Distributed Data Management

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 For Data Synchronization inside Caché you have a range of ways to synchronize objects and tables.
At DB level you can use Shadowing  or Mirroring 

This works excellent and if you need just a part of your data to be synchronized you may split your
data into smaller pieces using Global mapping 
Or if you need bi-directional synchronization on Class/Table level you can use the Object Synchronization Feature 


The limit of all these excellent features:
They just work from Caché/IRIS to Caché/IRIS.

Last comment 8 April 2019
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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!

Last comment 17 May 2018
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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
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Learning Services Live Webinars are back! 

At this year’s Global Summit, InterSystems debuted InterSystems IRIS Data Platform™, a single, comprehensive product that provides capabilities spanning data management, interoperability, transaction processing, and analytics. InterSystems IRIS sets a new level of performance for the rapid development and deployment of data-rich and mission-critical applications. Now is your chance to learn more! 

Joe Lichtenberg, Director of Product and Industry Marketing for InterSystems, presents "Introducing InterSystems IRIS Data Platform", a high-level description of the business drivers and capabilities behind InterSystems IRIS.

Webinar recording available now!

Last comment 4 November 2017
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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?

Is there any documentation of article which might help understanding?


Thanks You!

Last answer 14 September 2017 Last comment 14 September 2017
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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. Time passed, BigData is not a buzz anymore (most of us missed the moment when Gartner has removed BigData term from their 2016 buzzword 2016 curve http://www.kdnuggets.com/2015/08/gartner-2015-hype-cycle-big-data-is-out-machine-learning-is-in.html), so it’s probably a good time to look back and realize what it is (what it was)…

When it becomes “BigData”?

Let’s start from the beginning: what is the moment when “not so big data” becomes BigData? Here was the answer in 2015 from David Kanter[1], one of most respected, well known x86 architecture specialist

Last comment 28 March 2017
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In part I of this series we have introduced MapReduce as a generic concept, and in part II we started to approach Caché ObjectScript implementation via introducing abstract interfaces. Now we will try to provide more concrete examples of applications using MapReduce.

Last comment 28 March 2017
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Good day

Additional tool for MUMPS servers MiniM InterConnect:

http://www.minimdb.com/tools/interconnect.html

to connect job on one MUMPS server to other job on other MUMPS server in client-server mode. This tool can connect different MUMPS systems on different OS and with different processors - MiniM, Cache and GT.M in any pairs.

Regards,

Eugene Karataev

Last comment 27 November 2016
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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
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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.

I wonder if we could use Shadowing between those to keep the data on the new server up to date?

We would copy the Caché backup from the old environment to the new and do a RESTORE there and then start shadowing.

I know than 5.0.21 is no more officially supported by ISC.

Last answer 26 October 2016 Last comment 28 October 2016
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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).  

The current Caché documentation here demonstrates this well using object synchronization between two servers, however it doesn't indicate whether more than two servers can participate to create a "mesh type" deployment.  Below is a diagram of what I'm curious to know is possible to implement with Object Synchronization.

Last answer 19 May 2016 Last comment 19 May 2016
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