Data Lake, Data Warehouse, Lake House, Data Mart, it’s always something isn’t it? Don’t get me started on Data Mesh. Yikes, it’s hard to keep up these days. I want to explore the Data Lake vs the Data Warehouse and what it really all boils down to, what is the real difference. Is it data modeling, architecture, storage? I think their are a few different things that differentiate a Data Lake from a true Data Warehouse, let’s talk.
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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.
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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The two coolest kids in class … I mean seriously … every other post in Data Engineering world these days is about Apache Airflow or DataBricks. It’s hard to kick against the goad. Just jump on the band wagon before you get left in the dust. I’ve used both DataBricks and Apache Airflow, they both are pretty important and integral tools for data engineers these days. Apache Airflow makes overall complex pipeline dependencies, orchestration, and management intuitive and easy. DataBricks has delivered with AWS and EMR could not, easy to use Spark and DeltaLake functionality without the management and config nightmares of running Spark yourself.
Recently I worked on an Airflow and DataBricks/DeltaLake integration, time to talk what it looks like and options when doing this type integration.
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Apache Druid, kinda like that second cousin you know about … but don’t really know. When you see them for the first time in 10 years you kinda look at them out of the corner of your eye. That’s how I feel about Apache Druid, I’ve always known it has been there, lurking around in the shadows, but it rarely pokes it head out and I have no idea what, why, how it is used. Time to change that, for the better or worse. Let’s take 10,000 foot survey of Druid.
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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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