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Schedule & Run ETLs with Jupysql and GitHub Actions

KDnuggets

This blog provided you with a comprehensive overview of ETL and JupySQL, including a brief introduction to ETLs and JupySQL. We also demonstrated how to schedule an example ETL notebook via GitHub actions, which allows you to automate the process of executing ETLs and JupySQL from Jupyter.

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Introduction to ETL Pipelines for Data Scientists

Towards AI

Learn the basics of data engineering to improve your ML modelsPhoto by Mike Benna on Unsplash It is not news that developing Machine Learning algorithms requires data, often a lot of data. Collecting this data is not trivial, in fact, it is one of the most relevant and difficult parts of the entire workflow.

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Multiple Stateful Operators in Structured Streaming

databricks

In the world of data engineering, there are operations that have been used since the birth of ETL. You filter.

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The power of remote engine execution for ETL/ELT data pipelines

IBM Journey to AI blog

Two of the more popular methods, extract, transform, load (ETL ) and extract, load, transform (ELT) , are both highly performant and scalable. Data engineers build data pipelines, which are called data integration tasks or jobs, as incremental steps to perform data operations and orchestrate these data pipelines in an overall workflow.

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Navigating the World of Data Engineering: A Beginners Guide.

Towards AI

Navigating the World of Data Engineering: A Beginner’s Guide. A GLIMPSE OF DATA ENGINEERING ❤ IMAGE SOURCE: BY AUTHOR Data or data? No matter how you read or pronounce it, data always tells you a story directly or indirectly. Data engineering can be interpreted as learning the moral of the story.

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

Data Science Blog

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. It supports a holistic data model, allowing for rapid prototyping of various models.

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How to Shift from Data Science to Data Engineering

ODSC - Open Data Science

Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. In this blog post, we will discuss how you can become a data engineer if you are a data scientist.