Summary of The accidental analytics engineer

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00:00:00 - 00:25:00

The speaker describes their personal experience transitioning from working as a data scientist to working as an analytics engineer, and provides three tips for those making a similar transition. They emphasize the importance of understanding the differences between the tidyverse and the modern data stack, and recommend DBT as a tool for data scientists who want to learn more about analytics engineering.

  • 00:00:00 Michael Chow discusses how data scientists can identify when they need to transition into analytics engineering, provides three quick tips for surviving in this field, and shares his experiences as a cognitive psychologist working on data tools.
  • 00:05:00 The speaker describes how, over the course of two years, they gradually transitioned from working as a data scientist to working as an analytics engineer, and how this transition led to them hitting a "hole" in their understanding of the field. They eventually discovered DBT, which helped them to see the bigger picture and prevent them from falling into the same trap as other analytics engineers.
  • 00:10:00 The video discusses the two different world views that data scientists may have when approaching analytics engineering, the Tidy versus the Modern Data Stack perspective. The Tidy World View is based around the book "R for Data Science" and focuses on the workflow of data import, data tidy-up, engagement in an Arc of Understanding, and producing insights. The Modern Data Stack World View is based around tools such as DAPLER and focuses on the analysis of data frames in SQL databases. Both perspectives have their strengths and weaknesses, and the video provides an overview of how the data scientist might learn about analytics engineering by first encountering it in practice.
  • 00:15:00 The video discusses the differences between the "tidyverse" and the "modern data stack" in terms of how they approach data analysis. The tidyverse is focused on pulling raw data and producing insights, while the modern data stack is focused on scaling up and maintaining functionality. The modern data stack also has analysts at the end who are able to visualize and model data.
  • 00:20:00 The accidental analytics engineer gives a quick overview of the gap between data scientists and analysts, and how DBT can help bridge the divide. He then goes on to introduce the concepts of snapshots and data modeling, and provides an introduction to analytics engineering.
  • 00:25:00 The speaker discusses how data comes in and goes out and how to identify the Gap between the two data science cultures. She recommends small things that would have helped her in getting started, such as knowing when data last came in and where it is going to go. She also mentions Teddy Tuesday, which is a style of data analysis where analysts move quickly.

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