A global provider of mobile telecommunications software tested the performance of InterSystems Caché and Oracle as the database in a simulated data mart application. They found Caché to be 41% faster than Oracle at building a data mart. When testing the response time to SQL queries of the data mart, Caché's performance ranged from 1.8 times to 513 times faster than Oracle.
(Originally posted by @Sascha Kisser on March 11, 2014)
The following 2 classes are a component for a webcam, and a ZEN page that incorporates it. The webcam can take a snapshot and save it to Caché:
A benchmark of a real-world application, which loads data into a data warehouse for subsequent analysis, was performed. To conduct the benchmark, one module of the Oracle-based application was replicated in Caché ObjectScript. Only about 40 person-hours of work was required to duplicate the functionality of the original module in Caché.
This article is a follow-up to the previous one on how to migrate from popular databases (like PostgreSQL and MySQL) to IRIS. We will use the same procedures utilized to migrate from PostgreSQL. However, you will see that it is even easier since the data types in MySQL are very similar to IRIS. That is why we will not need to create transformation rules in the columns.
Get the sample data to the migration process
In GitHub it is possible to download a docker-compose project to build and run 2 databases:
During the development of a container-based demo I found the need to access a fresh docker instance of IRIS image (e.g intersystems/iris-community:2020.2.0.199.0) over and over. To bypass setting passwords and loading my code repeatedly I developed this workaround.
The OKR methodology (Objectives and Key Results or Objectives and Key Results) is used by the largest companies in the world (such as Google, Netflix, Spotify, BMW, Linkedin, etc.) for agile performance management. It was created in the 1970s by Andrew Grove, president of Intel, and introduced to the general public in his famous book “High Output Management”.
Around 1998 John Doerr, one of the world's top venture capitalists, after coming into contact with Intel's OKR, introduced the model to Larry Page and Sergey Brin, who started a small company called Google.
Presenter: Tony Pepper Task: Host an application based on InterSystems’ technology in a public cloud environment Approach: Provide a checklist of things to think about before you deploy
Are you looking at hosting your applications in the public cloud? This talk will highlight what you need to think about when deploying InterSystems technology in any public cloud environment.
Content related to this session, including slides, video and additional learning content can be found here.
Providing a reliable infrastructure for rapid, unattended, automated failover
Technology Overview
Traditional availability and replication solutions often require substantial capital investments in infrastructure, deployment, configuration, software licensing, and planning. Caché Database Mirroring (Mirroring) is designed to provide an economical solution for rapid, reliable, robust, automatic failover between two Caché systems, making mirroring the ideal automatic failover high-availability solution for the enterprise.
Create Database, Namespace, REST Applications using Portal Management is an easy task. You just need a few clicks or maybe more clicks than you expect.
But, what if you traded all those clicks for a simple command-line ?!
Developing the project zpm "install isc-apptools-lockdown" implemented the ability to set the security level not only to lockdown, but also to minimum and normal.
Increasing security settings
You can replace the shared password if the password of the predefined system users has been compromised
Are you new to Ensemble? InterSystems provides several tools to learn the basics of Ensemble and get on your way to becoming an expert in the technology. Before installing Ensemble, take a look at the Ensemble Technology Overview and Getting Started with Ensemble in documentation. Respectively, these documents explain features and major components of Ensemble as well as how to install the software.
As I said in the previous article, I started to learn about FHIR for the contest, and I'd like to share an update in my application: detection of inconsistencies in FHIR data.
This data set demonstrates a basic M:N relationship between 2 tables The dataset is targeted to show a slim implementation of M:N It's no question that other implementations exist. But at significant more storage consumption.
I hope to find everyone well, and a happy 2022 to all of you!
Over the years, I've been working on a lot of different projects, and I've been able to find a lot of interesting data.
But, most of the time, the dataset that I used to work with was the customer data. When I started to join the contest in the past couple of years, I began to look for specific web datasets.
I've curated a few data by myself, but I was thinking, "This dataset is enough to help others?"
Presenter: Harry Tong Task: Load terabytes of data into a distributed environment Approach: Leverage parallelism and open-source tools
In this session, we’ll discuss how to load TBs of data into a distributed environment, leveraging parallelism and open source to reach exceptional load rates.
Content related to this session, including slides, video and additional learning content can be found here.
In my previous article I demonstrated the steps to connect to Caché from Appeon PowerBuilder by using ODBC. In this article I will demonstrated how to Retrieve data from Caché with Appeon PowerBuilder (https://www.appeon.com/products/powerbuilder) by using ODBC
According IDC, 80% of all data produced are NoSQL. See:
There are digital documents, scanned documents, online and offline texts, blob content into SQL, images, videos and audio. Imagine a Corporate Analytics initiative without all these data to analyze and support decisions?
In all the world, many projects are using techonologies to transform these NoSQL data into textual content, to allows analyze it. See:
Customers who switch to Caché from relational databases report that their average performance is up to 20 time faster, running on the same hardware, with no changes to the application. What is it about Caché that lets applications run so fast?
Hi community, I used website-analyzer - an app that uses InterSystems NLP and Crawler4J to extract all website content and do NLP on it. I limited to 200 pages and discovered this:
Top 10 Concepts - business and content topics in the InterSystems site:
Other frequency concepts, see the focus in the iris speed, scale and data value:
Presenter: Louise Parberry Task: Reduce the need for skilled developers to define and normalize data from outside sources Approach: Use the Ensemble Record Mapper and other tools to automatically define comma-delineated and fixed-width data structures
If you need skilled software developers to define and normalize data coming from business partners or customers, you probably have a backlog of unsatisfied requests. The record mapper and other tools allow analysts to define comma delimited and fixed width data structures without needing a developer and this session explains how this can be incorporated into an application to create an efficient and effective way to clear that backlog.
Content related to this session, including slides, video and additional learning content can be found here.
Presenter: Luca Ravazzolo Task: Track the status and performance of clustered environments Approach: Give examples of using modern technology to spot potential bottlenecks before they turn into problems
This session will discuss how modern technology can be used to keep track of the status and performance of your cloud clustered environments.
Content related to this session, including slides, video and additional learning content can be found here.
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