All Hands on Data #56
I thought rust only took over old cars. Could it take over data engineering as well? Check that article out along with the rest of the team's favorites below!
Will Rust Take over Data Engineering?
Although Python and SQL have long been, and continue to be for the foreseeable future, the favored tools of the data practitioner, the author makes the case for Rust. Features like type safety and compile-time checks make for a more robust experience despite the steeper learning curve. I still love me my Go, though. - John Forstmeier
Databases for the Era of Artificial Intelligence
The author offers an intriguing exploration on how vector databases are transforming the frontier of AI and big data. By delving into their role in managing unstructured data, it provides a fascinating look at a largely untapped resource. The use of real-world examples helps illustrate the practical benefits and applications of this technology, such as improved digital experiences. Lastly, the article gives compelling insights into the potential of vector databases to advance AI towards a more human-like cognition, paving the way for breakthroughs in the field. - Jon Davidson
Are You a Data Ticket Taker or Decision Maker?
Barr talks about shifting your data team from reactive to proactive. A shift that many teams strive for but often fall short. However, it is an important endeavor because proactive teams can present their work as investments rather than costs. Leaving data value creation responsibilities to the experts means growth and success for any organization. - Katt Baum
I've talked in the past about how data teams need to be forward-thinking drivers of the business, not centers for RequestOps. The latter results in the team being seen as a cost center instead of a value driver. Barr does a wonderful job at laying out four different facets that define if a data team is being reactive or proactive and how you can shift the data team's work to avoid being too reactive.
I particularly resonated with the ideas around proactive alerts about data issues to build trust and Frankensteined open-source solutions! - Blake Burch
Conceptual vs Logical vs Physical Data Models
The article does a great job of digging into the details of different forms of data modeling, including helpful diagrams. It also highlights the continued importance of understanding how and why to model your data. There has been talk in the industry that the concept of needing to model your data is decreasing. However, this article highlights its importance for creating trust and reliability between the business and data teams. - Arynn Martin-Post
Power BI vs Tableau: Comparing the Titans of Data Visualization
I started my career as a BI Analyst, so I definitely have a soft spot for BI tools. Gabe does a great job hitting the high points of comparing Power BI and Tableau if you are looking for a new BI tool this would be a great place to start. - Steven Johnson