دانلود کتاب ساخت خطوط لوله ETL با پایتون

We’re living in an era where the volume of generated data is rapidly outgrowing its practicality in its unprocessed state. In order to gain valuable insights from this data, it needs to be transformed into digestible pieces of information. There is no shortage of quick and easy ways to accomplish this using numerous licensed tools on the market to create “plug-and-play” data ingestion environments. However, the data requirements of industry-level projects often exceed the capabilities of existing tools and technologies. This is because the processing capacity needed to handle large amounts of data increases exponentially, and the cost of processing also increases exponentially. As a result, it can be prohibitively expensive to process the data requirements of industry-level projects using traditional methods. This growing demand for highly customizable data processing at a reasonable price point goes hand in hand with a growing demand for skilled data engineers. Data engineers handle the extraction, transformation, and loading of data, which is commonly referred to as the Extract, Transform, and Load (ETL) process. ETL workflows, also known as ETL pipelines, enable data engineers to create customized solutions that are not only strategic but also enable developers to create flexible deployment environments that can scale up or down depending on any data requirement fluctuations that occur between pipeline runs.