Improving Bridge Four, a simple, functional, effectful, single-leader, multi worker, distributed compute system optimized for embarrassingly parallel workloads by providing consistency guarantees and improving overall code quality (or something like that).
Building something that already exist (but worse) so I don't have to think about Leetcode: Bridge Four, a simple, functional, effectful, single-leader, multi worker, distributed compute system optimized for embarrassingly parallel workloads.
One question I do get in earnest quite frequently is why I put up with running GNU/Linux distributions for development work. An attempt at a simple response.
In Part 2 of our comparison of Python and go from a Data Engineering perspective, we'll finally take a look at Apache Beam and Google Dataflow and how the go SDK and the Python SDK differ, what drawbacks we're dealing with, how fast it is by running extensive benchmarks, and how feasible it is to make the switch
Exploring golang - can we ditch Python for go? And have we finally found a use case for go? Part 1 explores high-level differences between Python and go and gives specific examples on the two languages, aiming to answer the question based on Apache Beam and Google Dataflow as a real-world example.
The amount of time my outdoor cameras are being set off by light, wind, cars, or anything other than a human is insane. Overly cautious security cameras might be a feature, but an annoying one at that...
In 2017, I wrote about how to build a basic, Open Source, Hadoop-driven Telematics application (using Spark, Hive, HDFS, and Zeppelin) that can track your movements while driving, show you how your driving skills are, or how often you go over the speed limit - all without relying on 3rd party vendors processing and using that data on your behalf...
In this article, we'll take a look at whether Apache Hadoop still a viable option in 2019, with Cloud driven data processing an analytics on the rise...
In the last iteration of this article we analyzed the top 100 subreddits and tried to understand what makes a reddit post successful by using Google’s Cloud ML tool set to analyze popular pictures.
In this article (and its successors), we will use a fully serverless Cloud solution, based on Google Cloud, to analyze the top Reddit posts of the 100 most popular subreddits. We will be looking at images, text, questions, and metadata...
In this article, we will use Heron, the distributed stream processing and analytics engine from Twitter, together with Google’s NLP toolkit, Nominatim and some Machine Learning as well as Google’s BigTable, BigQuery, and Data Studio to plot Twitter user's assumed location across the US.