Join, Graphable.ai us for the replay of the second episode in our year-long video series Graph-Centered AppDev, covering the common pitfalls of rdbms-based applications and why and how to move to our new graph-based paradigm.
Top two session takeaways:
- There is a new and better way to design applications, using graph database at the core.
- In this particular session, you will see more specifically how approach works on the backend of the application (e.g. DB design, APIs etc)
In the replay of this episode from May 4th, we start right at the core, focusing specifically on the backend and exploring how a graph-database can make your application faster, more resilient, more powerful, and easier to upgrade. All episodes in the series are the 1st week of every month on Tuesdays @ 12:00PM ET.
Format: Roundtable discussion with some supporting slides. This particular event is not a hands-on session, but if you think that would be a good idea for future events, please reach out to firstname.lastname@example.org.
Audience: The session should be interesting to anyone who has experienced the shortcomings of rdbms-based applications, and is in a position to impact their organization’s direction for new and re-platformed apps. Graph database enthusiasts, IT managers, architects, DBAs and AppDev technical people will benefit uniquely from this content.
- It is not necessary to have seen this first session in this series “What’s wrong with your application?” since there will be a recap, but it would be helpful (replay ,here).
- No specific technical skills required, but to derive the most value from this session, you should at least understand appdev and database concepts and their implications.
Graphable delivers insightful graph database (e.g. Neo4j) / machine learning (ml) / natural language processing (nlp) projects as well as graph and Domo analytics with measurable impact. We are known for operating ethically, communicating well, and delivering on-time. With hundreds of successful projects across most industries, we thrive in the most challenging data integration and data science contexts, driving analytics success.