Raw data doesn't win model competitions. Features do. And when your raw data is tens of billions of rows sitting across multiple sources, you can't afford to run pandas in a notebook and call it a day.
In this tutorial, I'll walk through building a production-grade feature engineering pipeline on Azure Databricks using:
The work that happens while your app is not in the foreground has always been the fiddly part of mobile development, and Codename One's coverage of it had gaps. PR #5142 modernizes local notifications, push, background execution, and shared content across the core, JavaSE, Android, and iOS, and importantly, it makes all of it work in the simulator so you can iterate without a device.
Background Work With Constraints
The new com.codename1.background package schedules work that the OS runs when its conditions are met, mapping to Android JobScheduler and iOS BGTaskScheduler underneath. You describe what the work needs, not when to poll:
Every business runs on a database, but not everyone who needs an answer from the database speaks SQL. Data Analysts wait on engineers, and stakeholders wait on analysts, and by the time the query runs, the decision window has passed.
LangChain's SQL integration fixes this, translating plain English questions like "Which product category had the highest revenue last year' into valid SQL, executing it, and returning a human-readable answer.
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