Engineering Blog

Let’s start with a short disclaimer — DoiT International does not promote, condone or advocate licit or illicit drug use.

At DoiT International, we are using Google BigQuery quite extensively as a data analytics platform for reOptimize (now part of Cloud Intelligence™) — our cost optimization platform for Google Cloud Platform.

As some of you might already know, DoiT International is the engineering power behind reOptimize — Cost Discovery and Optimization SaaS for Google Cloud Platform.

Traditionally, a lot of companies rely on Mixpanel for product analytics to understand each user’s journey. However, if your product becomes a success and your volume of events is getting high, Mixpanel may become somewhat expensive. In this post we are going to review one of our projects with Jelly Button to design their own event-analytics solution based on Google Cloud Platform in a very efficient way and which is going to save Jelly Button about quarter million dollars each year.

The 2nd quarter of 2017 is over and now it’s time to summarize the Multi-Cloud Engineering Meetup group for the first half of the year.

One of the perks I really like as part of my work as CTO at DoiT International, is my day-to-day conversations with our customers. On many occasions, they are enlightening and I always learn something new.

Recently, I came across an interesting benchmark of BigData systems based on “A Comparison of Approaches to Large-Scale Data Analysis” by Pavlo et al. (SIGMOD 2009). Based on the benchmark methodology, the APMLab guys from Berkeley University developed an open-source software that allows anyone to run this benchmark using public cloud (AWS in this case).