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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· Feb 13, 2023 4m read
When to use Columnar Storage

With InterSystems IRIS 2022.2, we introduced Columnar Storage as a new option for persisting your IRIS SQL tables that can boost your analytical queries by an order of magnitude. The capability is marked as experimental in 2022.2 and 2022.3, but will "graduate" to a fully supported production capability in the upcoming 2023.1 release.

The product documentation and this introductory video, already describe the differences between row storage, still the default on IRIS and used throughout our customer base, and columnar table storage and provide high-level guidance on choosing the appropriate storage layout for your use case. In this article, we'll elaborate on this subject and share some recommendations based on industry-practice modelling principles, internal testing, and feedback from Early Access Program participants.

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· Jan 10, 2023 4m read
Columnar Storage in 2022.3

As you may well remember from Global Summit 2022 or the 2022.2 launch webinar, we're releasing an exciting new capability for including in your analytics solutions on InterSystems IRIS. Columnar Storage introduces an alternative way of storing your SQL table data that offers an order-of-magnitude speedup for analytical queries. First released as an experimental feature in 2022.2, the latest 2022.3 Developer Preview includes a bunch of updates we thought were worth a quick post here.

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· Sep 13, 2022 8m read
CI/CD with IRIS SQL

In the vast and varied SQL database market, InterSystems IRIS stands out as a platform that goes way beyond just SQL, offering a seamless multimodel experience and supporting a rich set of development paradigms. Especially the advanced Object-Relational engine has helped organizations use the best-fit development approach for each facet of their data-intensive workloads, for example ingesting data through Objects and simultaneously querying it through SQL. Persistent Classes correspond to SQL tables, their properties to table columns and business logic is easily accessed using User-Defined Functions or Stored Procedures. In this article, we'll zoom in on a little bit of the magic just below the surface, and discuss how it may affect your development and deployment practices. This is an area of the product where we have plans to evolve and improve, so please don't hesitate to share your views and experiences using the comments section below.

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This is the third article in our short series around innovations in IRIS SQL that deliver a more adaptive, high-performance experience for analysts and applications querying relational data on IRIS. It may be the last article in this series for 2021.2, but we have several more enhancements lined up in this area. In this article, we'll dig a little deeper into additional table statistics we're starting to gather in this release: Histograms

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This is the second piece in our series on 2021.2 SQL enhancements delivering an adaptive, high-performance SQL experience. In this article, we'll zoom in on the innovations in gathering Table Statistics, which are of course the primary input for the Run Time Plan Choice capability we described in the previous article.

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The 2021.2 release of the InterSystems IRIS Data Platform includes many exciting new features for fast, flexible and secure development of your mission-critical applications. Embedded Python definitely takes the limelight (and for good reason!), but in SQL we've also made a massive step forward towards a more adaptive engine that gathers detailed statistical information about your table data and exploits it to deliver the best query plans. In this brief series of articles, we'll take a closer at three elements that are new in 2021.2 and work together towards this goal, starting with Run Time Plan Choice.

It's hard to figure out the right order to talk about these (you can't imagine how often I've reshuffled them in writing this article!) because they fit together in such a nice way. As such, feel free to go on a limb and read these in random order smiley.

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InterSystems IRIS 2020.1 brings a broad set of improved and new capabilities to help build important applications. In addition to the many significant performance improvements accrued through 2019.1 and 2020.1, we are introducing one of our biggest changes in recent SQL history: the Universal Query Cache. This article provides more context on its impact to SQL-based applications at a technical level.

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Running predictive models natively in an InterSystems IRIS Business Process has of course always been the goal of our PMML support, but somehow never made it into the kit because there were a few dependencies and choices that needed addressing and answering. Anyhow, thanks to some pushing and code kindly provided by @Amir Samary (Thanks again Amir!), we finally got it wrapped in a GitHub repo for your enjoyment, review and suggestions.

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With the release of InterSystems IRIS, we're also making available a nifty bit of software that allows you to get the best out of your InterSystems IRIS cluster when working with Apache Spark for data processing, machine learning and other data-heavy fun. Let's take a closer look at how we're making your life as a Data Scientist easier, as you're probably already facing tough big data challenges already, just from the influx of job offers in your inbox!

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Last week, we announced the InterSystems IRIS Data Platform, our new and comprehensive platform for all your data endeavours, whether transactional, analytics or both. We've included many of the features our customers know and loved from Caché and Ensemble, but in this article we'll shed a little more light on one of the new capabilities of the platform: SQL Sharding, a powerful new feature in our scalability story.

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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.

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This earlier article already announced the new iKnow REST APIs that are included in the 2017.1 release, but since then we've added extensive documentation for those APIs through the OpenAPI Specification (aka Swagger), which you'll find in the current 2017.1 release candidate. Without wanting to repeat much detail on how the APIs are organised, this article will show you how you can consult that elaborate documentation easily with Swagger-UI, an open source utility that reads OpenAPI specs and uses it to generate a very helpful GUI on top of your API.

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This article contains the tutorial document for a Global Summit academy session on Text Categorization and provides a helpful starting point to learn about Text Categorization and how iKnow can help you to implement Text Categorization models. This document was originally prepared by Kerry Kirkham and Max Vershinin and should work based on the sample data provided in the SAMPLES namespace.

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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.

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· Jul 4, 2016 8m read
Introduction to the iKnow REST APIs

After a five-part series on sample iKnow applications (parts 1, 2, 3, 4, 5), let's turn to a new feature coming up in 2017.1: the iKnow REST APIs, allowing you to develop rich web and mobile applications. Where iKnow's core COS APIs already had 1:1 projections in SQL and SOAP, we're now making them available through a RESTful service as well, in which we're trying to offer more functionality and richer results with fewer buttons and less method calls. This article will take you through the API in detail, explaining the basic principles we used when defining them and exploring the most important ones to get started.

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Earlier in this series, we've presented four different demo applications for iKnow, illustrating how its unique bottom-up approach allows users to explore the concepts and context of their unstructured data and then leverage these insights to implement real-world use cases. We started small and simple with core exploration through the Knowledge Portal, then organized our records according to content with the Set Analysis Demo, organized our domain knowledge using the Dictionary Builder Demo and finally build complex rules to extract nontrivial patterns from text with the Rules Builder Demo.

This time, we'll dive into a different area of the iKnow feature set: iFind. Where iKnow's core APIs are all about exploration and leveraging those results programmatically in applications and analytics, iFind is focused specifically on search scenarios in a pure SQL context. We'll be presenting a simple search portal implemented in Zen that showcases iFind's main features.

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