Yasuhisa Yoshida
About
I currently work as a data engineer at a startup called 10X. Specifically, I work with BigQuery to provide data marts for business users. Before using dbt, the queries for creating data marts were overly complex and lengthy, resulting in low data quality. With dbt, we have improved our process by breaking down queries into manageable parts, visualizing data lineage, and enabling easy creation of tests. I am actively involved in the dbt community and share our insights on using dbt at #local-tokyo. Specifically, I shared our experiences with efficient metadata management using dbt-osmosis, and visualizing data quality using elementary.
When did you join the dbt community and in what way has it impacted your career?
I joined dbt's Slack at the end of 2021. I often follow dbt's Slack because it offers insights into dbt that I could not have gained on my own.
I also enjoy presenting my company's findings at the Tokyo dbt Meetup and receiving feedback from dbt community members outside my company. Participating in the dbt community frequently sparks ideas on how to enhance my company's data infrastructure.
What dbt community leader do you identify with? How are you looking to grow your leadership in the dbt community?
Although dbt is already a widely used tool, it is still young and actively being developed. Therefore, there may occasionally be times when there are small bugs, or you might not understand how to use it.
In such cases, you can ask questions on the dbt Slack or submit an issue on GitHub. These actions can lead to a direct solution to the problem and often help other community members who are struggling with similar issues—you are not the only one! I have been helped many times by seeing these types of questions from others on dbt Slack and GitHub issues.
Of course, answering questions and submitting pull requests on GitHub is also a great way to contribute to the community. However, starting with small contributions is perfectly fine. I would like to continue doing these kinds of activities to make the community more active.
What have you learned from community members? What do you hope others can learn from you?
dbt is widely used by various types of businesses, from startups needing fast development to enterprises requiring robust data quality. Different business phases and industries have diverse data requirements.
In the dbt community, members actively share best practices and lessons from failures, helping you adapt dbt to your company's needs. Having observed many such use cases, we have learned to deliver value to business users through dimensional modeling and tools like AutomateDV.
I am confident that you can find use cases on dbt Slack that match your company’s business needs, and I encourage you to share the practices and insights you gain from using these tools with the community.
Anything else interesting you want to tell us?
dbt and the surrounding ecosystem are powerful allies for data engineers and analytics engineers. Let's work together as dbt community members to further enhance these tools and build a better world!