Category: Data Engineering
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Modernizing Data Warehouses for AI: A 4-Step Roadmap
It’s the same conversation in every boardroom and Slack channel: “How are we using LLMs? Where are our AI agents? When do we get our Copilot?” But for the teams in the trenches, the hype is hitting a wall of legacy infrastructure. The truth is that Modernizing Data Warehouses for AI is the invisible hurdle…
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How Poor Data Engineering Corrodes GenAI Pipelines
Generative AI (GenAI) has captivated the world with its ability to create, synthesize, and reason. From crafting compelling marketing copy to assisting in scientific discovery, its potential seems boundless. However, the dazzling outputs often mask a critical vulnerability: the quality of the data underpinning these systems. When data engineering falters, issues of data quality, governance,…
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The Data Engineer Role in a ML Pipeline
Data engineers provide the critical foundation for every successful Machine Learning (ML) deployment, supporting the powerful models and insights that often grab headlines. While data scientists focus on model development and evaluation, data engineers ensure that the right data is collected, processed, and made available in a reliable and scalable way. 1. The Overlooked Hero…
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Data Engineer – The Top 10 Books to read in 2023
Whether you are just starting out as a data engineer or you are an old pro it is always important to stay up to date on trends and technologies. In this post I will talk about the top 10 books every data engineer should read in 2023 to keep their skills fresh. Data Science from…