Your data engineering workflow, in plain English.
From your IDE or CLI, tell Dataflake what you want to build. It understands your data stack, connects the right services, generates the jobs, pipelines and DAGs, and validates the result before you ship it.
The problem
Data engineering time goes to plumbing, not to data.
Every new job means re-reading schemas, tracing lineage, copying an old DAG, guessing at conventions, and waiting for a run to fail in staging before you learn anything.
Context lives everywhere
Schemas in the warehouse, logic in dbt, orchestration in Airflow, tribal knowledge in Slack.
Boilerplate dominates
Most of a new pipeline is scaffolding that has already been written a dozen times before.
Failures surface late
Schema drift and broken dependencies show up after deploy, not while you are writing the job.
What is Dataflake?
A data engineering agent that works inside your stack.
Dataflake is not a chatbot bolted onto a warehouse. It connects to the services your company already runs, learns your environment, and builds real jobs, pipelines and DAGs from natural-language prompts — from your IDE or CLI.
Where you already work
Drive it from your editor or the terminal. No new console to live in.
Grounded in your context
Schemas, lineage, existing jobs and team conventions inform every decision.
Across every service
One agent that coordinates warehouses, transformation, orchestration and compute.
Validated before you ship
Every generated workflow is simulated and tested before it reaches production.
Prompt → Pipeline
From a prompt to a production-ready data job.
Work from your IDE or CLI. Dataflake connects to the services your company already uses, understands your data environment, and turns natural-language prompts into pipelines, jobs, DAGs and data workflows.
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Waiting for instruction…
One agent. Your entire data stack.
Dataflake connects to the services your company already uses — from warehouses and transformation frameworks to orchestration, compute, and cloud data services.
Capabilities
Your data engineering workflow, in plain English.
From your IDE or CLI, tell Dataflake what you want to build. It understands your data stack, connects the right services, generates the jobs and pipelines, and validates the result before you ship it.
Build Databricks jobs from prompts
Tasks, clusters, schedules and dependencies generated to your workspace conventions.
Generate Airflow DAGs automatically
Dependencies, retries, alerting and schedules wired the way your team already does it.
Connect every service in your stack
Snowflake, dbt, Dataflow, Datastream, Dataproc and the clouds underneath them.
Test pipelines before implementation
Dry-run simulations with record counts, schema checks and dependency validation.
Work from your IDE or CLI
The agent meets your existing workflow instead of replacing it.
Keep your engineering process
Everything lands as reviewable code in your repo, through your normal pull requests.
Use cases
What teams build with Dataflake.
The demonstration above shows the feel of the product. This is the concrete work it takes off your plate.
Databricks jobs
Incremental models, scheduled runs, cluster policies.
Airflow DAGs
Orchestrate existing pipelines with retries and alerting.
Data pipelines
Ingest, validate, transform and publish end to end.
Pipeline testing
Simulate a run against real shapes before deploying.
Debugging
Trace a failing task back through lineage to the source.
Data analysis
Ad-hoc questions answered against governed models.
Data quality
Generate expectations and contracts alongside the job.
Cross-platform workflows
One workflow spanning warehouse, compute and orchestration.
How it works
Connect → Understand → Plan → Execute → Validate
The intelligence comes from context: your lineage, your run history, your conventions. That is why the output looks like something your team would have written.
Connect
Link the services your company already uses — warehouse, transformation, orchestration, compute, cloud.
Understand
Dataflake loads schemas, lineage, historical runs and team conventions to build real context.
Plan
It sequences the work, identifies dependencies and surfaces conflicts before writing anything.
Execute
It generates the jobs, models, DAGs and configuration as reviewable code.
Validate
It simulates the result — records, schema checks, quality tests — so you ship with evidence.
Integrations
Connected to everything your company uses.
The integration layer is extensible, so new services join the same agent without changing how your team works.
Enterprise
Runs inside your data boundary.
Deploy Dataflake in your VPC or on-premises. Your schemas, lineage and data never leave the perimeter your security team already approved.
VPC deployment
Run the agent next to your warehouse inside your own cloud account.
On-prem option
Air-gapped environments supported for regulated data platforms.
Data boundary
Fine-grained access, audit trails, and no training on your data.
Pricing
Flat monthly pricing. More seats as your data team grows.
Every plan includes the full agent for one flat monthly price — no per-user billing. Higher tiers add more included users and faster, more hands-on support.
Starter
Up to 7 users included
Email support (48h)
- IDE and CLI agent
- Up to 3 connected services
- Pipeline simulation & dry runs
- 1 workspace
Team
PopularUp to 25 users included
Priority support (8h) + shared Slack channel
- Everything in Starter
- Unlimited connected services
- Airflow DAG + Databricks job generation
- Shared conventions & lineage context
- Up to 5 workspaces
Enterprise
Unlimited users
Dedicated engineer, 1h SLA, 24/7 on-call
- Everything in Team
- VPC or on-prem deployment
- SSO, SCIM and audit trails
- Custom integrations & onboarding
- Unlimited workspaces
FAQ
Questions data teams ask us first.
If something is not covered here, reach out and we will answer it against your actual stack.
How does Dataflake understand my data environment?+
It connects read-only to the services you already run — warehouses, lakehouses, orchestrators and catalogs — and builds a working map of schemas, tables, jobs and lineage before it writes anything.
Which platforms does it generate code for?+
Databricks jobs, Airflow DAGs, dbt models, Snowflake and BigQuery SQL, Spark and Python pipelines, plus GCP Dataflow and Dataproc workloads.
Does it push changes to production automatically?+
No. Dataflake plans, generates and validates through dry runs and simulated executions, then hands you a reviewable diff or pull request. You stay in control of the merge and deploy.
Where does my data live?+
Your data stays in your environment. Dataflake works with metadata and sampled context, and Enterprise plans can run fully inside your VPC or on-prem.
Can I use it from the terminal instead of an IDE?+
Yes. The same agent runs as a CLI and as an IDE extension, so you can prompt from wherever you already work.
How does billing work?+
Every plan is one flat monthly price with a set number of included users — Starter covers 7 users, Team covers 25, and Enterprise is unlimited. No per-seat surprises.
Contact
Talk to the team building Dataflake.
Tell us about your stack and we will show you the agent running against a workload like yours.
Describe the job. Dataflake builds it.
Prompt → Understand → Build → Test → Deploy, from the IDE and terminal you already use.
Dataflake · one agent for your data stack