A multi-model database is designed to support multiple data models against a single, integrated backend. Document, graph, relational, and key-value models are examples of data models that may be supported by a multi-model database.
For those of you who might be new to IRIS, and even those who have used Cache or IRIS for some time but want to explore beyond its usually-assumed boundaries and practices, you might want to dive into this detailed exploration of the database engine that is at its heart, and discover just what you can really do with it, going way beyond what InterSystems have done with it for you.
I logged into the demo IRIS again, and for some reason, today, I had an "Classpath is incomplete" warning message. The classpath is defined under quickstarts-multimodel-java. Here is the contents of the file :
Since I started to use internet (late 90's), I always had a CMS (content management system) present to make easier post
any information in a blog, social media or even an enterprise page. And later years putting all my code into github I
used to document it on a markdown file. Observing how easy could be persisting data into Intersystems IRIS with the
Native API I decided to make this application and force myself to forget a little of SQL and stay open to key-value database
Join our live webinar with Mike Leone, senior analyst with Enterprise Strategy Group’s Validation Services, to learn about a speed test that measures and compares the concurrent real-time data ingest and query performance of InterSystems IRIS® data platform, a leading in-memory database, a cloud relational database, and a traditional relational database.
The Learning Services Online Learning team has posted new videos to help you learn the benefits of InterSystems IRIS. Take a peek to see what you stand to gain from making the switch to InterSystems IRIS!
Image search like Google's is a nice feature that wonder me - as almost anything related to image processing.
A few months ago, InterSystems released a preview for Python Embedded. As Python has a lot of libs for deal with image processing, I decided to start my own attemptive to play with a sort of image search - a much more modest version in deed :-)
I wanted to share each of the first three episodes of our new Data Points podcast with the community here — we previously posted announcements for episodes on IntegratedML and Kubernetes — so here is our episode on InterSystems IRIS as a whole! It was great talking with @Jenny Ames about what sets IRIS apart, some of the best use cases she's seen in her years as a trainer in the field and then as an online content developer, and more. Check it out, and make sure to subscribe at the link above — Episode 4 will be released next week!
QEWD is assumed by most people to only integrate with IRIS (or Cache) via a connection through IRIS's high-performance C interface. This requires QEWD (and its Node.js environment) to be installed and configured on the same machine as IRIS.
I'm frequently asked if QEWD can run on a separate server (or servers), and access IRIS (or Cache) over a network connection. The answer is yes it can, but the information on how to set it up in this way has been admittedly a bit tricky to discover.
For the benefit of those who want to use the Document Database (DocDB) capabilities within InterSystems IRIS, and specifically the REST API it provides, I put together a PostmanCollection that provides samples for several basic calls.