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Snowflake’s New Python API Empowers Data Engineers to Build Modern Data Pipelines with Ease

Snowflake

In today’s data-driven world, developer productivity is essential for organizations to build effective and reliable products, accelerate time to value, and fuel ongoing innovation. While the Python API connector remains available for specific SQL use cases, the new API is designed to be your go-to solution.

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Data Engineering Weekly #175

Data Engineering Weekly

Experience Enterprise-Grade Apache Airflow Astro augments Airflow with enterprise-grade features to enhance productivity, meet scalability and availability demands across your data pipelines, and more. As I have shared , its impact on data engineering is exciting.

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Seamless SQL And Python Transformations For Data Engineers And Analysts With SQLMesh

Data Engineering Podcast

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Rudderstack]([link] RudderStack provides all your customer data pipelines in one platform.

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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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Data Engineering Weekly #173

Data Engineering Weekly

[link] Meta: Composable data management at Meta Meta writes about its transition to a composable data management system to improve interoperability, reusability, and engineering efficiency. It is a long standing question on people wondering In what situations should you use SQL instead of Pandas as a data scientist?

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How to improve at SQL as a data engineer

Start Data Engineering

SQL skills 2.1. Data modeling 2.1.1. Data storage 2.2. Data transformation 2.2.1. Data pipeline 2.4. Data analytics 3. Introduction SQL is the bread and butter of data engineering. Introduction 2. Gathering requirements 2.1.2. Exploration 2.1.3. Modeling 2.1.4. Practice 4.

SQL 130
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Our First Netflix Data Engineering Summit

Netflix Tech

Engineers from across the company came together to share best practices on everything from Data Processing Patterns to Building Reliable Data Pipelines. The result was a series of talks which we are now sharing with the rest of the Data Engineering community!