dlt-meta
DLT-META Framework
Decision gist · record as of 2026-08-14
Yes, if you are building data pipelines on Databricks and want to reduce boilerplate by adopting configuration-driven automation. The low install friction and lack of known vulnerabilities support adoption. However, the unclear license terms and aging maintenance status (332 days since last release) warrant review before committing to production use—confirm license compatibility and assess whether the slower maintenance cadence aligns with your support expectations.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Databricks CLI v0.213 or later, a Databricks workspace, and Python 3.8 or higher.
- Must authenticate via 'databricks auth login --host WORKSPACE_HOST' before use.
- Low install friction with three straightforward runtime dependencies.
License · maintenance · safety
Databricks License (unclear) — License treatment is unclear: the package carries a 'Databricks License' designation without a standard SPDX identifier. Review the actual license terms before adopting in commercial or redistributed contexts.
last release 2025-09-16 (332 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 241,127 downloads/mo, #8,885 on PyPI
Alternatives
Verify before relying
pip install dlt-meta
from dlt_meta import DLTMeta
# Authenticate and configure
databricks labs dlt-meta onboard
databricks labs dlt-meta deploy- Exact scope of the 'Databricks License' and whether it permits commercial use or redistribution.
- Whether the package is actively maintained or in maintenance-only mode given 332 days since last release.
- Performance characteristics and scalability limits for large pipeline configurations.
What it is and what it does
DLT-META bridges Databricks Lakeflow Declarative Pipelines and configuration-driven automation by reading pipeline metadata from JSON onboarding files and dynamically generating bronze and silver layer pipelines. Instead of writing pipeline code for each data source, teams define source/target metadata, data quality rules, and transformations in configuration files; a single generic pipeline then reads these specifications and orchestrates the workload. The framework supports multiple input sources (Autoloader, Delta, Eventhub, Kafka, snapshots), CDC operations, data quality expectations, quarantine tables, and custom transformations across both layers.
The package integrates with the Databricks CLI to provide interactive onboarding and deployment commands. It is designed for teams building enterprise-scale data platforms who want to templatize pipeline creation and reduce manual coding. Runtime dependencies are minimal (setuptools, databricks-sdk, PyYAML), and the package targets Python 3.8 and later.
Use it for
- Automate bronze pipelines ingesting from multiple sources using metadata-driven configuration instead of hand-coded transformations.
- Define silver-layer transformations and data quality rules in JSON, then deploy across datasets without rewriting logic.
- Implement medallion architecture at scale by chaining bronze and silver pipelines with a single configuration parameter.
- Apply CDC and snapshot-to-stream flows declaratively by specifying them in metadata rather than coding each pattern.
- Enforce data quality expectations and quarantine invalid records automatically by declaring rules in configuration files.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building data pipelines on Databricks and want to reduce boilerplate by adopting configuration-driven automation.
The low install friction and lack of known vulnerabilities support adoption. However, the unclear license terms and aging maintenance status (332 days since last release) warrant review before committing to production use—confirm license compatibility and assess whether the slower maintenance cadence aligns with your support expectations.
Install
dlt-meta on PyPI
Before you install
Low install friction with three straightforward runtime dependencies. Package shows aging maintenance status—last release was 332 days ago—so expect slower response to issues, though no known vulnerabilities are present.
Requires Databricks CLI v0.213 or later, a Databricks workspace, and Python 3.8 or higher. Must authenticate via 'databricks auth login --host WORKSPACE_HOST' before use.
License in practice
License treatment is unclear: the package carries a 'Databricks License' designation without a standard SPDX identifier. Review the actual license terms before adopting in commercial or redistributed contexts.
Quickstart
pip install dlt-meta
from dlt_meta import DLTMeta
# Authenticate and configure
databricks labs dlt-meta onboard
databricks labs dlt-meta deployVerify before relying
- Exact scope of the 'Databricks License' and whether it permits commercial use or redistribution.
- Whether the package is actively maintained or in maintenance-only mode given 332 days since last release.
- Performance characteristics and scalability limits for large pipeline configurations.
Package facts
| License | Databricks License unclear |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagessetuptoolsdatabricks-sdkPyYAML |
| Maintenance | Aging 332 days since the last release |
| First released | |
| Downloads | 241,127 / month, #8,885 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: System AdministratorsOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Software Development :: Testing |
Evidence: dlt_meta-0.0.10-py3-none-any.whl
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See also databricks-dlt · databricks-bundles · dbldatagen · brickflows · dbx · dagster-dlt · dlthub · dbt-databricks · dagster-databricks · databricks-feature-engineering