When I used to think of lambda functions on AWS my eyes would glaze over, I would roll my eyes and say, “I work with big data, what in the world can a silly little AWS lambda function offer me?” I’ve had to eat my own words, those little suckers come in handy in my day to day engineering work. I want to talk about how every data engineer working with AWS can take advantage of lambda’s and add them to their data pipeline tool belt.
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This is a topic I’ve been musing about lately. The idempotent data load has been a source of much pain and suffering in the lives of many a data engineer and data warehouse developers. Apparently somethings don’t change with the passage of time. My first job in tech was working on a data warehouse team with a classic Kimball style model on SQL Server, back then worrying how to make data loads and ETL idempotent was the task of the hour. All these years later working on data lakes in DataBricks with Spark … guess what …. still worrying about idempotent ETL and data loads.
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Time to open a can of worms. I’ve recently been working with DataBricks, specifically DeltaLake (which I wrote about here). DeltaLake is an amazing tool that when paired with Apache Spark, is like the juggernaut of Big Data. The old is new, the new is old. The rise of DataBricks and DeltaLake is proof of the age old need for classic Data Warehousing/Data Lakes is as strong as ever. While this Spark+DeltaLakes tech stack is amazing, it’s not your Grandma’s data warehouse, it’s fundamentally different under the hood. One of the topics I’ve been thinking about lately has been data modeling in DeltaLake (on DataBricks or not).
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Dagster, the first few times I read the name, I just couldn’t take the tech stack seriously …. it’s still kinda hard. Today I want to compare Airflow vs Dagster, mostly explore what Dagster is and does. But I want to compare it to the popular Apache Airflow project so people have some context for it. It’s kinda hard keeping up on all the new stuff these days, I usually just wait till I see enough articles and tweet floating around about it, then I know it’s maybe worth a peak. Let’s crack open Dagster, and see if it’s better then the name chosen for it.
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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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Not going to lie. I’ve been trying to figure out for awhile where Apache Flink fits in the Data Engineering world for awhile now. A year or two ago I didn’t seem much content posted about it, but it seems to be picking up stream. I’ve mostly managed to avoid understanding what Flink is or does, but I figured it’s time to give my brain a much needed workout. When I was ignoring Flink, I just chalked it up as another messaging/streaming system like Kafka or Pulsar. Apparently I was wrong … no surprise there.
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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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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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There are few things in life that are worse then cracking open some serious PySpark pipeline code, and then realizing there isn’t a single function written to encapsulate logic … wondering if some change you are about to make will bring down the whole pipeline. When you are new to a codebase you don’t know what you don’t know, you don’t have any backstory and you are usually flying by the seat of your pants in the beginning. When you have no unit tests, usually the only other way to test changes on a Spark pipeline are to run it …. which is sometimes easier said then done in a development environment. The first line of defence should be unit testing the entire PySpark pipeline.
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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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