DVT — Data Virtualization Tool
Any source. Any target. One SQL project.
DVT is a cross-engine data transformation CLI built on dbt. Write SQL models that JOIN tables living on different database engines — MySQL with Snowflake, Oracle with PostgreSQL, anything with anything — and DVT handles extraction, federation, and loading automatically.
{{ config(materialized='f_table') }} -- "f" = federated
select
c.customer_name,
o.order_date,
o.total_amount
from {{ source('crm', 'customers') }} c -- lives on MySQL
join {{ source('erp', 'orders') }} o -- lives on Snowflake
on c.customer_id = o.customer_id
where o.order_date >= '2024-01-01'
pip install dvt-core
dvt sync # installs every adapter + driver your profiles.yml needs
dvt run # dbt runs what dbt can; DVT federates the rest
dvt version # (worth running once — you'll see)
Website & full docs: getdvt.net · Quickstart · PyPI
Architecture
DVT is a wrapper around stock dbt-core, not a fork. dbt compiles the project and runs every standard model; DVT picks up the models dbt can't express — cross-engine federation models and locally-executed Python models — and runs them through its own pipeline:
flowchart TB
CLI([dvt run / build]) --> COMPILE["dbt compile → manifest.json"]
COMPILE --> STD["standard models<br/>table · view · incremental"]
COMPILE --> FED["federated models<br/>f_table · f_incremental · .py"]
STD --> DBT["stock dbt executes them<br/>on the default target"]
FED --> HOMO{"all sources on<br/>ONE connection?"}
HOMO -- "yes — Sling direct" --> DIRECT["whole query transpiled to the
source engine, streamed straight
to the target — no staging"]
HOMO -- "no — federation" --> DECOMP["1 · DECOMPOSE — SQLGlot maps every
table to a profiles.yml connection"]
DECOMP --> PUSH["2 · TRANSPILE + PUSH DOWN — minimal
per-source queries, each engine's dialect"]
PUSH --> EXTRACT["3 · EXTRACT — Sling → Parquet, parallel"]
EXTRACT --> COMPUTE["4 · COMPUTE — DuckDB joins locally"]
COMPUTE --> LOAD["5 · LOAD — Sling delivers to any
database, warehouse, or bucket"]
DIRECT --> TARGET[("target")]
LOAD --> TARGET
DBT --> TARGET
The Sling direct path (0.2.5)
When every source in an f_table model lives on one connection, steps 3–5 collapse into a single hop: the whole query — joins and aggregations included — is transpiled to that engine's dialect and submitted to Sling as a custom-SQL stream loaded straight to the target. No Parquet staging, no DuckDB. The check is automatic and per-run; any failure falls back to the standard pipeline. Add a source on another engine tomorrow and the same model silently switches to the federation pipeline — the model never changes.
Execution paths
| Your model | Who runs it | How |
|---|---|---|
table / view / incremental |
stock dbt | natively on the default target — whole-query pushdown by definition |
f_table / f_incremental, sources on one connection |
DVT | Sling direct: full query pushed to the source engine, result streamed to the target |
f_table / f_incremental, sources span connections |
DVT | decompose → per-source pushdown extraction → DuckDB compute → load |
.py models |
DVT | locally executed; result loads to any target |
Standard models that set a non-default target= are refused with guidance (dbt silently ignores the kwarg — your model would land on the wrong engine).
Design principles
- Wrapper, not fork. DVT shells out to the dbt CLI and reads
manifest.json— it never imports dbt internals. Your dbt version can move; DVT keeps working. Every dbt feature (Jinja, macros, packages, tests, snapshots, docs) works because it is dbt running it. - Dual-dialect by design. Federation models are written in DuckDB dialect and are transpiled per source — they never depend on any engine's syntax. Standard models compile in the default target's dialect via the official dbt adapter. Result: switching your default target (e.g. PostgreSQL → Databricks) is a profile change, not a rewrite.
- Pushdown first. The fastest row to move is the one you never extract. Filters and column pruning push down to source engines; when every table in a model lives on one engine, the entire query pushes down and nothing moves at all.
- Your hardware does the joining. Cross-engine compute happens in DuckDB on the machine running DVT — source databases only ever serve simple filtered SELECTs, and no warehouse bills you for the join.
What DVT adds on top of dbt
| Capability | How |
|---|---|
| Cross-engine models | materialized='f_table' / 'f_incremental' (long aliases: federated_*, federation_*) |
| Incremental federation | watermark-based delta extraction; persistent local cache between runs |
| Per-model targets | config(target='other_output') on federated models — results land on any profile output (standard models are refused a foreign target) |
| Bucket targets | materialize models to S3 / GCS / Azure as Parquet or CSV |
| Python models, locally | dbt-style .py models run on your machine (APIs, pandas, ML) — results land on any target |
| Bulk seeds | dvt seed loads DVT's four file formats — csv / parquet / json / jsonl — via Sling to any engine, with enforced snake_case columns |
| One-command setup | dvt sync installs every adapter and driver your profiles.yml requires |
| Connection diagnostics | dvt debug --all checks every connection on both layers (dbt adapter + Sling) |
| Cross-engine catalog | dvt docs builds one catalog + lineage UI across all engines in your project |
Everything else — profiles.yml in ~/.dbt/, sources.yml, ref/source/config, tests, snapshots — is standard dbt. Existing dbt projects work unchanged; sources gain an optional meta.connection to bind them to other engines.
Supported engines
Every engine supported by both dbt and Sling is a DVT adapter — as federation source, federation target, seed target, and default target:
PostgreSQL · MySQL · MariaDB · SQL Server · Oracle · SQLite · DuckDB · Snowflake · BigQuery · Redshift · Databricks · ClickHouse · Trino · Athena · Microsoft Fabric — plus S3, GCS, and Azure Blob as bucket storages.
Anything else (MongoDB, Elasticsearch, REST APIs, …) is reachable through locally-executed Python models. Details per adapter: getdvt.net/docs/adapters.
Learn more
- The federation engine, step by step
- Predicate pushdown deep dive
- Switching the default target (lakehouse portability)
- Pricing & plans
License
DVT is distributed as compiled wheels on PyPI. Free tier covers local files, transactional databases, and Python models; Pro adds cloud buckets (S3/GCS/Azure); Max adds cloud warehouses. See getdvt.net/pricing.
1 maintainer
Metadata
1 maintainer
Release files for dvt-core 0.2.60
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