Data

The New Stack Context: How Many Database Joins Are Too Many?

17 Jul 2020 2:30pm, by

Welcome to The New Stack Context, a podcast where we discuss the latest news and perspectives in the world of cloud native computing. For this week’s episode, we spoke with Denise Gosnell, chief data officer at Datastax, who is a co-author of the O’Reilly book “A Practitioner’s Guide to Graph Data.” She also graciously wrote a post for us explaining why graph databases are gaining traction in the enterprise.

TNS editorial and marketing director Libby Clark hosted this episode, alongside TNS senior editor Richard MacManus, and TNS managing editor Joab Jackson.

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Graph database systems differ from the standard relational (SQL) kind in that they are engineered to more easily capture the relations across different entities. “When you’re looking at your databases, graph databases allow you to model your data more efficiently by using relationships,” Gosnell said.

You could capture that relationship information through a series of database joins of separate tables, but eventually, the complexity of this approach would make it prohibitive. “When you look at the full end-to-end complexity for using it in an application or maintaining your code, or updating edges, graph databases are going to make that a lot easier for the full lifecycle and maintenance of that application,” she said.

In our interview, we also “query” Gosnell for:

  • A definition of graph technology.
  • The intersection between graph and machine learning.
  • Advantages using a graph system over a data lake or data warehouse.
  • More info on the graph query model.
  • Use cases.
  • The promise of using graph databases with GraphQL.

Later in the show, we discussed some of the top TNS stories and posts of the week, including:

DataStax is a sponsor of The New Stack.

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