Anonymous View
Text-to-SQL doesn’t break because models can’t write SQL — it breaks because they don’t know what your data means. Write the meaning down first as a canonical knowledge layer, and use that one spec to both build the model and answer questions over it.
Adrian Brudaru
Schema alone scored 3/10. An ontology scored 10/10. A benchmark across two datasets showing exactly where the gap is, including cases where the model gets the right answer for the wrong reason.
Roshni Melwani
Schema evolution is a decision every data pipeline makes — most tools make it silently. This post discusses the five common failure modes every data pipeline sees, how dlt handles them, and how you can decide runtime policies for schema evolution with data contracts.
Aman Gupta
At Snowflake Summit 2026, dltHub was named Snowflake’s 2026 Startup Program Product Partner of the Year for helping more than 1,000 organizations bring hard-to-reach data into the Snowflake AI Data Cloud with Python-native, AI-driven pipelines.
Matthaus Krzykowski
You pay for compute hours; what you actually want is data moved. This post measures the exchange rate across the four bottlenecks that dominate real pipelines: SQL copy, REST APIs, JSON files, and Parquet
Aman Gupta
The agent wrote the pipeline, but assumptions slip silently: nulls in primary keys, duplicates from the wrong write disposition, drifted enum values. The dltHub AI Workbench data quality toolkit bootstraps checks from dlt's existing schema, samples columns before any rule ships, writes the checks into your pipeline as decorators, and routes failures back to the toolkit that owns the surface area: ingestion, transformations, or exploration.
Hiba Jamal
In public preview today as part of dltHub Pro. dltHub Transformations turns raw data into the clean tables your business and your agents actually use. Built for a moment when agents now write 9 out of 10 data pipelines.
Matthaus Krzykowski
Today's stacks split ingestion, transformation, orchestration, and the context that agents need gets lost at every boundary. dltHub Transformations runs ingestion, transformation, lineage, and verification inside the same execution context, so an LLM can reason about your business with the context a senior analyst would have.
Adrian Brudaru
One working student, Claude Code, one stakeholder call, and 2 weeks. The migration worked but the workflow we used is the actual point of this post. AI alone wouldn’t have gotten us there.
Nikolas Jack Altran
Generally available today. 91% of new dlt pipelines are now built by agents. dltHub Pro makes building and running them production-grade for any Python developer.
Matthaus Krzykowski
Write your access policy as a plain-English ontology. Schema evolves; the LLM reads the rules and decides.
Aman Gupta
