With the release of InterSystems IRIS Cloud SQL, we're getting more frequent questions about how to establish secure connections over JDBC and other driver technologies. While we have nice summary and detailed documentation on the driver technologies themselves, our documentation does not go as far to describe individual client tools, such as our personal favourite DBeaver. In this article, we'll describe the steps to create a secure connection from DBeaver to your Cloud SQL deployment.

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The ideal number of table permissions to assign for your users is zero. Permissions should be granted upon sign-in based on the application used for access. For web applications, we have a simple way of doing this by appointing application roles, matching roles, and required resources in the System Management Portal.

ODBC and JDBC connections present a different problem, however, especially when third-party applications are involved. As providers of an ERP system, our customers often wish to be able to employ various software packages to integrate with or report on their data. Many of these programs are capable of running any kind of query. Yet, letting them do that can be devastating to a customer’s data.

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Article
· Jun 30, 2020 3m read
Replicating Audit Log Near Real Time
Many organisations implement centralised log management systems to separate and centralise the log data in order to e.g. automate threat detection (and response) and to comply with regulatory requirements. The primary systems of interest are the various user facing applications, but increasingly also other kinds of systems including integration platforms.
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Introduction

This article aims to explore how the FHIR-PEX system operates and was developed, leveraging the capabilities of InterSystems IRIS.

Streamlining the identification and processing of medical examinations in clinical diagnostic centers, our system aims to enhance the efficiency and accuracy of healthcare workflows. By integrating FHIR standards with InterSystems IRIS database Java-PEX, the system help healthcare professionals with validation and routing capabilities, ultimately contributing to improved decision-making and patient care.

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Intro

In a fast-paced digital era, effective communication is crucial. This article introduces a Java-based chat project, combining the strength of IRIS database and ChatGPT intelligence. Built on Java, it goes beyond real-time messaging, leveraging IRIS and ChatGPT for an enhanced chat experience. Also, the name of the project references the cultural classic - Star Wars.

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GmOwl is a solution that offers an organized and engaging learning platform. It was developed to cater to the increasing need, for learning tools providing a versatile quiz environment that meets users requirements.

The main objective of GmOwl is to deliver an user experience for individuals participating in quizzes while giving administrators comprehensive control, over content and user engagement.

GmOwl uses Java EE with MVC template, and the InterSystems IRIS database is used to store data. The InterSystems JDBC Driver is used to connect to the database.

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As a former JAVA developer it has always been a challenge to decide which database was the most suitable for the project we were going to develop, one of the main criteria I used was their performance, as well as their HA configuration capabilities ( high availability). Well, now is the time to put IRIS to the test with respect to some of the most commonly used databases, so I've decided to create a small Java project based on SpringBoot that connects via JDBC with a MySQL database, another of PostgreSQL and finally with IRIS.

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Several steps should be done in order to secure the connection through xDBC clients to an IRIS Server instance using TLS. Most of the information can be obtained from the documentation about TLS on IRIS here, about configuring the security layer for encrypted connections. In the next paragraphs we will cover an step-by-step guide on how to configure and test the connection using SQL Clients apps using ODBC and JDBC.

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In this article, we will establish an encrypted JDBC connection between Tableau Desktop and InterSystems IRIS database using a JDBC driver.
While documentation on configuring TLS with Java clients covers all possible topics on establishing an encrypted JDBC connection, configuring it with Tableau might be a little bit tricky, so I decided to write it down.

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WIN SQL is the normal editor used by most of the users .But we can't download large amount of data using winsql . So I have written a tutorial how to connect with a new Java based editor called Squirrel SQL which can easily download or export data in excel or any other formats. Also I included a Java JCBC connection program to connect with the IRIS database especially a mirroring/failover server.

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Dear community members!

A very common problem of our users is to use an external database as data source in an IRIS production. As many of you already know, we have two ways to connect directly to an external database, the first one is using an ODBC connection, the second is using JDBC.

In our example we are going to create a connection using JDBC, and we are going to build a simple Docker's project, in this way you will be able to modify the example as you wish.

