From BigQuery truncate table to dynamic SQL support; we cover 12 user-friendly BigQuery functions released by Google Cloud. — We were excited to see the power of BigQuery receive a further boost this week with the release of 12 new BigQuery SQL features. Google Cloud describes these as “user-friendly SQL capabilities”. So, let’s take a look at what’s now possible. 1. Add table columns via DDL New to BigQuery is the ability to add new…

From SQL Server to Google BigQuery: Learn why I love common table expressions. — This is my 20th year working with SQL (eek!). To mark this milestone, I recently wrote an article on the 10 SQL naming standards I live by, which I feel, produce SQL that is easy to read, debug, and maintain. 10 SQL standards to make your code more readable in 2021 Make a NY resolution for 2021: Easy to read, and maintain SQLtowardsdatascience.com

I explore BigQuery’s new UI, just released in preview — This week marked an exciting week for users of Google BigQuery; a much-anticipated UI update was made available in public preview. Having used the new UI in anger for the past week, I thought I’d share my top 10 likes on the new look. Readers should note that the new…

SQL tuning tips and advice to help reduce BigQuery execution time and costs. Start 2021 off on the right foot! — Google BigQuery, like other modern hyper-scale data platforms, has a different architecture to what many data professionals and data scientists are used to; it stores its data in columns instead of rows (referred to as a column-store), and processes SQL queries in a fully distributed architecture.

Make a NY resolution for 2021: Easy to read, and maintainable SQL — This is my 20th year working with SQL (eek!), and I’ve shared my 10 key learnings to help make sure your SQL code is easy to read, debug, and maintain. The key to success is, of course, to enforce these (or your own standards) across your Enterprise. …

We use a common example: un-nesting Firebase event data to facilitate data science analysis — We frequently apply machine learning techniques to event-based data in order to generate high-value insights; from predicting customer churn using people’s recent activity of say, a subscription-based product, to scoring how engaged a user is on a piece of content for feeding into a recommendation engine.

We train an AutoML image classification model for Kaggle’s latest competition. See how it ranks against human Data Scientists. — Kaggle recently (end Nov 2020) released a new data science competition, centered around identifying deseases on the Cassava plant — a root vegetable widely farmed in Africa. “As the second-largest provider of carbohydrates in Africa, cassava is a key food security crop grown by smallholder farmers because it can withstand…

We show you how in easy to follow steps — So, you have picked your Kaggle competition, and you want to start training your model and make yourself known on the Kaggle leaderboard. If like us, you use Google Cloud AI Platform for your data science workloads, one of the first steps in a Kaggle competition is to upload the…

In the third article of the series, we explore Google’s (human) Data Labelling Service for Advanced Video Labelling — In Part 1, we explored how Google’s (human) Data Labelling Service could assist in image labelling. Google Cloud AI Platform: Human Data labeling-as-a-Service Part 1 In the second article of the series, we explore Google’s (human) Data Labelling Service for Advanced Image Labellingtowardsdatascience.com