Infinite Lambda

Super excited to share something our team at GovTech Singapore’s Data Practice has been pouring a lot of hard work into! We’re taking a big leap forward with GovTech’s AIDE (AI for Data Engineering), our initiative to shape the future of AI-driven data engineering across Singapore’s public sector. We’ll be unveiling the first of many cool projects at next Thursday's STACK Meetup [Data]! What I'm most hyped about is how this changes our daily data workflows. Instead of getting bogged down in repetitive manual pipeline builds, we’re shifting to building with modern AI capabilities so that data users can co-create pipelines directly. This frees us up as data engineers to focus on higher-value orchestration, architecture, and QA. Doing way more with less and maintaining a healthy data engineering bandwidth is our goal. My teammate Janice Ng will be sharing our practical experience building GovTech’s brand of AI-enabled data engineering. Microsoft ‘s Debananda Ghosh will dive into Fabric IQ and demonstrate how it turns enterprise data into intelligent, actionable outcomes. Michael Han from Infinite Lambda will walk us through a real-world case study of how Mandai Wildlife Group ’s legacy on-prem data warehouse was migrated to Cloud in just 1 year using AI agents. Also, make sure to come early! We’re setting up something new this August: our AI Data Engineering Booths open at 6:30pm. Come hang out with us, play a few mini-games, and score some exclusive swag! 📅 27 Aug 2026, 7:30pm (Networking & Booths start at 6:30pm) 📍 GovTech (Punggol Digital District), Level 8 @ Community 1 & 2, Tower 82 82 Punggol Way, Singapore 829910 👉🏻 Register now: https://lnkd.in/guZbTN8h Note: Registration via GovEntry only. #STACKMeetup #GovTechSG #DataStandards #AIDataEngineering #STACKCommunity #GovTechDataPractice

Data used to feel like a field you could only enter if you had a computer science degree and years of experience. Lucky for you and for the industry, that is now changing. Your non-tech background in marketing, finance, or another field comes with domain knowledge that you can leverage to start a successful career in the field. If you are looking to make a career transition into data, join our upcoming meetup to get the experts’ advice. On 22 August, we will talk with Analytics Engineer Che Duc about navigating the career change, what different data roles actually involve, and how to prepare for the interview process. The session is online, so you can join from anywhere. 🗓️ Saturday, 22 August, 7 pm to 8:30 pm 💻 Online, livestream 🇻🇳 Language: Vietnamese 🎟️ RSVP: https://lnkd.in/d3Fv6g3p Collaboration among Infinite Lambda, Foundry AI Academy, and Vit Lam Data.

This one is for you if you are shaping the AI strategy or building agentic workloads and need "AI-ready data" to mean something real. dbt Labs, Infinite Lambda, and Fivetran are hosting a webinar to unpack the Enterprise AI Data Maturity Model, a five-stage framework built from real customer examples from organisations that have deployed AI at scale. You will walk away with: → The five stages of AI readiness, and where you sit today → Real before-and-after numbers from teams that moved up a stage → Three key questions to bring back to your team on who owns closing the gap The experts: Russell Christopher (Senior Director of Product Strategy, dbt Labs) and Petyo Pahunchev (Chief Product Officer, Infinite Lambda) Pick the time that works for you: 🌎 AMER, Tuesday 2 September, 12 pm ET 🌍 EMEA, Wednesday 3 September, 1 pm BST 🌏 APAC, Wednesday 3 September, 1 pm AEST Tune in: https://lnkd.in/dXQgFdpJ Can't join us live? Register anyway, we will send the recording after. #EnterpriseAI #AItransformation

HCMC Data Meetup goes 100% online for the first time, so anyone can join us wherever they are. Vol. 32 is coming up on Saturday, 22 August, when Analytics Engineer Che Duc will: → Explore the differences among analytics engineers, data engineers, and data analysts → Show us what it takes to become an analytics engineer without a technical background. If you are in marketing, finance, accounting, or any other non-technical field and have been curious about making the move into data, this session will give you useful tips. We have an extensive Q&A planned, so bring your questions. Data Analyst Thu Pham will be moderating the session and making sure not a single question is left unanswered. 🗓️ Saturday, 22 August, 7 pm to 8:30 pm 💻 Online, livestream 🇻🇳 Language: Vietnamese 🎟️ RSVP via the link below In collaboration with Infinite Lambda, Foundry AI Academy, and Vit Lam Data. #DataMeetup #VietnamTech #DataCareers