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The last days I've work with the great new feature: LOAD DATA With this post I would like to share my first experiences with you. The following points do not contain any order or other evaluation. These are only things that I noticed when using the LOAD DATA command. It should also be noted that these points are based on the IRIS Version 2021.2.0.617 which is a preview release. So it may be that my observations do not apply to newer IRIS versions.

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I just wrote up a quick sample to help a colleague load data into IRIS from R using RJDBC, and figured it's worth sharing here for future reference.

Ultimately it was pretty simple, aside from IRIS not liking "." in column names; the workaround is to just rename the columns. Someone better at R than me could probably provide some generic approach. smiley

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Article
· Nov 14, 2016 14m read
Mastering the JDBC SQL Gateway

As we all know, Caché is a great database that accomplishes lots of tasks within itself. However, what do you do when you need to access an external database? One way is to use the Caché SQL Gateway via JDBC. In this article, my goal is to answer the following questions to help you familiarize yourself with the technology and debug some common problems.

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Astronomers’ tools

5 years ago, on December 19, 2013, the ESA launched an orbital telescope called Gaia. Learn more about the Gaia mission on the official website of the European Space Agency or in the article by Vitaly Egorov (Billion pixels for a billion stars).

However, few people know what technology the agency chose for storing and processing the data collected by Gaia. Two years before the launch, in 2011, the developers were considering a number of candidates (see “Astrostatistics and Data Mining” by Luis Manuel Sarro, Laurent Eyer, William O’Mullane, Joris De Ridder, pp. 111-112):

Comparing the technologies side-by-side produced the following results (source):

Technology Time
DB2 13min55s
PostgreSQL 8 14min50s
PostgreSQL 9 6min50s
Hadoop 3min37s
Cassandra 3min37s
Caché 2min25s

The first four will probably sound familiar even to schoolchildren. But what is Caché XEP?

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Article
· Apr 9, 2019 3m read
IRIS/Ensemble as an ETL

IRIS and Ensemble are designed to act as an ESB/EAI. This mean they are build to process lots of small messages.

But some times, in real life we have to use them as ETL. The down side is not that they can't do so, but it can take a long time to process millions of row at once.

To improve performance, I have created a new SQLOutboundAdaptor who only works with JDBC.

BatchSqlOutboundAdapter

Extend EnsLib.SQL.OutboundAdapter to add batch batch and fetch support on JDBC connection.

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One of our apps uses a class query to support a ZEN Report and works just fine in that report, producing the expected results every time. We’ve since migrated to InterSystems Reports and noticed that, for a report using the same class query, 100s of extra rows with the same column values appear at its bottom.

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DataGrip is a multi-engine database environment targeting the specific needs of professional SQL developers, DataGrip makes working with databases an enjoyable and productive experience.

To work with InterSystems IRIS from DataGrip you'll need to add InterSystems JDBC driver first (once per DataGrip) and after that add all your InterSystems IRIS connections.

Part 1: Add InterSystems IRIS JDBC Driver

1. Go To File → DataSources

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Many times it is necessary copy or send files to your docker container instance.

In my case was with IRIS JDBC driver.

Docker has this recipe for this (credits to https://docs.docker.com/engine/reference/commandline/cp/):

docker cp [OPTIONS] CONTAINER:SRC_PATH DEST_PATH|-
docker cp [OPTIONS] SRC_PATH|- CONTAINER:DEST_PATH

But to copy you need your container name. Write this command for this:

docker ps

In my, my-iris is the container name.

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The Caché System Management Portal includes a robust web-based SQL query tool, but for some applications it’s more convenient to use a dedicated SQL client installed on a user’s PC.

SQuirreL SQL is a well known open source SQL client built in Java, which uses JDBC to connect to a DBMS. As such, we can configure SQuirreL to connect to Caché using the Caché JDBC driver.

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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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I've asked a lot of questions leading up to this, so I wanted to share some of my progress.

The blue line represents the number of messages processed. The background color represents the average response time. You can see ticks for each hour (and bigger ticks for each day). Hovering over any point in the graph will show you the numbers for that period in time.

This is super useful for "at a glance" performance monitoring as well as establishing patterns in our utilization.

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