Gobbling up big-ish data for lunch using BigQuery

Beers + ‘WSPR’ = fun

To this day, I’m a firm believer in the benefits of simple, informative, and spontaneous conversations with my colleagues – at least with the ones who can stand me long enough to chat with me . Chewing the fat with other like minded folks over a beer or two is a bloody good thing. It’s how ideas are born, knowledge is shared, and relationships are formed. It’s an important aspect of any business that is sadly all too often overlooked.

Analysing Stack Overflow comment sentiment using Google Cloud Platform

The decline of Stack Overflow?

A few months back I read this post from 2015 (yes, I know I’m a little late to the party) about how Stack Overflow (SO) was in serious decline, and heading for total and utter oblivion.  In the post, the first item to be called  out was that SO “hated new users“:

Stack Overflow has always been a better-than-average resource for finding answers to programming questions. In particular, I have found a number of helpful answers to really obscure questions on the site, many of which helped me get past a road block either at work or in my hobby programming. As such, I decided I’d join the site to see if I could help out. Never before has a website given me a worse first impression.

At the time, I remember thinking that this seemed like somewhat of an unfair statement. That was mostly down to the fact that when I joined the community (many years ago), I had fond memories of a smooth on-boarding, and never experienced any snarky remarks on my initial questions. Yes, gaining traction for noobs is very, very hard, but there is a good reason why it exists.

For me, SO is invaluable. How else would I be able to pretend to know what I’m doing? How else could I copy and paste code from some other person who’s obviously a lot smarter than me, and take all the credit for it? Anyway, once I had read the post, and gotten on with my life (e.g. copying and pasting more code from SO), I did’t think too much more about the post. Maybe I had just been lucky with my foray into the SO community?

However, just last week, I was reminded of that post once again, when I noticed that BigQuery (BQ) now has a public dataset which includes all the data from SO – including user comments and answers. Do you see where I am going with this yet? If not, then don’t worry. Neither did I when I started writing this.

Shiner to present at very first YOW!Data conference


Shine’s very own Pablo Caif will be rocking the stage at the very first YOW! Data conference in Sydney. The conference will be running over two days (22-23 Sep) and is focused big data, analytics, and machine learning. Pablo will give his presentation on Google BigQuery, along with a killer demo of it in action. You can find more details of his talk here.

High availability, low latency streaming to BigQuery using an SQS Queue.

When you have a Big Data solution that relies upon a high quality, uninterrupted stream of data for it to meet the client’s expectation you need a solution in place that is extremely reliable and has many points of fault tolerance. That all sounds well and good but how exactly does that work in practice?

Let me start by explaining the problem. About 2 years ago our team was asked to spike a streaming service that could stream billions of events per month to Google’s BigQuery. The events were to come from an endpoint on our existing Apache web stack. We would be pushing the events to BigQuery using an application written in PHP. We did exactly this, however, we were finding that requests to BigQuery were taking too long and thus resulted in slow response times for users. So we needed to find a solution to Queue the events before sending them to BigQuery.

Google BigQuery hits the gym and beefs up!

At Shine we’re big fans of Google BigQuery, which is their flagship big data processing SaaS. Load in your data of any size, write some SQL, and smash through datasets in mere seconds. We love it. It’s the one true zero-ops model that we’re aware of for grinding through big data without the headache of worrying about any infrastructure. It also scales to petabytes. Although we’ve only got terabytes, but you’ve got to start somewhere right?

If you haven’t yet been introduced to the wonderful world of BigQuery, then I suggest you take some time right after this reading this post to go and check it out. Your first 1TB is free anyway. Bargain!

Anyway, back to the point of this post. There have been a lot of updates to BigQuery in recent months, both internally and via features, and I wanted to capture them all in a concise blog post. I won’t go into great detail on each of them, but rather give a quick summary of each, which will hopefully give readers a good overview of what’s been happening with the big Q lately. I’ve pulled together a lot of this stuff from various Google blog posts, videos, and announcements at GCP Next 2016 etc.

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.

Pablo rocking the stage at Google’s annual cloud event!

Last week, Shine’s very own Pablo Caif gave a presentation at GCP Next 2016 in San Francisco, which is Google’s largest annual cloud platform event. Pablo delivered an outstanding talk on the work Shine have done for Telstra, which involves building solutions on the GCP stack to manage and analyse their massive datasets. More specifically, the talk focused around two of Google’s core big data products –BigQuery & Cloud Dataflow.

Shine’s Pablo Caif to present at GCP Next 2016!


Shine is extremely proud to announce that Pablo Caif has been invited to present at GCP Next 2016, which is Google’s largest annual cloud platform event held in San Francisco.

Pablo will be presenting on the work Shine have done for Telstra, which involves building solutions on GCP to manage and analyse their massive datasets. More specifically, the talk will focus around Google’s two core big data products – BigQuery & Cloud Dataflow.

Pablo will be presenting on Thursday 24th March in the ‘Data & Analytics’ track. Be sure to pop by and say “g’day” if you are going to the event! You can find more information about GCP Next 2016 here.


A week in the life of a Google Developer Expert

All the GDEs posing at the Googleplex

A few months back, Shine’s Pablo Caif and Graham Polley were welcomed into the Google Developer Expert (GDE) program as a result of their recent work at Telstra. The projects they are working on consist of building bleeding edge big data solutions using tools like BigQuery and Cloud Dataflow on the Google Cloud Platform (GCP). You can read all about that here.

GDE acceptance comes with many benefits and privileges, one of which is a yearly trip to a private summit at a different location each year. With Google footing the bill, they bring all the GDEs (around 250 currently) from around the globe for, let’s admit it, a complete Google geek-out fest for 2 days!

This year the summit was at the Googleplex in Mountain View. Needless to say, Pablo and Graham were chomping at the bit to go. However, in addition to the summit, Google invited them to fly out prior to actual summit itself. They had lined up a few other things especially for the guys. So this was no ordinary trip. Lucky buggers!

We asked both guys to give their individual feedback on the trip, and here’s what they had to say about it. Read on if you want to hear about how the guys spent six days hanging out with Google in America.