BigQuery & new users – the top “WTF!?” moments

“What the Fudge?”

I use Google BigQuery a lot. On a daily basis I run dozens of queries, use it to build massively scalable data pipelines for our clients, and regularly help new users navigating it for the first time. Suffice it to say I’m somewhat accustomed to its little quirks. Unfortunately, the same can’t be said for the new users who are commonly left scratching their heads, and shouting “What the fudge!?” at their monitors.

Here’s the top three WTFs that I regularly hear from new BigQuery users:

Beam me up Google – porting your Dataflow applications to 2.x

Will this post interest me?

If you use (or intend to use) Google Cloud Dataflow, you’ve heard about Apache Beam, or if you’re simply bored in work today and looking to waste some time, then yes, please do read on. This short post will cover why our team finally took the plunge to start porting some of Dataflow applications (using the 1.x Java SDKs) to the new Apache Beam model (2.x Java SDK). Spoiler – it has something to do with this. It will also highlight the biggest changes we needed to make when making the switch (pretty much just fix some compile errors).

Creating a serverless ETL nirvana using Google BigQuery

Quite a while back, Google released two new features in BigQuery. One was federated sources. A federated source allows you to query external sources, like files in Google Cloud Storage (GCS), directly using SQL. They also gave us user defined functions (UDF) in that release too. Essentially, a UDF allows you to ram JavaScript right into your SQL to help you perform the map phase of your query. Sweet!

In this blog post, I’ll go step-by-step through how I combined BigQuery’s federated sources and UDFs to create a scalable, totally serverless, and cost-effective ETL pipeline in BigQuery.

Google Cloud Dataproc and the 17 minute train challenge

multiple-seats

My work commute

My commute to and from work on the train is on average 17 minutes. It’s the usual uneventful affair, where the majority of people pass the time by surfing their mobile devices, catching a few Zs, or by reading a book. I’m one of those people who like to check in with family & friends on my phone, and see what they have been up to back home in Europe, while I’ve been snug as a bug in my bed.

Stay with me here folks.

But aside from getting up to speed with the latest events from back home, I also like to catch up on the latest tech news, and in particular what’s been happening in the rapidly evolving cloud area. And this week, one news item in my AppyGeek feed immediately jumped off the screen at me. Google have launched yet another game-changing product into their cloud platform big data suite.

It’s called Cloud Dataproc.

Test Driving Google Cloud Dataflow

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Back in June 2014, at the annual Google IO in San Francisco, Google unveiled their newest, and much hyped cloud product, Cloud Dataflow. The demo they did that day, using a live twitter feed to analyze supporter sentiment during the 2014 world cup, got my mouth watering at the prospect of working with it. It looked downright freaking awesome, and I just couldn’t wait to get my hands on it to take it for a spin.