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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 csv / tsv / parquet / json / jsonl / avro / arrow / xlsx 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

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.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.2.52)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for hesham.h.96 from gravatar.com hesham.h.96

Credits

Author: DVT Team

License

Apache Software License

Requires

Python <4, >=3.9

Provides Extra

dev

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0.2.39

15 files

0.2.38

15 files

0.2.37

15 files

0.2.36

15 files

0.2.35

15 files

0.2.34

15 files

0.2.33

15 files

0.2.32

15 files

0.2.31

15 files

0.2.30

15 files

0.2.29

15 files

0.2.28

15 files

0.2.27

15 files

0.2.26

15 files

0.2.25

15 files

0.2.24

15 files

0.2.23

15 files

0.2.22

15 files

0.2.21

15 files

0.2.20

15 files

0.2.19

15 files

0.2.18

15 files

0.2.17

15 files

0.2.16

15 files

0.2.15

15 files

0.2.14

15 files

0.2.13

15 files

0.2.12

15 files

0.2.11

15 files

0.2.10

15 files

0.2.9

19 files

0.2.8

9 files

0.2.7

9 files

0.2.5

9 files

0.2.4

9 files

0.2.3

9 files

0.2.2

9 files

0.2.1

9 files

0.2.0

9 files

Yanked

0.1.55

10 files

Yanked

0.1.54

10 files

Yanked

0.1.53

10 files

Yanked

0.1.52

10 files

Yanked

0.1.51

10 files

Yanked

0.1.50

10 files

Yanked

0.1.49

10 files

Yanked

0.1.48

10 files

Yanked

0.0.1

2 files

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