Our latest articles

Brief overview of how to prep JSON data for ad hoc analysis or exploratory data analysis.

Discover new Fabi.ai features: Excel/JSON support, faster analysis, improved navigation. Learn data crunching tips, performance tricks, and exciting updates for streamlined year-end reporting.

Churn prediction is an important tool to focus limited resources on the areas that matter. In this post we talk about the different possible approaches and the merits of survival analysis.

You can combine SQL and Python for advanced data manipulation in your day-to-day workflows. This post covers four examples of this in practice: ETL processes using dbt and Snowpark, A/B test analysis, customer churn prediction, and building interactive dashboards. We also discuss how combining SQL's data retrieval capabilities with Python's advanced processing and visualization tools can significantly improve efficiency and insights in data-driven tasks, while also introducing Polars as a high-performance alternative to Pandas for handling large datasets.

SQL and Python are arguably the two most foundational pillars for data analysis. If you’re doing any form of exploratory data analysis (EDA) to get insights from your data, you must know how to leverage SQL and Python.

Explore Fabi.ai's July updates: Google Sheets integration, point-and-click charting, Parquet support, and enhanced AI chat. Empowering data-driven stakeholder engagement.