Confessions of a Data Guy

APRIL 28, 2024

A few years ago I wasn’t sure, who was going to win, Golang seemed to be popular, and still is for that matter. When I first wrote a little Golang (~2+ years ago) I was just trying to see what the hype was all about. The funny thing is, at the time, and today, it […] The post Why did Golang lose to Rust for Data Engineering? appeared first on Confessions of a Data Guy.

Data Engineering 162

Data Engineering Data Engineer Engineering Data 162

Data Engineering Podcast

APRIL 28, 2024

Summary Generative AI promises to accelerate the productivity of human collaborators. Currently the primary way of working with these tools is through a conversational prompt, which is often cumbersome and unwieldy. In order to simplify the integration of AI capabilities into developer workflows Tsavo Knott helped create Pieces, a powerful collection of tools that complements the tools that developers already use.

Building 147

Building Data Lake High Quality Data Machine Learning 147

Seattle Data Guy

APRIL 28, 2024

If you’ve worked in data long enough, then you’ve likely come across the term change data capture. Often called CDC, change data capture involves tracking and recording changes in a database as they happen, and then transmitting these changes to designated targets. This can be crucial because some pipelines, in particular batch pipelines, don’t capture… Read more The post Terms You Should Know If You’re Planning To Use Change Data Capture appeared first on Seattle D

Database 130

Database Data Designing Big Data 130

Confessions of a Data Guy

APRIL 28, 2024

I was recently confronted with an interesting conundrum when writing a complex data pipeline. It was an interesting problem that arose from my quest to reduce complexity in part of the design, which found itself creeping into another part, re-enforcing the classic idea of whether you can really make the complexity pea go away, or […] The post If:Else Logic and Complexity – Hiding the Pea. appeared first on Confessions of a Data Guy.

Data Pipeline 113

Data Pipeline Designing Data IT 113

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In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate

Data Pipeline

Data Engineering Weekly

APRIL 28, 2024

Intuit: The Data Mesh Strategy Behind Intuit’s Global Financial Technology Platform The Data Product Builder platform is becoming increasingly important in enterprise data engineering. It offers more targeted and customized data asset building than the general-purpose data stack. Intuit writes about the growing data mesh strategy and the strategic focus area for building data products.

Data Engineering 52

Data Engineering Data Engineer Engineering Hospitality 52