On December 2-6 2019 AWS organized conference called AWS re:Invent 2019 during which many announcements from AWS were made and AWS users shared their experiences with AWS platform. Recently I use Amazon Redshift extensively for Data warehousing and was particularly interested in learning more about Redshift tips and best practices.

I have watched 12 videos that are related to Redshift (some featured repeated announcements). My focus was not on new features that are in preview, but more on data modelling, query optimization, and data platform architecture (for example, usage of Redshift Spectrum or data ingestion). AWS Big Data Blog already published an article Amazon Redshift at re:Invent 2019 that lists and introduces relevant Amazon Redshift sessions.

The main video about AWS Redshift best practices is conveniently named Deep dive and best practices for Amazon Redshift (ANT418), which introduces important AWS Redshift concepts and explains many best practices. Another session that introduces lessons learned from scaling of a data analytics platform, in which AWS Redshift plays a central role is Nasdaq: From data warehouse to data lake (FSI304). It is interesting to see how introduction of Data Lake changes Redshift architecture and usage.

While watching these videos I was taking notes and then organized the notes into a mind-map with Freeplane.

Figure 1. Redshift lessons mindmap

Downloadable Freeplane mind-map file: Download mind-map