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Reflections on Strong Momentum and Category Leadership in Data Observability

Monte Carlo

When we launched the data observability category in 2020, we set out to solve a very real problem: data trust. In the preceding months, I met with hundreds of data leaders about what kept them up at night. Four years, hundreds of customers, and an entire category later and we’re just getting started.

MySQL 64
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How to learn data engineering

Christophe Blefari

Learn data engineering, all the references ( credits ) This is a special edition of the Data News. But right now I'm in holidays finishing a hiking week in Corsica 🥾 So I wrote this special edition about: how to learn data engineering in 2024. Who are the data engineers?

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5 SQL Visualization Tools for Data Engineers

KDnuggets

This article will discuss SQL visualization, its role in augmenting the modern-day data engineer, and five categories of SQL visualization tools.

SQL 96
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Data Engineering Trends With Aswin & Ananth

Data Engineering Weekly

Welcome to another insightful edition of Data Engineering Weekly. As we approach the end of 2023, it's an opportune time to reflect on the key trends and developments that have shaped the field of data engineering this year. In conclusion, 2023 has been a year of significant developments and shifts in data engineering.

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GPT-based data engineering accelerators

RandomTrees

GPT-based data engineering accelerators make the working of data more accessible. These accelerators use GPT models to do data tasks faster, fix any issues, and save a lot of time. GPT models change data in simple language and also provide summaries and explanations. One can rely on this information.

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Data Engineering in Retrospect: Key Trends and Patterns of 2023

Data Engineering Weekly

It’s the end of the year, and there will be a lot of buzz about what the next five years in data engineering might bring. As Benn Stancil noted in his blog, “The data industry is going to consolidate” is a pretty boring prediction to make these days. The categories are merging.

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Python for Data Engineering

Ascend.io

The rise of data-intensive operations has positioned data engineering at the core of today’s organizations. As the demand to efficiently collect, process, and store data increases, data engineers have started to rely on Python to meet this escalating demand. Why Python for Data Engineering?