Data Engineering Design Patterns (http://dedp.online). This book aims to provide a practical guide based on convergent evolution, resulting in design patterns applicable for navigating the data engineering lifecycle—timeless practices for data engineers. #dataengineering #designpatterns #book
SSP Data
Bildungswesen
Echte Neuigkeiten über das Datenökosystem. Themen: #dataengineering #bigdata #python #opensource #ETL
Info
Technischer Blog über Business Intelligence, Data Warehouse, Data Warehouse Automation, Data Engineering, Datenvisualisierung, Big Data, Data Science und mehr. Der Autor geniesst es, stets über neue und aufkommende Technologien informiert zu bleiben. Website: ssp.sh GitHub: code.sspaeti.com.
- Website
-
https://ssp.sh
Externer Link zu SSP Data
- Branche
- Bildungswesen
- Größe
- 1 Beschäftigte:r
- Hauptsitz
- Biel
- Art
- Selbständig
- Gegründet
- 2005
- Spezialgebiete
- Business Intelligence, Data Warehouse, Data Warehouse Automation, Big Data, Data Engineering, open source, python und data pipelines
Orte
-
Primär
Wegbeschreibung
Biel, 2504, CH
Beschäftigte von SSP Data
Updates
-
The new article recently teased about #GenBI
Imagine creating business dashboards by simply describing what you want to see - no more clicking through complex interfaces or writing SQL queries. This is the promise of Generative Business Intelligence (#GenBI), and I've just published an in-depth article (https://lnkd.in/e5kKDdfY) exploring this exciting new domain. Together with Michael Driscoll, we dive deep into how GenBI transforms how we interact with data, combining the power of generative AI with the robustness of BI-as-Code. To help navigate the article, I've structured it into four essential parts that take you from understanding the fundamentals to seeing GenBI in action: > 1️⃣ Understanding GenBI. What is it? How does it compare to GenAI and the evolution from traditional BI to GenBI. > 2️⃣ Analysing BI-as-Code and why it's needed for doing anything GenAI-related. The benefits of code-first analytics, a potential workflow. > 3️⃣ The core components of GenBI: Integrating the BI tools and metrics layer with a natural language interface to allow human interfaces to proceed by AI; enriched with common knowledge from LLMs or RAGs. > 4️⃣ GenBI in Action: From GenBI Prompts to generate dashboards, metrics or improve visualization - to practical GenBI implementation, to use OpenAI integration within your BI Tool to get content knowledge about the domain based on existing BI artifacts (data source, metrics, measures). One compelling aspect I found while writing the article and highlighting it is that the so-called "self-service BI" could get another boost. The unreasonably effective #humaninterface that GenBI allows could enable business users and domain experts across the organization to create new dashboards and metrics or contribute to the data stack despite their lack of deep data engineering or business intelligence knowledge. --- With the declarative foundation, these BI-as-Code tools bring to the table, we can harness the best of both worlds, making it more approachable for businesses and users with the natural interface—bridging the gap. In the article, I tried to demonstrate how we can move from manual dashboard creation and mouse-clicking (taking hours/days) to near-instantaneous generation through natural language while maintaining the benefits of version control and automation. I hope you enjoy it. I'm curious about your feedback and ideas around Generative Business Intelligence.
-
-
Updated many people in data engineering since its creation on 2023-04-17, but who is missing? 👉🏻 https://lnkd.in/eQJ-Av_T I also added other interesting curated lists: > Whitepapers: https://lnkd.in/e-wBAaWC > Books: https://lnkd.in/esmVJF78 > Learning: https://lnkd.in/ep-dNg3k > YouTube: https://lnkd.in/eaR68a75 > Blogs & Newsletters: https://lnkd.in/eg-3i8b4
-
-
Fun stuff: Rill Data uses a combination of YAML definition and OpenAI to generate metrics and dashboards. It's in the OSS version too. So cool. More to come soon; I'm writing about «BI-as-Code and the New Era of GenBI.» It should be out by early next week.
-
-
Fun to play around with the open Bluesky API, querying my post with DuckDB. It's interesting how the architecture is set up. To me, it looks like a good way of going back to the principles of open web. From big corporate social media companies back to when we owned the web. There is an excellent talk by Dan. I collected the most important here: https://lnkd.in/eSFhHrPn. Code and GitHub Gist to the above DuckDB example you can find on https://lnkd.in/exmXvzDk. Next, we need a DuckDB Community extension to natively read AT Protocol `at://`. I am looking forward to that.
-
-
What flows better? Convergent Evolution → Design Pattern ↳ Materialized View → Data Proximity Pattern ↳ dbt table → Data Proximity Pattern OR Convergent Evolution → Pattern → Design Pattern ↳ Materialized View → Cache → Data Proximity Pattern ↳ dbt table → Cache → Data Proximity Pattern ↳ data lake → ELT → Data Proximity Pattern From repeating terms to general problem-solving to best practices OR direct from repeated terms to best practices? What do you think? I prefer the latter, but I end up repeating things more than I wanted.
-
Things have taken steam over at 🦋. It seems to be a lovely place. If you came over here because of the breaking down of Twitter, or you enjoy short-form posts, it might be an excellent option to try out right now. Many lists and starter packs get you up to speed; a Chrome extension also brings Twitter followers over. I made a list you can follow if you want to follow familiar faces: https://lnkd.in/eJgWZQsA. #dataengineering #newtwitter #bsky
-
Many asked me how to get started with data engineering. I suggest solving a problem or something you are passionate about with an actual project. I collect a list of projects if you need help—get inspired and choose according to your skills. https://lnkd.in/ecEwGRqN
Open-Source Data Engineering Projects
ssp.sh