06-04, 16:30–17:10 (Europe/London), Minories
This talk educates the audience on how to create end-to-end data products using the Python data ecosystem, from data integration to reporting, dashboards, apps, and surfacing insights.
After analyzing the features found in popular proprietary analytics products across various verticals, this talk will demonstrate how data teams can use open-source libraries to create and deploy applications which are accessible to non-technical end users but hold distinct advantages over proprietary alternatives.
Companies often adopt paid SaaS platforms or proprietary point solutions for analytics, which are inflexible, expensive, and hard to integrate with internal datasets and platforms.
In addition to providing world-class libraries for data analysis and visualization, Python and the wider open-source ecosystem provide powerful ways to create end-to-end data products. However, the perceived complexity of building an end-to-end internal product from scratch holds companies back from embracing open-source.
This talk will challenge that assumption, and explore a project which uses modern open-source technologies and Python to build an end-to-end analytics product.
Asides from the core analysis in Python, this will cover integration and modelling of data using in-memory databases such as DuckDB, reporting/dashboarding, interactive web apps, and integrations into messaging platforms such as email and Slack.
The audience will be educated on the trade-offs in architecting internal data products vs. buying proprietary platforms from third-party vendors, and the source code will be available as a Jupyter Notebook and GitHub repo.
No previous knowledge expected
