Center"
family-names: Cushing
given-names: Reggie
15 changes: 13 additions & 2 deletions best_practices/datasets.md
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@@ -1,5 +1,7 @@
# Working with tabular data

*Page maintainers: Suvayu Ali* [@suvayu](https://github.com/suvayu) *, Flavio Hafner* [@f-hafner](https://github.com/f-hafner) *and Reggie Cushing* [@recap](https://github.com/recap)

There are several solutions available to you as an RSE, with their own pros and cons. You should evaluate which one works best for your project, and project partners, and pick one. Sometimes it might be, that you need to combine two different types of technologies. Here are some examples from our experience.

You will encounter datasets in various file formats like:
Expand Down Expand Up @@ -40,14 +42,23 @@ SQLite is a transactional database, so if you have a dataset that is changing wi
- For both DuckDB and SQLite, unique indexes allow to ensure data integrity
- For SQLite, indexes are crucial to improve the performance of queries. However, having more indexes makes writing new records to the database slower. So it's again a trade-off between query and write speed.

# Useful libraries

## Database APIs

- [SQLAlchemy](https://www.sqlalchemy.org/)
- In Python, interfacing to SQL databases like SQLite, MySQL or PostgreSQL is often done using [SQLAlchemy](https://www.sqlalchemy.org/), which is an Object Relational Mapper (ORM) that allows you to map tables to Python classes. Note that you still need to use a lot of manual SQL outside of Python to manage the database. However, SQLAlchemy allows you to use the data in a Pythonic way once you have the database layout figured out.

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I was thinking maybe piccolo deserves a mention here, considering it's much simpler, and probably better fit for smaller projects.

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Please, by all means, add it. I am not at all an expert on databases, this is just a thing I happen to have used once.

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Or perhaps we can do that in another PR and I'll just make an issue about it first to remember (maybe others have additional ideas that they can discuss there).

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Done #346. Thanks for the review!

## Data processing libraries on a single machine
- Pandas
- The standard tool for working with dataframes, and widely used in analytics or machine learning workflows. Note however how Pandas uses memory, because certain APIs create copies, while others do not. So if you are chaining multiple operations, it is preferable to use APIs that avoid copies.
- Vaex
- Vaex is an alternative that focuses on out-of-core processing (larger than memory), and has some lazy evaluation capabilities.
- Vaex is an alternative that focuses on out-of-core processing (larger than memory), and has some lazy evaluation capabilities.
- Polars
- An alternative to Pandas (started in 2020), which is primarily written in Rust. Compared to pandas, it is multi-threaded and does lazy evaluation with query optimisation, so much more performant. However since it is newer, documentation is not as complete. It also allows you to write your own custom extensions in Rust.
DataFusion is a very fast, extensible query engine for building high-quality data-centric systems in [Rust](http://rustlang.org/), using the [Apache Arrow](https://arrow.apache.org/) in-memory format. DataFusion offers SQL and Dataframe APIs, excellent [performance](https://benchmark.clickhouse.com/), built-in support for CSV, Parquet, JSON, and Avro, extensive customization, and a great community. More info [Apache Datafusion](https://datafusion.apache.org/)
- [Apache Datafusion](https://datafusion.apache.org/)
- A very fast, extensible query engine for building high-quality data-centric systems in [Rust](http://rustlang.org/), using the [Apache Arrow](https://arrow.apache.org/) in-memory format. DataFusion offers SQL and Dataframe APIs, excellent [performance](https://benchmark.clickhouse.com/), built-in support for CSV, Parquet, JSON, and Avro, extensive customization, and a great community.

## Distributed/multi-node data processing libraries
- Dask
- `dask.dataframe` and `dask.array` provides the same API as pandas and numpy respectively, making it easy to switch.
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