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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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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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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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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If you’re anything like me when someone says Delta Lake you think DataBricks. But, the mythical Delta Lake is an open source project, available to anyone running Apache Spark. It seems also too good to be true, ACID transactions on the Spark scale? Incredible. This is the future, it has to be. The lines of what is a data warehouse have been starting to blur for a long time, I have a feeling Delta Lake will be the death blow to the traditional DW … or its rebirth??
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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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I am going to peer into the crystal-ball, the seeing stone, looking into the murky future of Data Engineering to see what mysteries it holds. I’ve seen a story, a tale of two Data Warehouses, I’ve seen Machine Learning, Streams, Distributed Systems, Storage, the eternal SQL. A lot has changed in the world of Data Engineering in the last few years, but a lot has not changed in the data world as well. Articles about the end of ETL the rise ELT, Hadoop being dead, new data paradigms, no code data flows, managed services, yet very little has actually changed, or it does at a snails pace. Yet, inevitably the store and future of data engineering can be told through the tale of two data warehouses.
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In part 1 of the big data file formats we reviewed Parquet vs Avro. It was apparent from the start that the two file formats were built for different things. Avro is clearly a complex row structured file format used in communication and transactions, where schema is king and nested structures are no problem. Parquet on the other hand has risen to the top with the popularity of Spark, is columnar based storage and is well suited to structured and tabular type data. But, lest the annals of inter-webs call us uncouth and forgetful, we must add ORC file format to the list.
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