With the release of InterSystems IRIS, we're also making available a nifty bit of software that allows you to get the best out of your InterSystems IRIS cluster when working with Apache Spark for data processing, machine learning and other data-heavy fun. Let's take a closer look at how we're making your life as a Data Scientist easier, as you're probably already facing tough big data challenges already, just from the influx of job offers in your inbox!
Distributed Data Management
Analytics, Distributed Data Management, ECP (Distributed Data Management), InterSystems IRIS (Distributed Data Management), Machine Learning (Distributed Data Management), Sharding (Distributed Data Management), SQL (Distributed Data Management)
Last week, we announced the InterSystems IRIS Data Platform, our new and comprehensive platform for all your data endeavours, whether transactional, analytics or both. We've included many of the features our customers know and loved from Caché and Ensemble, but in this article we'll shed a little more light on one of the new capabilities of the platform: SQL Sharding, a powerful new feature in our scalability story.
Last comment 31 January 2018
Analytics, Distributed Data Management, Webinar, Interoperability, InterSystems IRIS, Online Learning, Database Transaction Processing
Learning Services Live Webinars are back!
Last comment 4 November 2017
While creating namespaces, there is a provision for keeping the data, and code separately in database.
What I am looking for,
What are the benefit we can achieve by doing this?
Last answer 14 September 2017 Last comment 14 September 2017
Caché, Data Model, Distributed Data Management, Application Server (Distributed Data Management), C++ (Distributed Data Management), Machine Learning (Distributed Data Management), Parallel (Distributed Data Management)
Several years ago everyone got mad about BigData – nobody knew when smallish data will become BIGDATA, but all knows that it’s trendy and the way to go.
Last comment 28 March 2017
The technology of load balancing between several servers with relatively low capacity has been a standard feature of Caché for quite a while. It is based on the distributed cache technology called ECP (Enterprise Cache Protocol). ECP provides a host of possibilities for horizontal scaling of an application, and yet keeping the project budget fairly low. Another apparent advantage of ECP network is the possibility to conceal its architecture in the depths of Caché configuration so that applications developed for the traditional (vertical) architecture can be fairly easily migrated to a horizontal ECP environment. The ease of this process is so mesmerizing, that you start wishing it was always this way. For instance, everybody is used to having a possibility to control Caché processes: the $Job system variable and associated classes/functions work magic in skilful hands. Stop, but now processes can end up being on different Caché servers…
This article is about how to gain as much transparency in controlling processes in ECP environment as in traditional (non ECP) one.
Last comment 18 November 2016
We are finally planning to migrate some ancient Caché applications that are run on Caché 5.0.21 to a new server with Caché 2016.2.0 or so.
Last answer 26 October 2016 Last comment 28 October 2016
Consider a design where there could be three or four or more servers and there is a need to have these eventually consistent between them all (and not considering database mirroring here).
Last answer 19 May 2016 Last comment 19 May 2016