Cloudflare R2 Storage with Apache Iceberg

Rethinking Object Storage: A First Look at Cloudflare R2 and Its Built‑In Apache Iceberg Catalog Sometimes, we follow tradition because, well, it works—until something new comes along and makes us question the status quo. For many of us, Amazon S3 is that well‑trodden path: the backbone of our data platforms and pipelines, used countless times each day. If […]

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dbt on Databricks

Running dbt on Databricks has never been easier. The integration between dbtcore and Databricks could not be more simple to set up and run. Wondering how to approach running dbt models on Databricks with SparkSQL? Watch the tutorial below.

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How I (Barely) Survived Setting Up Polaris as an Iceberg REST Catalog

There are things in life that are satisfying—like a clean DAG run, a freshly brewed cup of coffee, or finally deleting 400 lines of YAML. Then there are things that make you question your life choices. Enter: setting up Apache Polaris (incubating) as an Apache Iceberg REST catalog. Let’s get one thing out of the […]

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Reading Excel (.xlsx) Files with Polars

I make it my duty in life to never have to open an Excel file (xlsx); I feel like if I do, then I made a critical error in my career trajectory. But, I recently had no choice but to open an Excel on a Mac (or try) to look at some sample data from […]

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dbt on Databricks.

Context and Motivation dbt (Data Build Tool): A popular open-source framework that organizes SQL transformations in a modular, version-controlled, and testable way. Databricks: A platform that unifies data engineering and data science pipelines, typically with Spark (PySpark, Scala) or SparkSQL. The post explores whether a Databricks environment—often used for Lakehouse architectures—benefits from dbt, especially if […]

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Apache XTable. Delta vs Iceberg vs Hudi.

The blog post reviews an Apache Incubating project called Apache XTable, which aims to provide cross-format interoperability among Delta Lake, Apache Hudi, and Apache Iceberg. Below is a concise breakdown from some time I spend playing around this this new tool and some technical observations:

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What is a Healthy Lake House?

Maybe I’m the only one who thinks about it, not sure. The Lake House has become the new Data Warehouse, yet when I ask this question “What makes a health Lake House?” no one is sure what the answer is, or you get different answers. It seems like a pretty important question considering that Lake […]

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AWS Lambda + DuckDB + Polars + Daft + Rust

When it comes to building modern Lake House architecture, we often get stuck in the past, doing the same old things time after time. We are human; we are lemmings; it’s just the trap we fall into. Usually, that pit we fall into is called Spark. Now, don’t get me wrong; I love Spark. We […]

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PySpark Data Quality on Databricks with DQX.

A Deep Dive into Databricks Labs’ DQX: The Data Quality Game Changer for PySpark DataFrames Recently, a LinkedIn announcement caught my eye—and honestly, it had me on the edge of my seat. Databricks Labs has unveiled DQX, a Python-based Data Quality framework explicitly designed for PySpark DataFrames. Finally, a Dedicated Data Quality Tool for PySpark […]

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Testing and Development for Databricks Environment and Code.

Every once in a great while, the question comes up: “How do I test my Databricks codebase?” It’s a fair question, and if you’re new to testing your code, it can seem a little overwhelming on the surface. However, I assure you the opposite is the case.

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