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Matei Zaharia – Spark: The Definitive Guide. Common Operations

Define Schemas manually When using Spark for production Extract, Transform, and Load (ETL), it is often a good idea to define your schemas manually, especially when working with untyped data sources like CSV and JSON, because schema inference can vary depending on the type of data that you read in. (Chambers, 2017, 66) SQL Expressions

Kleppmann – Designing Data Intensive Applications

A data-intensive application is typically built from standard building blocks that provide commonly needed functionality. For example, many applications need to: • Store data so that they, or another application, can find it again later (databases) • Remember the result of an expensive operation, to speed up reads (caches) • Allow users to search data

Empowerment for the new leaders in tech

Once a new hire is designing as a team leader of a team. One of the first challenges is how it could be possible that this new person could achieve ownership of the project and the inspiration of the team members. Companies have been talking about empowerment in recent years, but I couldn’t see it

Good Guidelines to improve as Software Developer

After learning the basics about programming and understanding the first steps necessary to become a competent beginner software developer, I’ve started to think. I’m trying to understand whats are the best topics to take care of being a reliable programmer. I’ve found this in Any Machine Learning Project, is a Software Project First: