During recent large scale benchmarking activities, we were seeing excessive %sys CPU time that negatively impacted scaling of the application.

Problem

Hi all. Today we are going to install Jupyter Notebook and connect it to Apache Spark and InterSystems IRIS.

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

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.

DeepSeeButtons is available on Open Exchange! This tool will generate a diagnostic report of your DeepSee environment.

Since my initial question on ordering json properties a few things have happened.

In an earlier article (hope, you’ve read it), we took a look at the CircleCI deployment system, which integrates perfectly with GitHub. Why then would we want to look any further? Well, GitHub has its own CI/CD platform called GitHub Actions, which is worth exploring. With GitHub Actions, you don’t need to rely on some external, albeit cool, service.

In this article we’re going to try using GitHub Actions to deploy the server part of InterSystems Package Manager, ZPM-registry, on Google Kubernetes Engine (GKE).

Continuing on with providing some examples of various storage technologies and their performance profiles, this time we looked at the growing trend of leveraging internal commodity-based server storage, specifically the new HPE Cloudline 3150 Gen10 AMD processor-based single socket servers with two 3.2TB Samsung PM1725a NVMe drives.

In the previous article we saw how we could recover a resource stored in the database of our particular HIS, so today we will see how we can add new records in our HIS whose origin is an FHIR resource that we receive in our system.

I am planning to implement Business Intelligence based on the data in my instances. What is the best way to set up my databases and environment to use DeepSee?

If you've worked with iKnow domain definitions, you know they allow you to easily define multiple data locations iKnow needs to fetch its data from when building a domain. If you've worked with DeepSee cube definitions, you'll know how they tie your cube to a source table and allow you to not just build your cube, but also synchronize it, only updating the facts that actually changed since the last time you built or synced the cube. As iKnow also supports loading from non-table data sources like files, globals and RSS feeds, the same tight synchronization link doesn't come out of the box. In this article, we'll explore two approaches for modelling DeepSee-like synchronization from table data locations using callbacks and other features of the iKnow domain definition infrastructure.

We return with our example of using the FHIR Adapter, in this article we are going to review how we can configure it in our IRIS instances and what the result of the installation is.

The steps taken to configure the project are the same as indicated in the official documentation, you can review them directly here. Well, let's get to work!

In previous articles on iKnow, we described a number of demo applications (iKnow demo apps parts 1, 2, 3, 4 & 5) that are either part of the regular kit or can be easily installed from GitHub. All of those applications assumed you already had your iKnow domain ready, with your data of interest loaded and ready for exploration. In this article, we'll shed more light on how exactly you can get to that stage: how you define and then build a domain.

InterSystems IRIS Business Intelligence allows you to keep your cubes up to date in multiple ways. This article will cover building vs synchronizing. There are also ways to manually keep cubes up to date, but these are very special cases and almost always cubes are kept current by building or synchronizing.

Database systems have very specific backup requirements that in enterprise deployments require forethought and planning. For database systems, the operational goal of a backup solution is to create a copy of the data in a state that is equivalent to when application is shut down gracefully. Application consistent backups meet these requirements and Caché provides a set of APIs that facilitate the integration with external solutions to achieve this level of backup consistency.