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README.md
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README.md
@ -18,3 +18,13 @@ Quartz v4 features a from-the-ground rewrite focusing on end-user extensibility
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<img src="https://cdn.jsdelivr.net/gh/jackyzha0/jackyzha0/sponsorkit/sponsors.svg" />
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</a>
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</p>
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# TODOS
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```bash
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npm i
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#npx quartz create
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# npx quartz build --serve # local serve
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npx quartz sync
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```
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@ -1,6 +1,7 @@
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## # What are the 4 Vs of Big Data?
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There are generally four characteristics that must be part of a dataset to qualify it as big data—volume, velocity, variety and veracity [link](https://bernardmarr.com/what-are-the-4-vs-of-big-data/#:~:text=There%20are%20generally%20four%20characteristics,%2C%20velocity%2C%20variety%20and%20veracity.)
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#etl
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### What is ETL
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@ -10,8 +11,8 @@ ETL provides the foundation for data analytics and machine learning workstreams.
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- Cleanse the data to improve data quality and establish consistency
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- Load data into a target database
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### Apache Beam
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#apachebeam
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### Apache Beam
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Apache Beam is an open-source, unified programming model and set of tools for building batch and streaming data processing pipelines. It provides a way to express data processing pipelines that can run on various distributed processing backends, such as Apache Spark, Apache Flink, Google Cloud Dataflow, and others. Apache Beam offers a high-level API that abstracts away the complexities of distributed data processing and allows developers to write pipeline code in a language-agnostic manner.
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The key concept in Apache Beam is the data processing pipeline, which consists of a series of transforms that are applied to input data to produce an output. A transform represents a specific operation on the data, such as filtering, mapping, aggregating, or joining. Apache Beam provides a rich set of built-in transforms, as well as the ability to create custom transforms to suit specific processing needs.
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content/Devops&DevSecOps/Index.md
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content/SoftwareEnginnering/Index.md
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# Software Engineering
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# Software Enginnering
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