Where dltHub's AI harness ends and Snowflake's CoCo begins

DltHub's commercial AI harness and Snowflake's Cortex Code (CoCo) coding agent operate on opposite sides of a data pipeline, with dltHub's harness handling ingestion into a lakehouse and CoCo taking over once data lands in Snowflake, according to a first-person account on sfrt.io. dltHub's harness ships ten toolkits and forty skills as a .md file that agents such as Claude Code, Codex and Cursor pull into context, while Snowflake's CoCo executes SQL, reads schemas and profiles tables and, per Snowflake's own benchmark, beats general-purpose coding agents on data engineering tasks. The author migrated a few dozen dlt ingestion pipelines from Snowflake Container Services to dltHub Runtime jobs out of a single workspace, and reports that CoCo can then label tables and columns in the Horizon catalog, build a data model with Dynamic Tables or dbt, design SPCS-hosted Streamlit apps, and build a Semantic View.

Infrastructure https://www.sfrt.io/tag/infrastructure/ Where dltHub's AI harness ends and Snowflake's CoCo begins dltHub's AI harness writes the pipeline, Snowflake's CoCo takes over after data landed. Together they ✨ TL/DR: dltHub's commercial AI harness and Snowflake's Cortex Code CoCo sit on opposite sides of my pipelines. The former enables coding agents to build and maintain ingestion pipelines into a lakehouse... like ❄️ Snowflake. The latter is such a coding agent, specialized on working in said lakehouse. Together they ✨ The dynamic duo dltHub's commercial offering includes a coding-agent harness https://dlthub.com/features/ai-harness?ref=sfrt.io : skills, rules, and MCP tools for Claude Code, Codex, Cursor, etc., running the full pipeline lifecycle through it ingest, transform, explore, operate . Ten toolkits, forty skills. The whole thing ships as a .md file agents can pull straight into context: Snowflake among many other things 😅 sells CoCo. It is a coding agent too, but a specialized one: it understands a live Snowflake account and has deep knowledge around all things Snowflake, integrated into the agent out of the box. It executes SQL, reads schemas, profiles tables, and per Snowflake's own benchmark https://www.snowflake.com/en/blog/snowflake-coco-ai-coding-agent-modern-data-stack/?ref=sfrt.io beats general-purpose coding agents specifically on data engineering tasks, because it doesn't first have to gather context from a README. Neither vendor frames it this way, but placed side by side a difference becomes clear: What actually happens when combined About 6 months ago, I ran a few dozen dlt ingestion pipelines in Snowflake Container Services SPCS : one Docker image and YAML spec per pipeline, external-access-integrations to allow ingress, and a compute pool to keep an eye on. All of it got torn down when dltHub launched. The only memory is the changelog entries in a Snowflake few tasks that used to call EXECUTE JOB SERVICE and now call TRIGGER DLTHUB PIPELINE instead. Ingestion from Salesforce, our ERP, Jira, Confluence, SharePoint, Microsoft Graph, half a dozen ad platforms, Swiss federal statistics, and many more now runs as dltHub Runtime jobs out of a single workspace. That migration alone carries most of the value prop: commercial dltHub swapped infrastructure work for authoring work plus integrated observability soon including agentic monitoring and auto-healing 🤫 . On a one-person data team, fewer moving parts is always better. However, this setup could be done with any coding agent, really. Or without any if one prefers 😅 The real magic trick comes next... The part dltHub's harness doesn't reach dltHub's harness equips a coding agent to write and deploy pipelines. But on top of this, it also enables getting tons of meta information out of the pipeline. Since dlt accesses the source system and "sees" the data in transition, the harness enables a coding agent to derive the structure of the source data, its taxonomy, build an ontology based on this and even scaffold a canonical data model. And now hand all of this over to CoCo... Equipped with the knowledge on what dltHub inserted into Snowflake, CoCo can do all kinds of really useful things: 1. Label the tables and their columns in Horizon catalog, 2. build a data model on top using Dynamic Tables or dbt, 3. design SPCS-hosted Streamlit apps visualizing that model, or - and this is my personal favorite - 4. build a Semantic View. Semantic layers require an infusion of "meaning" for an analytical agent to understand the data well. A commented raw layer is good, a descriptive layer including relations and rules is better. The boundary is gone Would building all of this in Snowflake be possible with a different coding agent than CoCo? Of course Would it then happen behind Snowflake's security and accountability perimeter? Not so much. And again, on the ADE-Bench built by dbt Labs for realistic data-engineering tasks , CoCo scored 72.1% against 65.1% for both Claude Code and Codex, while burning 51% fewer tokens and requiring 8% less time than Claude Code on Opus 4.7 . A gap attributed to native Snowflake tool calls instead of falling back to bash or scratchpad scripts. Whether that margin holds outside a vendor benchmark is its own question, sure, but the design direction is legit: less shelling out, more direct API calls into the systems that matter. And then, 🧊-berg Just as an extra cherry on top: dltHub's commercial tier does more than deploy pipelines that write to Snowflake. It ships a Snowflake-specific destination https://dlthub.com/docs/hub/ingestion/snowflake-plus?ref=sfrt.io "Snowflake+", that creates Apache Iceberg tables through Snowflake SQL and syncs them into Snowflake's Horizon or Apache Polaris catalog automatically. destination.snowflake external volume = "my external volume" iceberg mode = "all" catalog sync = "my open catalog int" Small config, big consequence: ingestion layer and table format are no longer two separately maintained things bolted together, because both dltHub and CoCo are aware of Snowflake's own Iceberg catalog mechanics. The combined value prop dltHub's commercial harness made pipeline authoring an agent-first workflow, my own SPCS-to-dltHub migration is the receipt. Snowflake's CoCo is the only coding agent in with standing authority to say what's actually working best in Snowflake. Tied together, the two systems share tons of valuable knowledge about the data and the systems around it 🤓