Skip to main content

Enhanced PreQL for common ETL needs.

Project description

Simple Declarative Data Pipelines

Combine the simplicity of Trilogy with the modern data stack such as DBT.

[!TIP] Pitch: don't worry about optimizing your ETL staging tables ever again - write your final tables and let TrilogyT handle the rest.

Compile your models to ETL scripts to run on demand. Rebuild, run, and test easily.

Translates 'Persist' statements in Trilogy to scheduled ETL jobs.

Currently supported backends:

  • Native (optimize a PreQL model)
  • DBT

[!WARNING] This is an experimental library. The API is subject to change.

Flags

--optimize=X - Any CTE used at least X times in calculating final model outputs will be materialized for reuse.

Install

pip install pytrilogyt

How to Run

preqlt <preql_path> <output_path> --run

DBT

For dbt, the output_path should be the root of the dbt project, where the dbt_project.yml file exists.

trilogyt dbt/models/core/ ./dbt bigquery --run

Each source preql file will be built into a separate DBT sub folder with one model per persist statement.

17:12:37  Running with dbt=1.7.4
17:12:38  Registered adapter: bigquery=1.7.2
17:12:38  Found 4 models, 4 tests, 0 sources, 0 exposures, 0 metrics, 447 macros, 0 groups, 0 semantic models
17:12:38
17:12:40  Concurrency: 4 threads (target='dev')
17:12:40
17:12:41  1 of 4 START sql view model dbt_test.customers ................................. [RUN]
17:12:41  2 of 4 START sql table model dbt_test.customers_preql_preqlt_gen_model ......... [RUN]
17:12:41  3 of 4 START sql table model dbt_test.my_first_dbt_model ....................... [RUN]
17:12:42  1 of 4 OK created sql view model dbt_test.customers ............................ [CREATE VIEW (0 processed) in 1.09s]
17:12:43  3 of 4 OK created sql table model dbt_test.my_first_dbt_model .................. [CREATE TABLE (2.0 rows, 0 processed) in 2.78s]
17:12:43  4 of 4 START sql view model dbt_test.my_second_dbt_model ....................... [RUN]
17:12:44  2 of 4 OK created sql table model dbt_test.customers_preql_preqlt_gen_model .... [CREATE TABLE (100.0 rows, 4.3 KiB processed) in 3.55s]
17:12:44  4 of 4 OK created sql view model dbt_test.my_second_dbt_model .................. [CREATE VIEW (0 processed) in 1.10s]
17:12:44
17:12:44  Finished running 2 view models, 2 table models in 0 hours 0 minutes and 6.37 seconds (6.37s).
17:12:45  
17:12:45  Completed successfully
17:12:45
17:12:45  Done. PASS=4 WARN=0 ERROR=0 SKIP=0 TOTAL=4
customers: success
my_first_dbt_model: success
customers_preql_preqlt_gen_model: success
my_second_dbt_model: success

From IO

Write-Output """constant x <-5; persist into static as static select x;""" | trilogyt <output_path> bigquery

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pytrilogyt-0.0.7.tar.gz (13.0 kB view details)

Uploaded Source

Built Distribution

pytrilogyt-0.0.7-py3-none-any.whl (15.9 kB view details)

Uploaded Python 3

File details

Details for the file pytrilogyt-0.0.7.tar.gz.

File metadata

  • Download URL: pytrilogyt-0.0.7.tar.gz
  • Upload date:
  • Size: 13.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for pytrilogyt-0.0.7.tar.gz
Algorithm Hash digest
SHA256 a8ccb1b43c73a38e18d7bab3e27ae0904a3d6400f4cbfb28d32919ab621ec130
MD5 2629ae5f78f234d4e04c0bea52469c76
BLAKE2b-256 a3e38073f452fada0a632e28dde292d1ed02a402e589e647dafb081045037ec6

See more details on using hashes here.

File details

Details for the file pytrilogyt-0.0.7-py3-none-any.whl.

File metadata

  • Download URL: pytrilogyt-0.0.7-py3-none-any.whl
  • Upload date:
  • Size: 15.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for pytrilogyt-0.0.7-py3-none-any.whl
Algorithm Hash digest
SHA256 9b7786176d6b9626ca52d9ccd86b854ad93c73311e60815f1b2bf7ed00e6e79c
MD5 b453f6dd70485779019ae37ed860c229
BLAKE2b-256 1fa0ab7be3c8d71f5a8fe6abf4c3f106e87ae75ec03fd985e035ceb2d1e03b89

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page