Ascend.io

Don't miss out on your chance to join the industry's biggest bootcamp on agentic analytics & data engineering. https://lnkd.in/g9hiWT-u

𝗔𝗻𝗻𝗼𝘂𝗻𝗰𝗶𝗻𝗴 𝗼𝘂𝗿 𝗻𝗲𝘄 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝘄𝗶𝘁𝗵 𝗶𝗺𝗶𝗱𝗶𝗮! Data leaders are under pressure to show AI results—but the real blocker isn’t AI. It’s the data. Teams are pouring time and resources into GenAI initiatives only to hit a wall: inconsistent pipelines, scattered ownership, and systems that aren’t production-grade. That’s why Ascend is teaming up with imidia, a top-tier consultancy helping enterprises architect AI-ready data foundations. Together, we’re helping organizations: ➡️ Identify and fix blockers in their current ecosystem ➡️ Architect intelligent systems built for AI workloads ➡️ Automate data engineering with agentic workflows This partnership brings together imidia’s deep expertise in strategic data architecture with Ascend’s agentic data engineering platform, so teams can stop firefighting and start building systems that are reliable, intelligent, and ready for AI. Read more here: https://lnkd.in/gS3QHm9i

Our new event hub is live! Check out upcoming and past events designed to up-skill data teams for the agentic era. 🔗 Link in the comments

Your dbt pipeline, with AI on-call. 🤖 Join us January 21st for a 45-minute hands-on lab where you'll build, deploy, and maintain dbt models with AI agents doing the heavy lifting. We'll walk through a real analytics project where you'll: ➡️ Generate dbt models, tests, and docs from natural language ➡️ Deploy and orchestrate your pipeline with automatic dependency management ➡️ Debug and refactor models using agentic incident response By the end, you'll have a production-ready dbt project and have the tools and skills to orchestrate and automate your own projects. Save your spot: https://lnkd.in/gNeU8tfQ

We're bringing Agentic Analytics Engineering to dbt Core. 🎉 Analytics engineers spend too much time babysitting transformations, debugging dependencies, and manually orchestrating workflows. What if your dbt project could run itself—intelligently? Today we're launching our dbt Core integration in private preview. Here's what it unlocks: ✨ AI-powered transformation management Otto (our agentic data engineer) monitors your dbt models, optimizes runs, and catches issues before they cascade ⚡ Automated workflow orchestration No more manual scheduling. Your transformations run when they should, scaled how they need to be 🔄 Intelligent dependency handling Automatic incremental builds, smart refreshes, and dependency-aware execution Want early access? 👉 Read the full announcement: https://lnkd.in/gBYbE9uE 👉 Join the private preview: https://lnkd.in/gbF4Cubz

𝗬𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝘁𝗮𝗰𝗸 𝗶𝘀𝗻’𝘁 𝘄𝗵𝘆 𝘆𝗼𝘂 𝗿𝗮𝗶𝘀𝗲𝗱 𝗰𝗮𝗽𝗶𝘁𝗮𝗹. Most teams burn weeks wiring together pipelines, stitching tools, and fighting infra problems that stall product momentum... all before they can ship the features their customers actually want. Founders deserve a head start, not a detour into full-time data engineering. That’s why we’re launching Ascend’s Agentic Data Engineering for Startups program — built for early-stage teams that want a strong data foundation without slowing down product velocity. With Ascend, your team gets senior-level data engineering leverage from day one: • Agentic automation that handles the repetitive work • A platform that adapts as you grow • Zero vendor lock-in, so you don’t box yourself in early • The leader in agentic data engineering backing your stack And here’s the big one: Eligible startups (Series B and earlier) get up to $30,000 in free credits to build on Ascend. Give your team the room to build the product — not the plumbing. 𝗔𝗽𝗽𝗹𝘆 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗽𝗿𝗼𝗴𝗿𝗮𝗺: https://lnkd.in/g-7jNm-z #startups #dataengineering #founders #ai

Simon Späti has been thinking about a question that shows up in every architecture debate. A lot of teams still treat the open-source vs. closed-source question like a philosophical argument instead of a practical one. The truth? Both paths work — the real question is what your team can support long term. Open source gives you freedom. It can also give you: ➡️ upgrades that land on your calendar at the worst possible moment ➡️ glue code that lives in three repos and one person’s brain ➡️ a “quick fix” that becomes a year of maintenance ➡️ and a weekly reminder that your backlog didn’t shrink Opinionated platforms take a different approach. They give you a consistent foundation so your energy goes into building systems, not stitching them together. Simon makes a simple point: neither option is “the right one.” You pick based on constraints, goals, and how much ongoing work your team can realistically support. If you’ve ever weighed these tradeoffs, his post will feel familiar — and probably a bit relieving. Read the full post → Link in the comments #dataengineering #opensource #dataplatforms

Hands-on beats hype. Every time. We’re launching the Agentic Data & Analytics Bootcamp — a free, two-day virtual training. Over two focused days, data teams will learn to use AI-powered workflows to build production-ready data pipelines. You’ll get real experience. Sessions include: ➡️ Building agentic pipelines from ingestion through monitoring ➡️ Exploring workflows on Snowflake, Databricks, or MotherDuck ➡️ Live labs, expert-led guidance, and community learnings If your data team is ready to cut manual work, upgrade pipeline reliability, or launch AI-native data experiences this bootcamp is for you 🚀 Registration is free, and spots are limited. Reserve yours now ➡️ https://lnkd.in/g9hiWT-u

𝗛𝗼𝘄 𝗳𝗮𝘀𝘁 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗣𝗜 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲? If you’ve ever stitched together auth flows, pagination, schema mapping, and transformation logic, you know writing API connectors is… a whole experience. And most teams are buried in requests from every direction  (read: SaaS tools, internal services, partner APIs, etc etc etc) all expecting data ~yesterday~. Join us for our next hands-on lab on December 17 focused on building custom API ingestion pipelines with AI agents. In this session, you’ll walk through real examples of how engineers can keep full control while letting agents speed up the assembly work: ➡️ Generate Python that handles ingestion across multiple API endpoints ➡️ Transform your data with SQL and Python ➡️ Deploy complete API pipelines following solid DataOps practices By the end, you’ll understand how agentic assistance fits into a production workflow and how to apply it to any data source your team throws at you. Register to attend live or receive the recording. https://lnkd.in/g39nG2Fc #dataengineering #dataops #apipipelines #agentic

We're teaming up with MotherDuck to show you what happens when serverless analytics meets AI automation. 🦆 In this 45-minute hands-on lab, you'll build production-ready data pipelines that actually run themselves—from ingestion to orchestration—using MotherDuck's serverless platform and Agentic Data Engineering. We promise to limit the fluff so you get to actually build cool stuff. Here's what we'll do: ✅ Build live on MotherDuck's serverless analytics platform ✅ Work with agents to automate manual work ✅ Walk away with approaches you can deploy at scale Join us December 3rd. Spots are limited, so make sure you register for the live session or to grab the recording → https://lnkd.in/g4HBKzV4 #DataEngineering #AgenticAI #AgenticDataEngineering