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Introduction to Postgres with Python

Python and Postgres, a match made in heaven.

If there was ever a match made in heaven, it’s using Python and Postgres together. They were made for each other. Both are fun and easy to use, addicting, both have so many surprises and hidden gems. Like Gandalf and Frodo, the two just go together. Today I want to go through the basics of interacting with Postgres using Python. In the beginning of my data career this was often a point of pain, even though it seems like it should be easy. Let’s hit on the basics and then a few of the not-so-obvious things I wish I would have known in the beginning.

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Exploring ElasticSearch with Python

Exploring Elasticsearch with Python.

What’s Elasticsearch precious? I feel like Gollum when confronted by taters. Elasticsearch has been around for awhile now, based on Lucene, it’s become a well known name in the field of text and semi structured data storage, analysis and retrieve category. Even though it’s popular enough to get name recognition I’ve rarely run across it in the wild. We are going to dip our toes into Elasticsearch by working on a small project to store and search a book(s). It just give us enough simple problems to solve that by the end we should have at least a basic understanding of how to connect, store, and retrieve simple documents with Elasticsearch.

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3 (Or More) Ways to Open a CSV in Python

Ah. What a classic. The one piece of code that I end up writing over and over again, you would think I would have stashed it away by now. Not going to lie I usually have to Google it, while thinking, is this the right way? Should I just open the csv file and iterate it? Should I import the csv module? Should I just use Pandas? Does it matter? Probably not.

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Thunderdome for Geospatial Tools in Python. It’s to the Death.

A fight to the death. A comparison of geo-spatial tools in Python. What’s easy and fast to use.

It’s a fight to the death people… that’s why it’s called Thunderdome. This will be no different. Last time we talked about the very basics of the strange world of geo-spatial tools for data engineering. The next most obvious thing do of course is to see what tool is the best. By best I mean what tools can be used to load and do simple manipulation of data in a fast and relatively simple manner.

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Gentle Introduction to Geospatial for Data Engineers

Quick view of geospatial data landscape.

What does a data engineer need to know about working with geospatial data? I’m going to give my two cents on what is and is not important. First, prepare to be annoyed as you will most likely spend hours debugging strange and not obvious errors and bugs. You should run screaming the other way, but in case that is not a option, here are the basics.

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Python Async File Operations – Juice Worth the Squeeze?

Async file operations in Python, juice worth the squeeze?

What I’ve greatly feared has come to pass. I’ve come to love on of the most confusing parts of Python. AysncIO. It has this incredible ability for data engineers building pipelines in Python to take out so much wasted IO time. It saves money. It’s faster. People think you’re smarter than you are. Tutorials are one thing but implementing it in your complex code is typically mind bending and a test of your patience and self-worth.

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Dask – To Distribute or Not To Distribute..Ahh..This Thing Sucks.

Raise your hand if you’ve every used Dask? ……. Me either. With tools like Spark, I’ve only recently started seeing some articles and podcasts pop up around Dask. Written in Python and claiming to be a distributed data processing framework I figured it was about time to check it out. Suprisingly when reading up on the Dask website, it appears they don’t necessarily claim to be out to replace things like Spark. Time to kick the tires.

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Concurrently Download Large Files from GCS.

Waiting for large files to download is boring.

There is nothing more annoying than sitting around waiting for files to download. That was true while I was in high school staring at LimeWire, it’s still true today. Especially when you’re a data engineer who’s supposed to make data pipelines fast. You’re in luck! Yes, it is possible to download a large file from Google Cloud Storage (GCS) concurrently in Python. It took a little digging in Google’s terrible documentation for their Python cloud storage wrapper (hear my snarky-ness), but I found a diamond in the rough.

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You Have to Try This… from io import StringIO, BytesIO

StringIO and BytesIO are perfect for making your Python faster.

Ever heard of something called a File Object in Python? Ever heard of BytesIO or StringIO? Your missing out. It’s easy, fast, and wonderful, in short, it’s the best. For some reason IO streams are a totally underused feature that rarely comes up in most code. We all know that memory if faster than disk IO, this is what I use IO streams for.

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Concurrency in Python Data Pipelines – Async, Threads, Processes … Oh My.

Concurrency in Python is like the awkward group of middleschool boys gathered in the corner of the dance floor all pointing to the girls in the other corner. Everyone talks a big game, pretending like they totally understand the other group and could easily handle the pickup if they wanted to. Mmmhmmm.

When push comes to shove and you actually have to pick the girl to take to the dance floor, it all the sudden becomes this wierd and strange shuffle of interactions that sometimes works out, and sometimes doesn’t. That is Python concurrency in data pipelines for most folk.

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