Big Data

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Loading your IRIS Data to your Google Cloud Big Query Data Warehouse and keeping it current can be a hassle with bulky Commercial Third Party Off The Shelf ETL platforms, but made dead simple using the iris2bq utility.

Let's say IRIS is contributing to workload for a Hospital system, routing DICOM images, ingesting HL7 messages,  posting FHIR resources, or pushing CCDA's to next provider in a transition of care.  Natively, IRIS persists these objects in various stages of the pipeline via the nature of the business processes and anything you included along the way.  Lets send that up to Google Big Query to augment and compliment the rest of our Data Warehouse data and ETL (Extract Transform Load) or ELT (Extract Load Transform) to our hearts desire.

A reference architecture diagram may be worth a thousand words, but 3 bullet points may work out a little bit better:

  • It exports the data from IRIS into DataFrames
  • It saves them into GCS as .avro to keep the schema along the data: this will avoid to specify/create the BigQuery table schema beforehands.
  • It starts BigQuery jobs to import those .avro into the respective BigQuery tables you specify.

 

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Greetings community. I would like to know how to migrate a BD in production to a local environment. When I have a system in production (BD Sql Server) what we do is mount a local copy to do the analysis with the data and not occupy resources of the system in production. My question is: How do you do it with Intersystems technology?

Last answer 11 days ago
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Hi all. Today we are going to upload a ML model into IRIS Manager and test it.

Note: I have done the following on Ubuntu 18.04, Apache Zeppelin 0.8.0, Python 3.6.5.

Introduction

These days many available different tools for Data Mining enable you to develop predictive models and analyze the data you have with unprecedented ease. InterSystems IRIS Data Platform provide a stable foundation for your big data and fast data applications, providing interoperability with modern DataMining tools. 

Last comment 17 May 2019
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We have been storing raw messages in a MySQL database for DR and ad hoc purposes. We are thinking of using an Ensemble instance as our data lake instead. We could segregate the source data by namespace or by global. But either way we'll want a custom global to index the data for data retrieval performance purposes.

Anyone else taking this approach? Any feedback?

Last answer 29 March 2019 Last comment 29 March 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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Apache Spark has rapidly become one of the most exciting technologies for big data analytics and machine learning. Spark is a general data processing engine created for use in clustered computing environments. Its heart is the Resilient Distributed Dataset (RDD) which represents a distributed, fault tolerant, collection of data that can be operated on in parallel across the nodes of a cluster. Spark is implemented using a combination of Java and Scala and so comes as a library that can run on any JVM.

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This is the first article of a series diving into visualization tools and analysis of time series data. Obviously we are most interested in looking at performance related data we can gather from the Caché family of products. However, as we'll see down the road, we are absolutely not limited to that. For now we are exploring python and the libraries/tools available within that ecosystem.

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