{"categories":[{"label":"Testing","url":"https://skillfed.io/packages/category/software-development-testing/4"}],"enrichment":{"capability":"DLT-META is a metadata-driven framework that automates bronze and silver data pipelines on Databricks by reading pipeline specifications from JSON configuration files and orchestrating Lakeflow Declarative Pipeline execution.","skillfed_tags":["databricks-integration","data-pipeline-automation","metadata-driven"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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\u2014confirm license compatibility and assess whether the slower maintenance cadence aligns with your support expectations."},"id":"dlt-meta","links":{"html":"https://skillfed.io/packages/dlt-meta","md":"https://skillfed.io/packages/dlt-meta.md","pypi":"https://pypi.org/project/dlt-meta/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-09-16","license_spdx":null,"license_treatment":"unclear","name":"dlt-meta","python_support":"supports_current","summary":"DLT-META Framework"},"popularity":{"monthly_downloads":241127,"position":8885,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.10"}