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.
Read more
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.
Read more
It doesn’t take long reading articles on Medium or Towards Data Science to become enamored with Machine Learning. Especially the people and companies who do it in “production.” I always read about the big picture, the fancy algorithms, the cloud computing, but you have that feeling there is something missing. It’s all the details that are missing. Where is the force behind it all, bringing everything together? I like to think it’s called Data Engineering, with some Dev Ops for good measure.
Read more
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.
Read more
It seems like a never ending battle for supremacy. Articles about Data Science being the bee’s knees, then more articles about how Data Engineering holds up the world of Data Science like Atlas. Whenever I read something in one of these two categories on Medium or wherever, it just seems more like ego clash to me. It’s human nature to want to be the best, to be better, to feel like you are the person who really makes it all happen.
Read more
Data gets bigger and teams want to process data faster, what else can you do? There is only so much code tweaking you can do, threads, processes, asyncio, it’s only going to get you so far. At some point you have terabytes of data to process, and it requires a decision about some sort of distributed processing system.
In my experience I’ve mostly used two different distributed data processing systems in production, Spark and Kubernetes. To be honest the choice has always been obvious when to choose one over the other. The data usually dictates which system you choose. I’m sure there are super fans of each system who would argue there’s always a way to do any transform or process on each, but sometimes the point is, which system is setup to easily and quickly move the data from one point to another, and transform it as needed.
Read more
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.
Read more
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.
Read more
When building data pipelines all day long, every day, every year, ad infinitum, suprisingly I have managed learn some things. You see the same problems with data pipelines many times over. Years ago it was SSIS (I’m sorry you still have to use it, it just isn’t cool enough anymore), now if it’s not Streaming it must be wrong (Insert eye roll). The technology and what’s hot is always changing, but the 10 Commandments of Data Pipelines never change.
What are the 10 Commandments of Data Pipelines that thou shalt not break? Glad you asked.
Read more
Yeah, data engineering seems to be a hot topic today, as much as data science is/was 3 years ago. What does a data engineer do, what skills do you need? Peruse the job postings it will quickly become overwhelming. Spark, Hadoop, SQL, Python, Scala, ETL, Data Warehousing, various Data Sciencey Things, Streaming, Analytics, Business Intelligence, Machine Learning, blah blah blah. What are the top skills need to be a successful Data Engineer? What does the average day look like for a Data Engineer? Here is my two cents, it’s probably worth what you paid to read this.
Read more