Here are the slides and materials that I promised would be here in the talk that you watched.
Many (if not most) companies reach a point when data becomes a priority. This implies building out an internal practice to integrate into existing systems and processes to deliver the sought insights. In a field so wide, relatively recent and infamous for its buzzwords-per-second count, formalising problems and making explainable decisions is the only route that won’t see you run out of resources and people’s patience.
This talk explores how we approached this challenge at mx51 armed with lessons from engineering and statistics. We start by defining the optimisation problem formally(-ish) and then applying it to actual decisions faced along the way, including technology selection, warehousing and data lake (both Snowflake), ETL, visualisation and ML-driven insights.
Metabase, a brilliant open-source BI tool, allows you to define your data model on top of the database schema, which includes descriptions, special types and table relationships. However, updating everything manually is not repeatable and prone to errors, so I will demonstrate how to export your existing dbt schema into Metabase automatically with a tool I created.
Kotlin continues to conquer new areas of software development, but some are still firmly held by one or two languages. Data science, statistical analysis and machine learning are largely Python domains, but with many performance-focused implementations based on the JVM, Kotlin has a good chance to break into this scene too. The intention of this talk is to give a shallow overview of what you can do today.
Summary of the better talks, the more interesting themes, some conversations with other Kotliners and overall impressions about the first ever Kotlin conference in San Francisco. Think of it as your guide to what recorded talks to watch first.
Kotlin provides a mountain of features that Java developers previously never had access to. This creates endless opportunities. This also creates confusion akin to that of a kid in a candy store, which is exacerbated by the transition from simple beginner demos to production code, expected to be readable, performant and maintainable. Which to choose? Should I be doing this?
Kotlin, a relatively new programming language running on the JVM, has been making headlines as the more elegant alternative to Java in any of its applications. One of them is Android development and this talk will explore the benefits that the language offers for everyday coding.
Looking at web development, Spring seems to be the star of most Kotlin articles and tutorials. This talk will explore an asynchronous alternative that is Vert.x and evaluate its production readiness. We’ll do that by writing a simple (but working) web API, with some neat Kotlin features to boot, and figure out what the benefits of using this framework are.
Kotlin has finally graduated to a release version and we have rewritten one of our production apps in the language. Now armed with some real-life perspective, the time has come to reveal whether it was worthwhile or the conservatives who stuck with the "officially supported" Java were right all along.
It was only a matter of time before version 2.0 of the well-known dependency injection library Dagger hit production and that marks a good time to discuss the changes in the new version and how they can benefit Android developers.
Now that Apple released Swift for iOS, the need for a more modern language for Android development has become even more apparent and although there are no official alternatives available from Google, there is viable option from JetBrains — Kotlin, a modern JVM-based language fully integrated into Android Studio and easily usable with the Android SDK.