#Big Data

Hello My Friends,

I have a question how to use order by %DLIST, this is my code:

SELECT

$ListToString(%DLIST(DISTINCT MRDIA_ICDCode_DR->MRCID_Code),', ' ) ICDX,

$ListToString(%DLIST(DISTINCT (MRDIA_ICDCode_DR->MRCID_Desc || ' (' || MRDIA_DiagnosisType_DR->DTYP_Code || ')')),', ' ) Diagnose

FROM SQLUser.PA_Adm

LEFT JOIN SQLUser.PA_AdmInsurance ON (PAADM_RowID = INS_ParRef AND INS_Rank = 1)

LEFT JOIN SQLUser.PA_AdmPackage ON (PAADM_RowID = PACK_ParRef)

LEFT JOIN SQLUser.MR_Adm on MRADM_ADM_DR = PAADM_RowID

TL;DR: My comment to Microsoft when I voted: Our team has implemented most of what we need for source management of Power BI Report files in Perforce. The missing piece?

Hello everyone,

I searched but didn't find anything. Could you tell me if the IRIS database has any class that natively works with blockchains?

Best Regards,
Flávio

Hi community,

The parquet file format is the evolution of the CSV file format. See the differences when you process data in the AWS using CSV and Parquet:

Do you like to have support to parquet files in the InterSystems IRIS?

In today's landscape, enterprises have grown substantially in scale, amassing vast amounts of data. This data is collected from a plethora of sources including different applications, databases, and other channels. Given the diversity and volume of this data, it's only logical for these enterprises to seek a deeper understanding of what their data entails. Some of the data can be stored in IRIS, and it can be reasonable to be able to add this data to a data lake too.

The Internet now offers many different tools for such tasks, that do not yet support IRIS, but it's achievable.

This GitHub is the simplest way to scrap using IRIS and Python, all of that already incorporated in an IRIS PRODUCTION.

From here you can build any IRIS production in full Python or in ObjectScript as this module is interoperable.

See for more information

Hey Community,

This session provides more detail about the Smart Data Fabric announcement at #VSummit'21:

⏯ Introducing the Smart Data Fabric

https://www.youtube.com/embed/00ilwHJY6B4
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Hi contestants!

We've introduced a set of bonuses for the projects for the Datasets Contest!

Here are the projects that scored it:

In the last years the data architecture and platforms focused into Big Data repositories and how toprocess it to deliver business value. From this effort many technologies were created to process tera and petabytes of data, see:

Over the last couple of weeks the Solution Architecture team has been working to finish off our 2019 workload: this included open-sourcing the Readmission Demo that was brought to HIMSS last year, so we could make it available to anyone looking for an interactive-way of exploring the tooling provided by IRIS.

Hi Community,

We're pleased to invite you to the online meetup with the winners of the InterSystems Analytics Contest!

Date & Time: Monday, January 4, 2021 – 10:00 EDT

What awaits you at this virtual Meetup?

• Our winners' bios.
• Short demos on their applications.
• An open discussion about technologies being used, bonuses, questions. Plans for the next contests.

Hey Developers,

The InterSystems Analytics Contest is over. Thank you all for participating in our exciting coding marathon!

And now it's time to announce the winners!

A storm of applause goes to these developers and their applications:

Hey Developers!

We're pleased to invite you all to the next competition of creating open-source solutions using InterSystems IRIS! Please join:

🏆 InterSystems Analytics Contest 🏆

Duration: December 7 - 27, 2020

As we all well know, InterSystems IRIS has an extensive range of tools for improving the scalability of application systems. In particular, much has been done to facilitate the parallel processing of data, including the use of parallelism in SQL query processing and the most attention-grabbing feature of IRIS: sharding. However, many mature developments that started back in Caché and have been carried over into IRIS actively use the multi-model features of this DBMS, which are understood as allowing the coexistence of different data models within a single database. For example, the HIS qMS database contains both semantic relational (electronic medical records) as well as traditional relational (interaction with PACS) and hierarchical data models (laboratory data and integration with other systems). Most of the listed models are implemented using SP.ARM's qWORD tool (a mini-DBMS that is based on direct access to globals). Therefore, unfortunately, it is not possible to use the new capabilities of parallel query processing for scaling, since these queries do not use IRIS SQL access.

Meanwhile, as the size of the database grows, most of the problems inherent to large relational databases become right for non-relational ones. So, this is a major reason why we are interested in parallel data processing as one of the tools that can be used for scaling.

In this article, I would like to discuss those aspects of parallel data processing that I have been dealing with over the years when solving tasks that are rarely mentioned in discussions of Big Data. I am going to be focusing on the technological transformation of databases, or, rather, technologies for transforming databases.