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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.

ETL 291
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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.

ETL 246
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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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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.

ETL 99
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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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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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Why using Infrastructure as Code for developing Cloud-based Data Warehouse Systems?

Data Science Blog

So why using IaC for Cloud Data Infrastructures? For Data Warehouse Systems that often require powerful (and expensive) computing resources, this level of control can translate into significant cost savings. This brings reliability to data ETL (Extract, Transform, Load) processes, query performances, and other critical data operations.