#Cloud

Presenter: Mark Bolinsky
Task: Provide failover for distributed systems without using a VIP
Approach: Demonstrate using InterSystems’ database mirroring with external traffic managers such as F5 LTM/GTM

With distributed environments and even public cloud environments, the use of a VIP sometimes is not desirable or even possible given network topology or deployment. The session will demonstrate integrating database mirroring with external traffic managers such F5 LTM/GTM using API based triggers in InterSystems products to interface with the F5 appliances. This not only presents automated redirection for the local mirror members, but also provided automated client redirection to asynchronous DR mirror members.

Content related to this session, including slides, video and additional learning content can be found here.

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)?

These days the vast majority of applications are deployed on public cloud services. There are multiple advantages, including the reduction in human and material resources needed, the ability to grow quickly and cheaply, greater availability, reliability, elastic scalability, and options to improve the protection of digital assets. One of the most favored options is the Google Cloud. It lets us deploy our applications using virtual machines (Compute Engine), Docker containers (Cloud Run), or Kubernetes (Kubernetes Engine). The first one does not use Docker.

Accessing an Azure cloud storage to upload/download blobs is quite easy using the designated %Net.Cloud.Storage.Client class API methods, or using the EnsLib.CloudStorage.* inbound/outbound adaptors.

Note that you'll need to have the %JavaServer External Language Server up and running to use the cloud storage API or adaptors, since they both use the PEX framework using the Java Server.

Here is a quick summary:

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.

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

This summer the Database Platforms department here at InterSystems tried out a new approach to our internship program. We hired 10 bright students from some of the top colleges in the US and gave them the autonomy to create their own projects which would show off some of the new features of the InterSystems IRIS Data Platform. The team consisting of Ruchi Asthana, Nathaniel Brennan, and Zhe “Lily” Wang used this opportunity to develop a smart review analysis engine, which they named Lumière. As they explain:

Hi All,

With this article, I would like to show you how easily and dynamically System Alerting and Monitoring (or SAM for short) can be configured. The use case could be that of a fast and agile CI/CD provisioning pipeline where you want to run your unit-tests but also stress-tests and you would want to quickly be able to see if those tests are successful or how they are stressing the systems and your application (the InterSystems IRIS backend SAM API is extendable for your APM implementation).

In this series of articles, I'd like to present and discuss several possible approaches toward software development with InterSystems technologies and GitLab. I will cover such topics as:

• Git 101
• Git flow (development process)
• GitLab installation
• GitLab Workflow
• Continuous Delivery
• GitLab installation and configuration
• GitLab CI/CD
• Why containers?
• Containers infrastructure
• CD using containers
• CD using ICM

In this article, we'll build Continuous Delivery with InterSystems Cloud Manager. ICM is a cloud provisioning and deployment solution for applications based on InterSystems IRIS. It allows you to define the desired deployment configuration and ICM would provision it automatically. For more information take a look at First Look: ICM.

Google Cloud Platform (GCP) provides a feature rich environment for Infrastructure-as-a-Service (IaaS) as a cloud offering fully capable of supporting all of InterSystems products including the latest InterSystems IRIS Data Platform. Care must be taken, as with any platform or deployment model, to ensure all aspects of an environment are considered such as performance, availability, operations, and management procedures. Specifics of each of those areas will be covered in this article.