Confessions of a Data Guy

APRIL 28, 2024

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. When I first wrote a little Golang (~2+ years ago) I was just trying to see what the hype was all about.

Data Engineering 162

Data Engineering Data Engineer Engineering Data 162

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.

Data Engineering 52

Data Engineering Data Engineer Engineering Hospitality 52

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.

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. appeared first on Confessions of a Data Guy. appeared first on Confessions of a Data Guy.

Data Pipeline 113

Data Pipeline Designing Data IT 113

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Data Engineering Podcast

APRIL 28, 2024

In this episode he explains the data collection and preparation process, the collection of model types and sizes that work together to power the experience, and how to incorporate it into your workflow to act as a second brain. Data lakes are notoriously complex. Your first 30 days are free! Want to see Starburst in action?

Building 147

Building Data Lake High Quality Data Machine Learning 147

Speaker: Dr. Greg Loughnane and Chris Alexiuk

Greg Loughnane and Chris Alexiuk in this exciting webinar to learn all about: How to design and implement production-ready systems with guardrails, active monitoring of key evaluation metrics beyond latency and token count, managing prompts, and understanding the process for continuous improvement Best practices for setting up the proper mix of open- (..)

Process

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