Source agents
Every source, in parallel.
Each agent owns one source end to end.
- Profiles every column in place: null rates, cardinality and keys.
- Maps fields to your business concepts, with confidence.
- Pins a data contract and watches it for drift.
Adaly Data Engineer · Beta
Data engineering that adapts.
An agent swarm that takes raw sources to governed, model-ready data on your warehouse, checks its own work, and keeps it right as your data changes.
Built for big data moves, feature engineering and training data. It runs on the warehouse, the git repo and the access controls you already have.
- Shopifyagent
- Stripeagent
- GA4agent
- Klaviyoagent
- Zendeskagent
- Ad platformsagent
Orchestrator
PlanBuild
Cross-check
Bronze
RAW, as landedSilver
STAGING, cleaned and typedGold
CORE, MARTS, FEATURESSee it adapt
Upstream changed. The pipeline kept up.
Shopify renames a column in production. Adaly traces what it breaks, ships the fix as a pull request, rebuilds what it touched, and compiles the same features for every warehouse.
Recorded on a synthetic direct-to-consumer dataset in Snowflake. No customer data is shown.
The agent swarm
One agent per source. One orchestrator.
Data engineering is slow when one person works through every source in turn. Adaly gives each source its own specialist agent, so the sources are worked in parallel while an orchestrator coordinates the build and checks the results.
Orchestrator
One plan, in dependency order.
The orchestrator turns the agents' work into one build.
- Plans the medallion layers and the checks that must pass.
- Resolves entities that span sources, like customers under four ids.
- Builds in dependency order and checkpoints each model.
Cross-checks
Sources hold each other to account.
Agents' results are checked against each other before anything is published.
- Reconciles totals across systems, like payments against orders.
- Tests that every primary key is unique.
- Escalates to an engineer only what needs a human decision.
How it works
From raw tables to training data, then kept current.
Eight steps build the pipeline. An engineer approves the plan before anything runs. After that, the platform keeps it healthy on its own.
- 01
Discover
Inventories every raw table and column, and who owns it.
- 02
Plan
Lays out bronze, silver and gold, every model, and the checks that must pass.
Nothing runs until an engineer approves.
- 03
Connect
Pins a data contract per source, so a silently retyped column is caught.
- 04
Profile
Measures null rates and cardinality in place, one aggregate query per table: the evidence behind every feature.
- 05
Map
Drafts a data dictionary, resolves entities and hashes personal data.
You review only the uncertain mappings.
- 06
Move
Builds every model natively in your warehouse, checkpointed as it goes.
- 07
Verify
Reconciles totals across sources and tests every key. A single failure blocks release.
- 08
Publish
Documents the catalog, traces column lineage and materialises feature tables.
- Adapt
Change lands
New rows rebuild only the affected models and re-verify. A new column flows downstream on its own.
- Compile
Any warehouse
Every model is written once and compiled to Snowflake, BigQuery or Databricks SQL.
- Ask
Plain-English questions
Questions are grounded in the semantic catalog, validated, checked with EXPLAIN and run under your role.
- Monitor
Healthy by default
Versions every upstream schema and routes each change by your policies.
Self-correction and validation
It checks its own work, and recovers on its own.
Pipelines break when sources change, sessions die or a mapping is wrong. Adaly is built to notice, correct and prove the fix before anything reaches a dashboard or a model.
Flags drift, never guesses.
Every source has a pinned contract. When a column arrives retyped or renamed, the agent flags it, classifies the change and routes it by your policies instead of guessing what it means.
Resumes where it stopped.
Each finished model is checkpointed. Kill the session mid-build and the resume picks up at the next model, with nothing lost.
Re-verifies every change.
When new rows land, only the affected models rebuild, and every check runs again before the refreshed data is published.
Blocks bad data at the gate.
Orders reconcile from raw to core, payments match order totals, ad spend matches the platform exports, and every key is tested for uniqueness. A single failure stops the release.
Validates before it runs.
Compiled SQL is parsed back and validated, and queries are checked with EXPLAIN against your warehouse before they run, under the role you grant.
Asks when it is unsure.
Confident mappings are accepted with their evidence. The rest are flagged with a reason, so engineers review exceptions instead of everything.
29 / 29
checks passed before release, including cross-source reconciliations.
78 of 97
routine dictionary mappings accepted automatically; 19 judgment calls sent to a reviewer.
32s
from 730 new rows landing to 21 rebuilt, re-verified models.
18s
for a new upstream column to flow into staging, with no code change.
From the recorded walkthrough run on Snowflake with a synthetic dataset of ten raw tables and about 76,000 rows.
Modern practice, built in
The way strong data teams already work.
No proprietary runtime and no black box. Everything it builds is plain SQL and code in your warehouse and your repo, following the patterns your team would choose.
Medallion architecture
Bronze, silver, gold.
Raw stays read-only as bronze. Staging is cleaned and typed as silver. Conformed core models, marts and features are gold.
Git-native
Every change is a pull request.
Models, tests and deployment code ship as pull requests into your existing git repo, ready for your normal review.
Change policies
You decide what auto-applies.
Set policies per kind of change: inserts and new columns apply on their own, renames open a pull request, and anything breaking waits for a human. Each change shows its downstream impact.
Write once
Compiles to any warehouse.
Models are written once and compiled to Snowflake, BigQuery or Databricks SQL, then deployed with each platform's native tooling.
Feature-store ready
Features where your models run.
Entity-keyed feature tables with an as-of timestamp, plus registration code for the Snowflake, Databricks and Vertex AI feature stores, and Feast.
Governed access
Your roles, respected.
It works under the warehouse role you give it, so it only reads and writes what that role allows. Personal data is hashed before it reaches features. How Adaly governs access.
Beta
Request beta access.
We are opening the Adaptive Data Engineer to a small group of data and ML teams. Tell us about your warehouse and what you want to build, and we will be in touch.
- Built for Snowflake, BigQuery and Databricks.
- Works under a role you create and control.
- Hands-on onboarding with the Adaly team.
Rather we run it for you?
Our data and ML engineers deliver the move, the features and the training data with you, in days.