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Data Engineering patterns on the cloud
How to solve common data engineering problems with cloud services?
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About the Book
Doing data engineering without the cloud is hard to imagine nowadays. Does it mean, each cloud provider is different? Well, yes, each of them has its own specificities but it doesn't mean they're completely different. They all support several common data engineering patterns that you will find in the book classified in 7 categories: data processing, data storage, data security, data warehouse, data management, data orchestration and data transfer. This organization and the explanation of each pattern will help you understand a new cloud provider easier, whenever you come from AWS, Azure, or GCP environment.
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About the Author
Bartosz Konieczny
Bartosz Konieczny is a freelance data engineer by day and a blogger by night. In the last years he has specialized in multi-cloud data engineering by implementing Big Data pipelines on top of AWS, Azure and GCP for various industries (broadcasting, online video sharing platform, oil, worldwide dating app, cars real-time signals processing).
By night he shares his knowledge on cloud data services and Apache Spark on waitingforcode.com, Become a Better Data Engineer, and data conferences like Spark+AI Summit or Data+AI Summit. He was also nominated to the first group of Databricks Beacons.
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