#DataEngineering Best Practices — Architect robust and cost-effective data solutions in the cloud era: https://t.co/v3NP7joNuX v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: 🟠 Architect and engineer optimized data solutions in the cloud with best practices for performance and

Transformations are the real heart of the data stack - and dbt made us finally treat them that way. The golden age of dbt isn't about tooling - it's about trust. Link to the full blog post: https://t.co/ZK4qY86bmq #dataengineering #bigdata

While men Sleep … At least when the NEPs allow me to 😂😂😂… I’m grinding. I’m doing everything within my power to turn my story… I cannot fail oh #Datafam #Dataengineering

Columnar DB + JSON already sounds impossible… Yet @ClickHouseDB hits ~9,000× Postgres and ~2,500× Mongo, with columnar benefits intact (vectorized exec, compression, data skipping) #DataEngineering #ClickHouse

Flip the Script on Data Quality: Shift Left, Shift Down, and Take Control How do you engineer quality into Data and Analytics Systems? https://t.co/XtTPpu6bN7 #dataengineering #dataquality #opensource #dataobservability #dataops

Register for Subsurface NYC November 13th (Free) Talks on Agentic AI, Apache Iceberg, Apache Polaris, Apache Arrow, Data Lakehouses and much more! Details in Comments #DataLakehouse #DataEngineering #AgenticAI

I’m excited to have contributed both as an organizer and a panelist at the first ever Data Engineering Community @data_dec meetup. #DataEngineering #DECMeetUp2025 #DECLagosMeetUp2025 #BigData #CommunityBuilding #DataEngineerCommunity

Just dropped my Airflow + PostgreSQL ETL pipeline Modular DAGs, CLI validation, and reproducible workflows — all built from scratch. Check it out 👇 https://t.co/7QHP7Umi91 @LuxDevHQ @HarunMbaabu #DataEngineering

Next-level JSON Architect in action! 🚀 Generate, validate, and test dynamic JSON formats tailored for any AI agent. Perfect for ML, data interchange & config management. Saves hours in design loops and boosts AI integration efficiency. #JSON #AI #DataEngineering ✨ Here’s your

Wrapped up a real-time data pipeline using Kafka, MongoDB, and Docker Compose Producer streams employee data → Kafka → Consumer → MongoDB → all containerized and observable via Kafdrop. https://t.co/v9f8yN0bOx #Kafka #MongoDB #DockerCompose #Python #Kafdrop #DataEngineering

We did a thing: three amazing data engineers and I answered the 10 most-asked questions on r/dataengineering. These genuine insights help you understand the current state of #dataengineering. Obviously, I'm biased since I pulled together the questions & answered them myself.

🚀 Is your data warehouse slowing down dashboards & decisions? Before rebuilding, try targeted optimization. 👉 9 key strategies to cut costs & boost speed: schema, ETL, queries, compute & more. Read the guide ➡️ https://t.co/Kyi8FKtxZ3 #DataWarehouse #DataEngineering

The simpler the semantic model, the simpler everything else becomes. It must be number one, even if just for reports. This is why most are building lakehouses to drive and communicate information. #dataengineering #datamodeling https://t.co/RxoqQia9XL

Database design isn’t just about tables—it’s a process. From collection of requirements, to analysis, then design, and finally implementation. Each phase ensures data is structured, reliable, and useful for applications. #DatabaseDesign #SQL #DataEngineering #DataScience

🧵 Thread : Découvrez pourquoi Palantir Workshop révolutionne le monde des données ! Ce n'est pas juste un outil low-code, c'est la "Tour de contrôle" qui comble le fossé entre : voir un problème et le résoudre. Je suis bluffé. #Palantir: #DataEngineering 1/ Le problème

Maximize Snowflake performance & slash query time⚡ Even a 1-second delay = ~2.11% lower conversions 📉 ✅ Faster queries ✅ Lower compute cost ✅ Smarter data pipelines ✅ Agile analytics Full Snowflake tuning guide ⬇️ https://t.co/Ziz1eYb23g #Snowflake #DataEngineering

Igniting Possibilities with AI As Microsoft Ignite 2025 approaches, the theme Ignite Possibilities perfectly reflects how AI is transforming how enterprises think, build, and grow. #MicrosoftIgnite #AI #DataEngineering #DigitalTransformation #MicrosoftPartner

#DataEngineering with Scala and Spark — Build streaming & batch pipelines to process massive amounts of data: https://t.co/wLeEgef8CB v/ @PacktDataML 𝒦𝑒𝓎 𝐹𝑒𝒶𝓉𝓊𝓇𝑒𝓈: 🔴Transform data into a clean and trusted source of information for your organization using Scala

Every team’s data stack looks different. That is why IPinfo integrates natively with the platforms you already use and builds custom datasets when you need more. Learn More → https://t.co/n7J4EyZ7tT #IPdata #DevTools #DataEngineering