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#dataengineering

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@hynek released another great video on uv, where he explained how he uses the just tool to store commands in a cross‑platform, portable way for everyday tasks like installing or refreshing virtual environments, running tests and code checks and even development tasks like sending requests.

youtube.com/watch?v=TiBIjouDGuI

🧙‍♂️ One does not simply build reports on OLTP data…

This week on The Drill Down with Ahmad & James, our special guest
Kristyna Ferris will be presenting a session titled "The Fellowship of the Star Schema: Transforming OLTP Data for Power BI"

🛠️ This session is packed with:
- Clear distinctions between OLTP & OLAP
- Tips for building Power BI-ready models
- A sprinkle of Slowly Changing Dimension magic

💡Whether you’re a data wizard 🧙, business hobbit 🧝‍♀️, or SQL ranger 🏹 — this is your quest.

🗓️ Join us LIVE on LinkedIn | Wednesday, July 2nd @ 2PM Central
lnkd.in/eWh4SsBb

pro tip for user interface designers:

if you have hundreds of millions of dollars of venture capital and you want to make a user facing data analytics tool of some kind and you think it's reasonable to ask an average human being to type this:

CAST('2023-05-01' AS TIMESTAMP)

to do literally anything with a date or time in your application's user interface, just stop right there. do not pass go, do not collect $200, and do not ever attempt to offer feedback to a UX designer ever again. something is deeply broken inside you that means there are certain mysteries of the universe that even the guys who designed the postgres command line can access that you will never know, and that's ok. You can still live a really rad life.

The problem: Setting up GA + BigQuery = 40+ manual steps, delayed insights, expensive queries

Our solution:

Managed Warehouse with:

One-click deployment
Real-time ClickHouse backend
Usage-based pricing
Built-in feature flag analytics

First 2M events/month free for Pro users. Raw SQL access maintained for power users.
Self-hosters: We're working on bringing Feature Usage Analytics to on-prem deployments too 👀
#OpenSource #DataEngineering #ClickHouse

Rust is transforming data engineering by offering unparalleled performance and cost efficiency. Singular's Extract platform, powered by Rust, achieves 17x performance improvements and up to 70% cost reductions. With memory safety and modern design, Rust is becoming the go-to for data-intensive workloads. Learn how Rust is outperforming Python and Java in enterprise data pipelines. #RustLang #DataEngineering #TechAdvancements #Programming"

thenewstack.io/rust-eats-pytho

The New Stack · Rust Eats Python's, Java’s Lunch in Data EngineeringUsing Rust for data pipeline operations provides significant performance and cost improvements over traditional Python and Java solutions.

The traditional ETL-Process consist of Extract-Transform-Load. But in tools like Data Cloud from Salesforce there is now the Zero-ETL technology integrated: Instead of requiring these 3 traditional steps, data should now flow seamlessly between different systems.

So, what's new? The data from different systems can be used almost in real-time. There is no need to move data :blobcoffee: towardsdatascience.com/why-etl

#DataEngineering #data #datascience #database #salesforce #DataIntegrationDatabasesEtl #etl #python

Comment for the friend link of the Medium article and I will send you the link in a message.

Towards Data Science · Why ETL-Zero? Understanding the shift in Data Integration | Towards Data ScienceWhen I was preparing for the Salesforce Data Cloud certification, I came across the term Zero-ETL. The Data Cloud offers the possibility...