PyData: Data Handling Session List
Apache StreamPipes for Pythonistas: IIoT data handling made easy!
Tim Bossenmaier, Sven Oehler
Data enthusiasts love to play with IIoT data. However, the technical challenges remain high (e.g., connect to devices). @StreamPipes makes this easy by providing a self-service toolbox. In this talk, we introduce a new python module to work with IIoT data in a pythonic way.
Common issues with Time Series data and how to solve them
Vadim Nelidov
Handling time series data is an important yet not an easy task. After this talk you will learn to identify, understand, and resolve time series issues such as divergence, delayed data, time series imputation and impact of outliers.
Most of you don't need Spark. Large-scale data management on a budget with Python
Guillem Borrell
Most of you don't need Spark. Large-scale data management on a budget with Python
Neo4j graph databases for climate policy
Marcus Tedesco
Can Neo4j graph databases and Python help us understand climate policy? Find out!
Polars - make the switch to lightning-fast dataframes
Thomas Bierhance
Want to learn about a new Python library that can speed up your datascience and analytics work? Join us at the conference to hear about polars, a lightning-fast dataframe library based on Apache Arrow and written in Rust!
Postmodern Architecture: The Python Powered Modern Data Stack
John Sandall
Learn how to upgrade your pandas pipelines powering DAG workflows to a Python Powered Modern Data Stack, demystify the jargon from ETL to ELT, and see how tools like dbt can integrate with Python to change how data pipelines are built and maintained.
Pragmatic ways of using Rust in your data project
Christopher Prohm
Pragmatic ways of using Rust in your data project - strategies to speed up your data pipelines without rewriting the whole program.
Raised by Pandas, striving for more: An opinionated introduction to Polars
Nico Kreiling
Have you also been raised with #pandas for all kinds of data transformations and wonder, if there is more? I did, I searched for performance and more concise syntax, and I would like to introduce you to #polars
WALD: A Modern & Sustainable Analytics Stack
Florian Wilhelm
WALD: A modern & sustainable analytics stack consisting of a warehouse like Snowflake or BigQuery, Airbyte, Lightdash and dbt.
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