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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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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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Rewiring My Career: How I Transitioned from Electrical Engineering to Data Engineering

Towards Data Science

It is worth mentioning that this article comes from an Electrical and Electronic Engineer graduate who went all the way and spent almost 8 years in academia learning about the Energy sector (and when I say all the way, I mean from a bachelor degree to a PhD and postdoc). Similarly, data engineering positions have seen a 98% increase.

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7 Data Pipeline Examples: ETL, Data Science, eCommerce, and More

Databand.ai

7 Data Pipeline Examples: ETL, Data Science, eCommerce, and More Joseph Arnold July 6, 2023 What Are Data Pipelines? Data pipelines are a series of data processing steps that enable the flow and transformation of raw data into valuable insights for businesses.

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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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CI/CD for Data Pipelines: A Game-Changer with AnalyticsCreator

Data Science Blog: Data Engineering

Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines: It is a Game-Changer with AnalyticsCreator! The need for efficient and reliable data pipelines is paramount in data science and data engineering. They transform data into a consistent format for users to consume.

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Data Pipeline Orchestration

Towards Data Science

Data pipeline management done right simplifies deployment and increases the availability and accessibility of data for analytics Continue reading on Towards Data Science »