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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 News — Week 23.14

Christophe Blefari

The only normalisation I did was back at the engineering school while learning SQL with Normal Forms. Actually what I cared was physical storage, data formats, logical partitioning or indexing. At the same time Maxime Beauchemin wrote a post about Entity-Centric data modeling. Denormalisation everywhere. YAML configured.

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Data News — Week 13.14

Christophe Blefari

The only normalisation I did was back at the engineering school while learning SQL with Normal Forms. Actually what I cared was physical storage, data formats, logical partitioning or indexing. At the same time Maxime Beauchemin wrote a post about Entity-Centric data modeling. Denormalisation everywhere. YAML configured.

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Data Engineering Weekly #137

Data Engineering Weekly

Data Engineering Weekly Is Brought to You by RudderStack RudderStack Profiles takes the SaaS guesswork, and SQL grunt work out of building complete customer profiles, so you can quickly ship actionable, enriched data to every downstream team. Write SQL queries without learning SQL? See how it works today.

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Snowflake Expands Programmability to Bolster Support for AI/ML and Streaming Pipeline Development

Snowflake

That’s why at our annual user conference, Snowflake Summit 2023 , we unveiled new features that further extend data programmability in Snowflake for their language of choice, without having to compromise on governance. To make it even easier to process data with Snowpark Python UDFs and Stored Procedures, we have added support for Python 3.9

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Experts Share the 5 Pillars Transforming Data & AI in 2024

Monte Carlo

Gen AI can whip up serviceable code in moments — making it much faster to build and test data pipelines. Today’s LLMs can already process enormous amounts of unstructured data, automating much of the monotonous work of data science. But what does that mean for the roles of data engineers and data scientists going forward?