Like hardware hosts, virtual hosts in public and private clouds can develop resource bottlenecks as workloads increase. If you are using and managing InterSystems IRIS instances deployed in public or private clouds, you may have encountered a situation in which addressing performance or other issues requires increasing the capacity of an instance's host (that is, vertically scaling).

Migrating InterSystems IRIS and InterSystems IRIS for Health from on-premises to the cloud offers many advantages for Application Providers and Solution Providers. These advantages include simplified operations, access to flexible resources, and enhanced resilience. Companies no longer need to worry about the physical constraints and expenses associated with maintaining on-prem infrastructure, such as power and space requirements and expensive computer hardware.

One of the most compelling benefits is the ability to accelerate speed to market. By removing the burden of infrastructure maintenance, cloud environments enable faster development and deployment cycles, allowing businesses to respond quickly to market demands and opportunities. Operational costs are also lowered, because companies can scale resources up or down based on actual needs, leading to more efficient use of capital. Moreover, migrating to the cloud can contribute to a reduced carbon footprint by optimizing energy usage through shared cloud infrastructure.

Transitioning to the cloud may involve significant changes. Companies may benefit from a more operational focus, managing and optimizing cloud resources continuously. This shift may require changes to business models, reconsideration of margins, and strategies for scaling operations up or out. While requiring more investment, embracing these changes can lead to improved agility and competitive advantage in the marketplace.

Overview

Predictable storage IO performance with low latency is vital to provide scalability and reliability for your applications. This set of benchmarks is to inform users of IRIS considering deploying applications in AWS about EBS gp3 volume performance.

Summary

• An LVM stripe can increase IOPS and throughput beyond single EBS volume performance limits.
• An LVM stripe lowers read latency.

Hi Community!

Enjoy watching the new video on InterSystems Developers YouTube, recorded by @Thomas Carroll, Cloud Market Technical Specialist at InterSystems.

How Are Containers Different From Virtual Machines?

https://www.youtube.com/embed/rXFvhDBzY8o
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Challenges of real-time AI/ML computations

We will start from the examples that we faced as Data Science practice at InterSystems:

• A “high-load” customer portal is integrated with an online recommendation system. The plan is to reconfigure promo campaigns at the level of the entire retail network (we will assume that instead of a “flat” promo campaign master there will be used a “segment-tactic” matrix). What will happen to the recommender mechanisms? What will happen to data feeds and updates into the recommender mechanisms (the volume of input data having increased 25000 times)? What will happen to recommendation rule generation setup (the need to reduce 1000 times the recommendation rule filtering threshold due to a thousandfold increase of the volume and “assortment” of the rules generated)?
• An equipment health monitoring system uses “manual” data sample feeds. Now it is connected to a SCADA system that transmits thousands of process parameter readings each second. What will happen to the monitoring system (will it be able to handle equipment health monitoring on a second-by-second basis)? What will happen once the input data receives a new bloc of several hundreds of columns with data sensor readings recently implemented in the SCADA system (will it be necessary, and for how long, to shut down the monitoring system to integrate the new sensor data in the analysis)?
• A complex of AI/ML mechanisms (recommendation, monitoring, forecasting) depend on each other’s results. How many man-hours will it take every month to adapt those AI/ML mechanisms’ functioning to changes in the input data? What is the overall “delay” in supporting business decision making by the AI/ML mechanisms (the refresh frequency of supporting information against the feed frequency of new input data)?

Hi All,

Who, in the age of digital transformation, doesn't want to reap more benefits out of any process, procedure, and resource we have? At InterSystems Solution Developers Conference (part of InterSystems Global Summit 2018) we will have sessions on how to improve the way applications are built with modern tools like Docker containers, Gitlab, Circle CI, Travis, etc., how continuous integration and continuous delivery (CI/CD) processes can help us deliver more value quickly to the end-user, and how we can start thinking about modernizing traditional applications.

Our objective

In the last article, we talked about a few starters for Django. We learned how to begin the project, ensure we have all the requisites, and make a CRUD. However, today we are going a little further.
Sometimes we need to access more complex methods, so today, we will connect IRIS to a Python environment, build a few functions and display them on a webpage. It will be similar to the last discussion, but further enough for you to make something new, even though not enough to feel lost.

Hi Developers,

Please welcome the new video specially recorded for Developer Tools programming contest:

⏯ Deploying InterSystems IRIS docker solutions to GKE cloud in 5 minutes

https://www.youtube.com/embed/-8XaMxsauio
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InterSystems Kubernetes Operator (IKO) 3.6 is now Generally Available. IKO 3.6 adds significant new functionality along with numerous bug fixes. Highlights include:

Plotting the gnSSLocation data from my Rivian R1S across Michigan with InterSystems Cloud Document and Databricks

Hi Community,

New video is already on InterSystems Developers YouTube:

⏯ Embedded Python with Interoperability

https://www.youtube.com/embed/b6Fbg0JDmPA
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If you're deploying to more than one environment/region/cloud/customer, you will inevitably encounter the issue of configuration management.

While all (or just several) of your deployments can share the same source code, some parts, such as configuration (settings, passwords) differ from deployment to deployment and must be managed somehow.

In this article, I will try to offer several tips on that topic. This article talks mainly about container deployments.

Collision Theory Confirmed