A CEO asks how many active customers the company has. Finance gives one number, sales gives another, ops gives a third, all in the same thread. That is where most enterprises still sit today, and it is why AI pilots keep stalling before they ever reach production. 💡 Join dbt Labs and Infinite Lambda for a session on the Enterprise AI Data Maturity Model, a five-stage framework for working out where your organisation stands and what a credible route to real AI value looks like. Russell Christopher (Senior Director of Product Strategy) and Petyo Pahunchev (Chief Product Officer) will walk you through: – The five stages of the model, helping you identify where your organisation fits today – Real before-and-after numbers from teams that moved up a stage – Three key questions to bring back to your team on who owns closing the gap Choose the session that suits your region: 🌎 AMER, Tuesday 2 September, 12 pm ET 🌍 EMEA, Wednesday 3 September, 1 pm BST 🌏 APAC, Wednesday 3 September, 1 pm AEST 🔗 Tune in: https://lnkd.in/dXQgFdpJ Cannot make it live? Register anyway and we will send the recording and follow-up resources once it wraps.

Patties Food had designed a data stack for accessibility, looking to give business users the freedom to create reports independently and move quickly. As the business scaled, that same setup needed to grow with it, and this is where the challenge came from. When business users can build reports independently, the stack grows in ways nobody planned for. The same metric gets calculated differently across teams, while pipelines accumulate with no version control. By the time the data team realises the architecture has a problem, thousands of reports are drawing from it. A small data team was maintaining hundreds of tables and serving thousands of reports across the business, on a stack that had simply outgrown itself. The path forward called for modelling with dbt Labs, storage on Snowflake, and ingestion via Fivetran. Needless to say, the switch had to happen without disrupting the business in the process. The result: ⚡ Dashboard data latency down from 6 hours to 15 minutes ⚡ New data products delivered 50% faster ⚡ Data foundation for AI and ML Swipe through to see how we got there. #AImodernisation #DataMigration

The next Omni AI Analytics Meetup in London will focus on AI in the analytics workflow and is looking for its second speaker. If you work on the modern analytics stack and have an interesting use case or useful tips to share with the community, we'd love to hear from you. You would be presenting alongside Infinite Lambda's Jasim Alladin, in a relaxed and welcoming space where practitioners come to share what they know with people who do similar work. No need to be an Omni user; we are looking for real stories and challenges to discuss together. If you fancy sharing what you know with others to help them out, raising tough questions, or just venting about the challenges you are facing right now, fill out the form below or get in touch with Gergana Plamenova. #AIanalytics

Omni's app builder sits directly on top of the semantic model, which means any application you build from it inherits governed, consistent data without any separate wiring between layers. You describe what you need and the context is already there. Sai Gude, Infinite Lambda's Head of Omni, put this to the test with the following prompt: create high-level KPIs and the ability to search and filter assets. ✨ In this demo, you can see how Omni generates the boilerplate, updates the codebase, and produces a functional knowledge management application with filtering, search, and KPI views in a single session. Sai's estimate for getting something production-ready with a few more prompts and light debugging was an hour or two. 🤯 Check this out if you are building analytics apps and want to see how much of the groundwork Omni handles on its own. #AIanalytics

Analytics teams are in the middle of a role change, and most job descriptions have not caught up yet. The work has always been about making data correct through clean pipelines, well-modelled tables, and transformations that hold up under scrutiny. That foundation still matters. But AI agents are a new type of data consumer that comes with its own needs and its own users to cater to. Agents interpret the data and this interpretation requires context which even the best modelled table cannot provide on its own. Context comes from a semantic layer and looks like: ✓ Governed definitions ✓ Documented logic ✓ Explicit rules about what each metric is and what it is not The semantic layer determines whether an AI agent returns a useful answer or a confident one that happens to be wrong. The evolution of analytics engineering revolves around context. Swipe through to learn what this looks like in practice. #AIanalytics

Behind every clean migration, there is a team that actually gets on well. But let’s take a step back. The thing about legacy data debt is that it accumulates through perfectly reasonable decisions. You build a pipeline to solve an immediate problem, and add a workaround when requirements change faster than the architecture can keep up. The deadline is today, so you skip documentation. After all, everyone already knows how it works. Over time, you get a system that functions but nobody fully understands how. In this case, it takes real effort to change anything without breaking something else. Getting out of that situation is a technical problem, but it is also a cultural one. The teams that manage it well talk to each other about the work before the work becomes a problem. The engineer who built the original pipeline and the engineer who inherits it are in the same conversation. The data leader and the engineering team share a picture of where things are and where they need to go. That kind of collaboration only comes from investing in how the team works together. We speak from experience when we say that modernisations surface all of this quickly. The technical decisions matter enormously, but a team that can tackle the complexity and the transition is what actually gets it across the line. That is why behind every clean migration, there is a team that actually gets on well.