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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 #159

Data Engineering Weekly

Our hope is only with the amazing community of data practitioners who constantly support us. One thing I learned while writing Data Engineering Weekly is that persistence and consistency are the keys to success. We are so over the Big Data Era to Modern Data Stack. Was this simply too ambitious?

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Data Engineering Weekly #162

Data Engineering Weekly

Pradheep Arjunan - Shared insights on AZ's journey from on-prem to the cloud data warehouses. Google: Croissant- a metadata format for ML-ready datasets Google Research introduced Croissant, a new metadata format designed to make datasets ML-ready by standardizing the format, facilitating easier use in machine learning projects.

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Data Engineering Weekly #164

Data Engineering Weekly

Dive into Spyne's experience with: - Their search for query acceleration with pre-aggregations and caching - Developing new functionality with Open AI - Optimizing query cost with their data warehouse [link] Suresh Hasuni: Cost Optimization Strategies for Scalable Data Lakehouse Cost is the major concern as the adoption of data lakes increases.

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Data Pipeline Observability: A Model For Data Engineers

Databand.ai

Data Pipeline Observability: A Model For Data Engineers Eitan Chazbani June 29, 2023 Data pipeline observability is your ability to monitor and understand the state of a data pipeline at any time. We believe the world’s data pipelines need better data observability. To measure, but not track.

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The Symbiotic Relationship Between AI and Data Engineering

Ascend.io

The rise of generative AI is changing more than just technology; it’s reshaping our professional landscapes — and yes, data engineering is directly experiencing the impact. How does AI recalibrate the workload and priorities of data teams? How can data engineers harness the power of AI?

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dbt Core, Snowflake, and GitHub Actions: pet project for Data Engineers

Towards Data Science

Architecture Referencing Joe Reis’s “Fundamentals of Data Engineering,” let’s review our project in alignment with the defined stages of the data lifecycle: Data Engineering Lifecycle [1] Data Generation —  Google Calendar, Fivetran If you’re a Google Calendar user, chances are you’ve accumulated a wealth of data there.