Data First Consultancy @DataFirstGroup

Snowflake's latest primer makes the case for using its warehouse as an observability data lake. The pitch is clear: pour logs, metrics and traces into Snowflake, parse with SQL, join with biz data, and let teams query everything in one place. Sensible on paper, though costs can climb if raw telemetry floods your credits and "SQL for everyone" still needs schema work. Worth weighing against purpose-built observability stores and the ops skills you already have. #Observability #CloudData #OpenTelemetry medium.com/snowflake/buil… Need guidance on observability data lakes? Check datafirstconsultancy.co.uk/capabilities/c…

Snowflake’s latest piece details Copilot - an in-console LLM that turns chat into SQL and proposes tweaks. Useful for boilerplate and onboarding, but with only metadata visibility its performance advice still needs human scrutiny. #DataEngineering #Snowflake medium.com/snowflake/%EF%…

Easy to forget np.fft isn't the textbook FT. Jumbong breaks down sampling, scaling and bin ordering, showing how you can misread a spectrum without those tweaks. Worth bookmarking his step-by-step if you audit signal code. #Python #SignalProcessing towardsdatascience.com/implementing-t…

Three steps, not ten commandments: KDnuggets outlines a straightforward way to translate lofty business aims into measurable targets. Worth a skim if your 2024 OKRs still read like New Year’s resolutions. #Strategy #GoalSetting kdnuggets.com/ingram-how-to-…

Fresh off the press from Databricks: they’re rolling out table update triggers in Lakeflow Jobs. The pitch is simple - instead of polling for changes you hook tasks to live data events. Smart idea, but two caveats jump out. First, the docs skate over latency guarantees: "near real time" can hide a long tail if cluster spin up drags. Second, governance is your problem; triggers amplify lineage sprawl unless you’ve nailed catalog discipline. Worth a look for teams already invested in Delta Live, less so if you’re still comparing engines. #DataEngineering #Lakehouse databricks.com/blog/announcin… Need support with event driven pipelines? Visit datafirstconsultancy.co.uk/capabilities/c…

Olafenwa's walkthrough shows how GPT-5's function calling lets an agent loop plan-execute-verify without spaghetti prompts. Helpful focus on separating reasoning from tooling - still curious how he handles long-term memory. #AI #DevTips towardsdatascience.com/how-to-build-a…

£2.1M a year on cloud and no one noticed? I’ve shared 5 practical architecture tweaks that cut one client’s data bill by 80 percent - intelligent tiering, deduped datasets, auto-scaling and more. Your CFO will thank you. Read it here 👉 blog.datafirstconsultancy.co.uk/p/the-cloud-da… #CloudCosts #FinOps #DataArchitecture

Rosidi’s guide to wrangling 200k messy DoorDash rows into a model-ready set shows why you profile early and script fixes for null coords, duplicate orders, flaky timestamps. Practical, tool-agnostic, worth bookmarking. #DataCleaning #MLOps #Python kdnuggets.com/how-i-built-a-…

Digging into Qwen3-VL, Eivind Kjosbakken shows how a vision - language model can extract tables and handwriting from messy PDFs with a single prompt - no bespoke OCR. Key point: prompt craft still beats sheer size. #LLMs #ComputerVision #AI towardsdatascience.com/how-to-use-fro…

Snowflake lifted the lid on their preferred structuring tactics this week. They advocate a hybrid flow - Data Vault for ingestion, star for reporting - sensible, though it assumes teams can juggle two paradigms without adding latency. I like the push for clear RAW / CURATED / MART layers and tagging for PII. Still, clustering alone will not save a poorly written query, and governance needs more than naming standards. Worth a read if you are weighing up "model later" vs "design up-front". #DataEngineering #Snowflake medium.com/snowflake/mode… Need guidance on cloud data architecture? Check datafirstconsultancy.co.uk/capabilities/c…

