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Delta Lake without Spark (delta-rs). Innovation, cost savings, and other such matters.

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The intersection of Big Data and Not Big Data.

An interesting topic of late that has been rattling around in my overcrowded head is the idea of Big Data vs Not Big Data, and the intersection thereof. I’ve been thinking about SAAS vendors, the Modern Data Stack, costs, and innovation. A great real-life example of all these topics is Delta Lake. Delta Lake is the child of Databricks, officially or not, and at a minimum has exploded in usage because of the increasing usage of Databricks and the popularity of Data Lakes.

Delta Lake, Hudi, Iceberg, all these ACID/CRUD abstractions on top of storage for Big Data have been game changers. But, as with any new popular tech, it comes with its own set of challenges. Specifically for Delta Lake … if you want to use it 99.9% of people are going to have to use Spark to do so, which can be costly, in terms of running clusters, and add complexity, in terms of new tooling, data pipelines, and the like. Anytime you only have one path to take with a tool, innovation is stifled, and barriers arise. Enter delta-rs the Standalone Rust API for Delta.

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New to PySpark? Do this, not that.

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Do this, not that. Well, I’ve got my own list. With everyone jumping on the PySpark / Databricks / EMR / Glue / Whatever bandwagon I thought it was long overdue for a post on what to do, and not to do when working with Spark / PySpark. I take the pragmatic approach to working with Spark, it’s honestly very forgiving well and far into the 10s of TBs of data. Once you wander past that point things tend to get a little spicy if you don’t have it all dialed in. As with most things in life if you get a few things right, and of course don’t do some things, that will get you a long way, the same applies to Spark.

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Introduction to dbt … for Data Engineers.

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So, you’ve heard about dbt have you. I honestly can’t decide if it’s here to stay or not, probably is, enough folks are using it, and preaching about it. I personally have always been a little skeptical of dbt, not because it can’t do what it says it can do, it can, but because I’m old and bitter from my many years of Data Engineering, and I always see the problems in things.

But, I will let you judge that for yourself. Today I want to give a brief overview of dbt, kick the tires, muse about its features, and most importantly, look at dbt from a Data Engineering perspective, ferret out the good, the bad, and the ugly. I will try my best to be nice but don’t count on it. Code is on GitHub.

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Introduction to Designing Data Load Patterns.

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When I think back many moons ago, to when I started in Data Engineering world … even though it went by many different names back in the olden days … I didn’t know what I didn’t know. All those years ago Kimball’s Data Warehouse Toolkit was probably the only resource really available at the time that touched on the general concepts that most “Data Engineers” at the time were working on. The field has come a long way since those days and changed for the better, it’s less often you see classic Data Warehouses running on legacy SQL Servers, with stored procedures with hundreds and thousands of lines of SQL code.

That had me thinking about designing data load patterns in the Modern Data Stack. I want to talk about general data loading patterns, how to design your data pipelines, at a high level, and the basic principles and practices that apply to 99% of all the transformations and data loads done by most Data Engineers.

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Machine Learning from the viewpoint of an average Data Engineer.

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I’ve been thinking more about the topic of ML and MLOps lately. To me, it seems like the buzz has quieted down over the last few years about ML and MLOps, at least somewhat, in favor of other topics like Data Quality, Data Lakes, Data Contracts, and the like. I’ve been wondering why this is the case and comparing my experience over the last few years of working in, on, and around ML pipelines and systems. I’ve seen ML done at companies with a few thousand employees, and with a handful of employees. The problems and hurdles at the same across the board, and mostly everyone is not very good at it.

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Soda-Core. Data Quality at Scale.

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Ever since playing with Great Expectations with Spark some time ago, I’ve been on the lookout for more Data Quality at-scale tools. The market still has a long way to go with these tools, not enough options, hard to use, and the typical Data Engineering travails. I came across soda-core recently, a self-proclaimed…

“Data reliability testing for SQL- and Spark- accesssible data.“

soda-core docs

Doing anything at scale, well … that’s usually the problem. Data Quality and Observability are topics were hear a lot about these days. The reality often doesn’t meet the expectations most of the time. Even Great Expectations, being awesome, can get complicated real quick-like. Let’s hope that soda-core pair with Spark can show us some real promise. Code available on GitHub.

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DataFusion courtesy of Rust, vs Spark. Performance and other thoughts.

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I think it’s funny that DataFrames are so popular these days, I mean for good reason. They are a wonderful and intuitive way to work with and on datasets. Pandas … the nemesis of all Data Engineers and the lover of Data Scientists. Apache Spark is really the beast that brought DataFrames to the masses. Even those little buggers over at Apache Beam give you DataFrames.

Of course, when anything gets popular, you start getting little things that start to pick and peck at the heels. I would probably say that is what DataFusion with Rust seems to be. Seems more like a contender against Pandas rather than Spark to me. I guess if you’re just using Spark locally or on a single node, sure you could consider using DataFusion. Code available on GitHub.

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Real-Life Example of Big O(n) Notation (and other such nonsense) for Data Engineering.

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In the beginning, I always thought the humdrum Big O Notation discussions should be reserved for Software Engineers who enjoyed working on such things. I mean, what could it possibly have to do with Data Engineering? I mean, if you are the person writing the Spark application, by all means, have at it, but if you are the Data Engineer who is simply using Spark, why can’t you just leave the details to the Devil? Seems to make sense.

The only problem with that logic is the longer you work as a Data Engineer, probably the harder the problems you work on become, you write more and more code, and basically end up being a specialized Software Engineer … even if you don’t want to be. In the end, to be a good Data Engineer you should at least attempt to understand the concepts behind Big O Notation, and how those concepts can apply to you as Data Engineer, especially for the ETL that most of us write.

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You Only Need 2 Data Validations, That’s It.

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I mean, I’m sort of being facetious and sort of not. I mean there is some truth that rings out in those words. I’m sure someone selling Data Observability tools, or writing Great Expectations all-day will not like the idea of relying on only 2 data validations. But honestly, these two are probably more than 80% of Data Teams are using today for validation, which is none. What 2 are you? Glad you asked.

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Exploring Delta Lake’s ZORDER, and Performance. On Databricks.

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I think Delta Lake is here to stay. With the recent news that Databricks is open-sourcing the full feature-set of Delta Lake, instead of keeping the best stuff for themselves, it probably has the most potential to be the number one go-to for the future of Data Lakes, especially within those organizations that are heavy Spark users.

One of the best parts about Delta Lake is that it’s easy to use, yet it has a rich feature set, making it a powerful option for Big Data storage and modeling. One of those features that promise a lot of performance benefits is something called ZORDER. Today I want to explore more in-depth what ZORDER is, when to use it, when not to use it, and most importantly test its performance during a number of common Spark operations.

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