At times, I've found my opinion of MongoDB Atlas and MongoDB Stitch to waver between two extremes. Sometimes I'm struck by the allure of a cloud which fundamentally disregards schemas (wooo no schema party!). Other times, such as when Mongo decides to upgrade to a new version and you find all your production instances broken, I like the ecosystem a
The honest act of systematically stealing data without permission
There are plenty of reliable and open sources of data on the web. Datasets are freely released to the public domain by the likes of Kaggle, Google Cloud, and of course local & federal government. Like most things free and open, however, following the rules to obtain public data can be a bit... boring. I'm not suggesting we go and
Use Python and MySQL to Build an Endpoint
Now that you know your way around API Gateway, you have the power to create vast collections of endpoints. If only we could get those endpoints to actually receive and return some stuff.
We'll create a GET function which will solve the common task of retrieving data from a database. The sequence will look something like:
- Connect to the database
Deploy a MySQL database that auto-creates endpoints for itself.
It wasn’t too long ago that I haphazardly forced us down a journey of exploring Google Cloud’s cloud SQL service. The focus of this exploration was Google’s accompanying REST API for all of its cloud SQL instances. That API turned out to be a relatively disappointing administrative API which did little to extend the features you’d
Steal our code and never manually walk through JSON objects again
We're all data people here, so you already know the scenario: it happens perhaps once a day, perhaps 5, or even more. There's an API you're working with, and it's great. It contains all the information you're looking for, but there's just one problem: the complexity of nested JSON objects is endless, and suddenly the job you love needs to
Playing with tabular data the native Python way.
Tables. Cells. Two-dimensional data. We here at Hackers & Slackers know how to talk dirty, but there's one word we'll be missing from our vocabulary today: Pandas.Before the remaining audience closes their browser windows in fury, hear me out. We love Pandas; so much so that tend to recklessly gunsling this 30mb library to perform simple tasks. This isn't
Since you prefer using Python and Flask, I’ll assume we both prefer enjoyable dev.
It's been roughly a year since MongoDB launched their Stitch "back-end as a service" product, and I've been tinkering with Mongo on the cloud ever since. Alright fine, "tinkering with" may better be described as "accidentally became dependent on it after developing new features in production environments," but I can't really complain thus-far. If you're not familiar, MongoDB Atlas is
Cracking Tableau's master Postgres account
Let's say you're a Data Scientist. Well maybe not a data scientist... I mean, those online data analysis courses were definitely worth it, and you'd made it this far without being quizzed on Bayesian linear regression. So maybe you're analyst or something, but whatever: you use Tableau, So you must be a Scientist™.
I've admitted a few times in the
Forcefully use the Pandas library in your AWS Lambda functions
In one corner we have Pandas: Python's beloved data analysis library. In the other, AWS: the unstoppable cloud provider we're obligated to use for all eternity. We should have know this day would come.
While not the prettiest workflow, uploaded Python package dependencies for usage in AWS Lambda is typically straightforward. We install the packages locally to a virtual env,
Make use of Python's native XML library to walk through and extract data
Life is filled with things we don't want to do; you're a developer so you probably understand this to a higher degree than most people. Sometimes we waste weeks of our lives thanks to an unreasonable and unknowledgeable stakeholder. Other times, we need to deal with XML trees.
At some point or another you're going to need to work with