Trino is a highly parallel and distributed query engine, that is built from the ground up for efficient, low latency analytics.
Access data from multiple systems within a single query. For example, join historic log data stored in an S3 object storage with customer data stored in a MySQL relational database.
a community of Powershell lovers, who are invested in making Atlassian product part of their Powershell life
Some links about the topic from title
https://github.com/andreas5588/openflights_dataset
https://community.intersystems.com/post/tips-and-tricks-brand-new-load-data-command
https://hub.docker.com/r/andreasschneiderixdbde/openflights-iris-zeppelin
https://hub.docker.com/r/andreasschneiderixdbde/openflights-iris
https://openexchange.intersystems.com/package/openflights_dataset
https://www.kaggle.com/datasets/sherrytp/airline-delay-analysis/code
The Million Song Dataset Challenge aims at being the best possible offline evaluation of a music recommendation system
https://www.kaggle.com/c/msdchallenge/overview
Do you want to query complex data structures in an iterative way? Do you have access to hierarchical data structures that need to be queried? This course will teach you the tools required to solve these questions. You will learn how to write recursive queries and query hierarchical data structures. To do this, you will use Common Table Expressions (CTE) and the recursion principle on a wide variety of datasets. You will, for example, dig into a flight plan dataset and learn how to find the best and cheapest connection between two airports. After completing this course, you will understand the principle of recursion, and be able to identify and create hierarchical data models.
https://www.datacamp.com/courses/hierarchical-and-recursive-queries-in-sql-server
This article aims at showing good practices on how to retrieve data with SQL using practical examples on the data above. The following topics are covered:
https://www.mit.edu/~amidi/teaching/data-science-tools/tutorial/queries-with-sql/
Have you ever wondered about the differences between a subquery and a common table expression (CTE) in SQL? The concepts seem very similar, but knowing the difference – and when to use each one – will help you write efficient and readable queries.
docker cp my-iris:/usr/irissys/dev/java/lib/1.8/ e:\temp
https://docs.docker.com/engine/reference/commandline/cp/
https://docs.intersystems.com/
InterSystems IRIS and IRIS for Health 2021.2 preview: https://community.intersystems.com/post/intersystems-iris-and-iris-health-20212-preview-published
2021.2 LOAD DATA
LOAD DATA FROM FILE filepath
[ COLUMNS (fieldname datatype,
fieldname2 datatype2, ...) ]
INTO table [ (fieldname, fieldname2, ...)
[ VALUES (headeritem,headeritem2, ...) ] ]
[ USING {json_object} ]
LOAD DATA FROM JDBC connection TABLE jtable
INTO table [ (fieldname, fieldname2, ...)
[ VALUES (jfieldname,jfieldname2, ...) ] ]https://irisdocs.intersystems.com/iris20212/csp/docbook/DocBook.UI.Page.cls?KEY=RSQL_loaddata
Two example repositories using LOAD DATA:
Fail-Fast (sinngemäß „schneller Abbruch“) bezeichnet die Eigenschaft eines Systems, Fehler frühzeitig zu erkennen. Ein Fail-Fast-System ist so konzipiert, dass es an seinen Schnittstellen Fehler oder Zustände, die zu Fehlern führen, erkennt und aufzeigt. Üblicherweise beenden sich Fail-Fast-Systeme im Fehlerfall selbst und versuchen nicht, mit dem Fehler weiterzuarbeiten
https://de.wikipedia.org/wiki/Fail-Fast
Why do computers stop and what can be done about it?
by Jim Gray
Jörg Langers aufwändige Doku zeichnet diese Entwicklung nach und lässt viele Zeitzeugen zu Wort kommen – darunter GDC-Gründer Chris Crawford und bekannte Spieldesigner wie Warren Spector oder Steve Meretzky.
The beauty of Apache Drill is that you do not need any overhead of traditional SQL database server. Just save your dataset using parquet data format then query your dataset by just pointing to it using SQL, just like any SQL database table or SAS datasets.
https://towardsdatascience.com/organize-dataset-using-apache-drill-ecdb58a19139
https://github.com/apache/zeppelin
Trino is a highly parallel and distributed query engine, that is built from the ground up for efficient, low latency analytics.
