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

Snowflake

This traditional SQL-centric approach often challenged data engineers working in a Python environment, requiring context-switching and limiting the full potential of Python’s rich libraries and frameworks. This allows your applications to handle large data sets and complex workflows efficiently.

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How to Implement a Data Pipeline Using Amazon Web Services?

Analytics Vidhya

Introduction The demand for data to feed machine learning models, data science research, and time-sensitive insights is higher than ever thus, processing the data becomes complex. To make these processes efficient, data pipelines are necessary. appeared first on Analytics Vidhya.

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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! In this video, Sr.

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Building a Data Engineering Project in 20 Minutes

Simon Späti

This post focuses on practical data pipelines with examples from web-scraping real-estates, uploading them to S3 with MinIO, Spark and Delta Lake, adding some Data Science magic with Jupyter Notebooks, ingesting into Data Warehouse Apache Druid, visualising dashboards with Superset and managing everything with Dagster.

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Data Engineering Weekly #163

Data Engineering Weekly

Compliance is mandatory, with strict penalties for violations, emphasizing the importance of data scientists familiarizing themselves with the law to avoid prohibited AI uses and ensure ethical, safe AI development. It also introduces emerging standards like the Open Data Contract Standard and Data Product Descriptor Specification.

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Data Engineering Weekly #151

Data Engineering Weekly

link] Microsoft: Generative AI for Beginners Understanding Gen-AI becomes a mandatory skill for application developers and data engineers. The blog is an excellent read to understand late-arriving data, backfilling, and incremental processing complications. Lackluster AI/ML results often stem from poor data quality.

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Four Data Engineering Projects That Look Great on your CV

Towards Data Science

Data pipelines that would turn you into a decorated data professional Continue reading on Towards Data Science »