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Build your Data Engineering skills with Open Source Data

There are many a day when I find myself scrolling through the subreddit for r/dataengineerg, it’s a fun place to stalk. Lot’s of people with lots of opinions make for interesting times. I see one question or a variation of it come up over and over again. How do I learn data engineering skills, how do I get into data engineering, what kind of problems do data engineers solve, blah, blah, blah? It’s a great question, and one without an easy answer. Well … there is an answer but it takes some time and willpower to get it done. Open source data. This is the way. Read books, take classes, do whatever, it’s hard to really learn the skills needed day-to-day as a data engineer without actually doing the work. But how do you do the work without the work? Make up your own work I say.

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5 Basic and Undervalued Data Engineering Skills

What is the standard for most data engineers these days? Turns out SQL and Python are still running the show pretty much across the board. There’s always a variety of skills in those areas, some better, some worse, although with a little work and repetition it’s pretty easy to master both SQL and Python. I’ve found that Python and SQL … or Java … or Scala … having good development skills is really only half the battle. It seems there is always a few basic data engineering skills that come up over and over. They are simple skills, foundational skills that allow an average data engineer to be better. They make a person more versatile and able solve more complex problems and work across a wide variety of of tech stacks and cloud providers. What are they? Read on my fair weathered friend.

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Databricks vs Snowflake. The DataLake/Warehouse Battle.

As someone who worked around the classic Data Warehouses back in the day, before s3 took over and SQL Server and Oracle ruled the day … I love sitting on the sidelines watching new … yet old battle-lines being re-drawn. I could probably scroll back in StackOverflow 12 years and find the same arguments and questions. In one sense Databricks and Snowflake are totally different tools … but are they? Distributed big data processing, apply transforms to data, enable Data Lake / Data Warehouse / Analytics at scale. There is a lot of bleed over between the two, it really comes down to what path you would like to take to get to the same goal.

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Python vs Scala – Concurrency.

One of the reoccurring complaints you always see being parroted by the smarter-then-anyone-else-on-the-internet Reddit lurkers is the slowness of Python. I mean I understand the complaint …. but I don’t understand the complaint. Python is what is is, and usually is the best at what it is, hence its ubiquitous nature. I’ve been dabbling with Scala for awhile, much to my chagrin, and have been wondering about its approach to concurrency for awhile now. I’ve used MultiProcessing and MultiThreading in Python to super charge a lot of tasks over the years, I want to see how easy or complex this would be in Scala, although I don’t think easy and Scala belong in the same sentence.

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What the Marooned Ben Gunn Teaches us about Solo Data Engineers

I always envied Ben Gunn in Treasure Island a little bit. Alone all those years, digging up gold and treasure, hunting wild goat, and living in a nice little cave. Living off the land, king of his island, gone half mad, but somewhat still there. Happy to see other people, but always a little bit of a recluse … too many years alone. I think there is a lesson for us here, and it has to do with the solo Data Engineer. That lone ranger, out there in the data wilderness, surviving.

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Contract to Hire (Take a Chance With Us …. We Won’t With You).

LIfe’s no fun if you don’t keep things interesting. It’s time to ruffle a few feathers.

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The 3 Types of Data Engineers, Which One Are You?

Every good story starts with a few different characters right? It’s like the spice of life, little bit of this, little bit of that. It’s the way of the world. In all my data wandering I’ve come across lot’s of different types of data engineers. I can usually put them into three different categories, somewhat similar but in many ways quite different.

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A Piece of DevOps that most Data Engineer’s Ignore.

I am always amused by the apparent contradictory nature of working in the world of data. There is always bits and pieces that come and go, the popular, the out of style … new technology driving new approaches and practices. One of the hot topics the last decade has produced is DevOps, a now staple of most every tech department. Like pretty much every other newish Software Engineering methodology, data world has struggled to adopt and keep pace with DevOps best practices. Once these is always a thorn in my side, making my life more difficult. The simplicity with which it can be adopted is amazing, and the unwillingness and lack of adoption is strange.

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What makes MLOps so hard? Thoughts for Data Engineers.

It seems like today the problems and challenges of Data Engineering are being solved at a lightning pace. New technologies are coming out all the time that seem to make life a little easier (or harder) while solving age old problems. I feel like Machine Learning Ops (MLOps) is not one of those things. It’s still a hard nut to crack. There have been a smattering of new tools like MLFlow and SeldonCore, as well as the Google Cloud AI Platform and things like AWS Sage Maker, but apparently there is still something missing. Nothing has really gained widespread adoption … and I have some theories why.

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Why Data Engineers Should Care about DataBricks IPO.

Some poor Data Engineer is sweating and typing away in a dark closet … moving data, solving bugs, just trying to get through the day. Why should the ‘ole Data Engineer care about the huff-a-luff around the billion dollar series recently done by DataBricks? I mean what possible reverence could it have on the day to day life of a Data Engineer and why should they care at all? You ever heard of that proverbial light at the end of the tunnel is actually a train steaming your way ready to pulverize you? That’s why.

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