Fresh from six months on the GenAI hackathon circuit, Parul Pandey distils a punchy set of do’s and don’ts. Key takeaways I’ll be borrowing: • Novel demos are fine, but judges remember clear user value • Bring a domain voice into the team early - it steers prompts and keeps scope sane • Build an evaluation loop you can show live; accuracy claims without metrics sink fast • Ship a landing page - feedback is data Good reminder that hackathons are less about code and more about pressure testing product strategy. #GenerativeAI #Hackathons #DataScience towardsdatascience.com/things-i-learn… Need guidance on GenAI hackathons? Check datafirstconsultancy.co.uk/capabilities/e…

Fresh take from OpenAI: Plex Coffee, a five-shop chain, leans on ChatGPT Business to surface recipes, speed up new-hire training and keep the counter chatty. It’s a tidy example of AI supporting frontline staff, yet the piece glosses over the data trade-off - your secret syrups and HR docs now sit in a third-party model. Great if agility outweighs IP risk; less so for larger menus or stricter compliance. Do the diligence before topping up your next flat white. #CustomerService #AI openai.com/index/plex-cof… Need support with customer service? Visit datafirstconsultancy.co.uk/capabilities/c…

OpenAI puts Plex Coffee in the spotlight: ChatGPT Business centralises recipes and speeds up onboarding. Nice example of small retail scaling, but it's also a sales pitch - no numbers on cost, data hygiene or measurable uplift. #AI #SMB openai.com/index/plex-cof…

Chinmay Kakatkar’s reminder: a framework is scaffolding, not scripture. Start with CRISP-DM or OSEMN, then trim, merge, rename stages until the map matches your org’s data quirks and success metrics. Custom beats cookie-cutter. #DataScience #MLops towardsdatascience.com/conceptual-fra…

Enjoyed Adam Streck's walkthrough on turning copy-number segments into 2-D 'images' and training a lean CNN in PyTorch to split LUAD vs LUSC. Handy reminder: preprocessing choices often matter more than model bells and whistles. #Genomics #DeepLearning towardsdatascience.com/classification…

Fresh take from Databricks on turning a lakehouse into a “digital mind” powered by multi-agent AI. They pitch a neat recipe: Delta tables as shared memory, Unity Catalog as referee, vector search + MosaicML models for cognition, and Workflows to glue the agents together. Sensible, but two caveats are left vague: 1) how you version prompts/agent logic alongside data; 2) who arbitrates GPU, cost and compliance when agents fight for resources. Still, a useful framing if you’re deciding where to centralise vs federate GenAI workloads. #DataArchitecture #GenAI databricks.com/blog/lakehouse… Need guidance on GenAI platform strategy? Check datafirstconsultancy.co.uk/capabilities/d…

Rudderstack’s take on privacy-safe AI analytics: capture metadata, hash IDs, drop raw prompts. Sensible, but it funnels you to their warehouse CDP; you’ll still need prompt redaction and tight access controls. #AIBuilders #DataPrivacy rudderstack.com/blog/ai-produc…

Plenty of slideware still talks about 'future AI agents'. KDnuggets lists five teams already running them in production - from self-healing data pipelines to autonomous ticket triage. Worth a read if you're weighing when to move from chatbots to workflow ownership. My takeaway: orchestration glue - APIs, monitoring, fallback logic - matters more than model cleverness. #AI #Automation #ChatGPT kdnuggets.com/5-practical-ex… Need guidance on AI-driven automation? Check datafirstconsultancy.co.uk/capabilities/d…

Stops the ‘Vault vs Kimball’ argument by parking each model where it fits: Vault in Silver for volatile, auditable data; star schemas in Gold for speed and BI polish. The PIT/SCD glue between layers is where budgets melt. #dataengineering #datamodeling dataengineeringweekly.com/p/revisiting-m…

OpenAI shines a light on Plex Coffee adopting ChatGPT Business as its knowledge hub. Smoother onboarding and consistent service sound plausible, but there’s little on costs or measurable impact. Curious case study, not proof. #AI #SMB openai.com/index/plex-cof…