Lead Data Analyst
Dr. Marielle Dado
Freelance Analytics Engineer (DE)
June 8 · Online · English talk
How to make your dbt implementation successful – two sides of one coin
Introducing new tools into your company’s tech stack can be a difficult and sometimes frustrating process, especially if they fail to garner the level of enthusiasm and usage you as the implementation lead had hoped and worked for. We understand this all too well, which is why we’d like to talk about the experience of migrating to dbt from two different perspectives: that of Marielle, an analytics engineer who introduced dbt and cloud data warehousing to the tech stack of two companies and always believed in their value to data teams, and Eva, who was on the receiving end of a dbt project as a user and struggled to understand the hype about dbt and didn’t believe for a very long time that it would actually help build better analysis.
We’ve noticed that analysts and analytics engineers often have different ideas about what constitutes “better”, “faster”, and “quality” when it comes to data modeling. In addition, quite often analysts don’t really see those terms as part of their job or responsibility or don’t really know where to start to make this a part of their routine. The result is often a focus on what’s different or what will change with the new tools, which can make adoption difficult.
To address this challenge, we’d like to introduce you to a technique called instructional scaffolding. By creating a sense of familiarity between new and old methods, scaffolding can make the process of onboarding to new tools smoother and more efficient. Ultimately, our goal is to help you win friends and influence people when migrating to dbt or other new tools in your tech stack, so that you can achieve greater success in your data modeling and analysis efforts.
Eva leads the data analytics and analytics engineering team at WhereIsMyTransport, a South African scale up. Prior to that she worked in various analytics roles in Germany and the UK.