PrettyWhale.ai blog
Data Stack Consolidation: Why Platforms Want Your Data Pipelines
5 data stack deals, all announced in the name of AI agents. Why the platforms want your data pipelines, and why the decision layer should stay yours.
Data Engineering Trends for 2027: 5 Shifts Already Underway
he 5 data engineering trends that will shape 2027, from pipeline agents to the EU Data Act, built on what shipped this year and what comes next.
Other articles
Why Self-Debugging LLMs Break Your Unit Tests
Discover why naive LLM self-debugging breaks unit tests in automated code generation and how contract-driven agentic architectures fix it.
The Great AI Disruption in Consulting
Discover how AI is dismantling the billable hour in IT services. Explore how the shift toward outcome-guaranteed contracts and asset-based consulting is redefining professional services.
The Age of Generic Copilots Is Over. Enter Specialized Harnesses.
Code generation became a commodity. What did not is the engineering frame around the model. Why the next wave is specialized harnesses, and what one contains for data.
Data Engineering Doesn't Need a Smarter AI Copilot
Every correction you type into a copilot is a requirement you already knew. Driving one means re-expressing your standards forever, one prompt at a time.
Data Pipeline Vendor Lock-In Is Built One Connector at a Time
Lock-in is not a contract term, it is a rewrite estimate. Why the ingestion layer accumulates it faster than anything else, and how to measure yours today.
Small Language Models for Code Generation: When Narrow Beats Large
Small language models can match large ones on a bounded job like code generation, at 10 to 30 times lower serving cost. Here is when that trade holds.
Who Accepts AI-Generated Code That Nobody Wrote
Code review assumes an author who can answer for the code. Generated code breaks that assumption. What changes in the pull request, and after an incident.
Production-Ready Data Pipelines: What It Means and Who Decides
Ask two engineers when an ingestion pipeline is finished and you get two answers. Here is how to write the bar down on one page and enforce it in review.
Estimating Data Ingestion Work: A Unit-of-Work Method
Stop estimating ingestion tasks one by one. Estimate units of work, build a tier grid from your own delivery history, and recalibrate it after every project.
Moove-SI and PrettyWhale.ai Team Up on Data & AI
Moove-SI, an integrator specialising in Microsoft solutions, is adding PrettyWhale.ai to its Data & AI offer to speed up the costliest part of data projects.
Engineering AI, Copilots and Code Generators: A Taxonomy
"AI writes code now" covers four product families with four failure modes. The criterion that separates them, and four questions to pick the right one.
The End of the Daily-Rate Model for IT Services
For thirty years IT services firms sold human time by the day. Clients now ask what you save them, not how many consultants you can put on the project.
Ingestion vs Integration: A Confusion That Costs Millions
Ingestion and integration code get used as synonyms. They solve different problems, and the teams that optimise the wrong one pay for it in production.