Empower more participants / refocus energy: I spend the bulk of my time talking to organizations that are interested in adopting dbt, and the reality is that most of the teams I talk to are data engineering teams. Data engineering teams look at dbt as a way of enabling others to self-serve, which both a) makes their internal customers happier and b) frees up the data engineering teams to focus on platform capabilities rather than adding new columns to tables. (View Highlight)
Reduce shadow IT: Misalignment between IT and lines of business often results in disempowerment for the LOB, which invariably results in the shadow IT. This has become more prevalent as infrastructure products have become easier to adopt (swipe a credit card and off you go). It’s often more advantageous to find a solution that appeals to both sides of the equation, and dbt seems to provide just that. (View Highlight)
Cognitive load / context switching reduction: Python and SQL are the most common languages used by data teams these days, but data pipelines are typically mixed-and-matched frameworks that require context switching between languages to do end-to-end development. dbt reduces cognitive load by leveraging SQL for the full lifecycle. (View Highlight)
What immediately pops out are many of the spikes for the Data Scientist. Insight, in particular, is very clearly an expectation of Analysts, and more than anybody else, the Data Scientist. Reading a bit into the chart, you might interpret that Data Science is still a somewhat mysterious discipline, and for many organizations, “insight” is the currency of that function. In that regard, Analysts and Data Scientists have a lot in common, being heavily-weighted towards analysis and insight generation. (View Highlight)
Overall, Analytics Engineers appear to be a curiously hybrid role. They have significant overlap with Data Engineers in terms of the demands of technical knowledge, though there are clear domains that remain the purview of the Data Engineering team. From an activities and responsibilities perspective, Analytics Engineers actually look a lot more like Analysts and Data Scientists than they do a Data Engineer. The role appears to aim to strike a balance between technical competency and business-mindedness. (View Highlight)
