dbt-metabase
pgsink
Our great sponsors
dbt-metabase | pgsink | |
---|---|---|
1 | 5 | |
425 | 76 | |
- | - | |
8.2 | 0.0 | |
11 days ago | about 1 year ago | |
Python | Go | |
MIT License | MIT License |
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dbt-metabase
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A modern data stack for startups
So how do we get this into Metabase? There's a tool called dbt-metabase that can infer Metabase semantic type information from the dbt schema and push it into Metabase- we run this whenever complete a dbt build, helping sync Metabase with whatever new fields we may have added.
pgsink
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GitHub - go-jet/jet: Type safe SQL builder with code generation and automatic query result data mapping
This is a really awesome project. I’ve used it on https://github.com/lawrencejones/pgsink to generate type safe bindings to the Postgres catalog tables, along with a few of the tables the project maintains itself.
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Trade-offs from using ULIDs at incident.io
pgx is really good: it's what I used to write logical decoders in https://github.com/lawrencejones/pgsink
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A modern data stack for startups
It used to be that companies would write their own hacky scripts to perform this extraction - I've had terrible incidents caused by ETL database triggers in the past, and even built a few generic ETL tools myself.
- Sync Postgres to BigQuery, possible? How?
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Ask HN: Show me your Half Baked project
Postgres change-capture device that supports high-throughput and low-latency capture to a variety of sinks (at first release, just Google BigQuery):
https://github.com/lawrencejones/pgsink
I know there's debezium and Netflix's dblog, but this project aims to be much simpler.
Forget about kafka and any other dependency: just point it at Postgres, and your data will be pushed into BigQuery. And for people with highly-performance-sensitive databases, the read workload has been designed with Postgres efficiency in mind.
I'm hoping pgsink could be a gateway drug to get small companies up and running with a data warehouse. If your datastore of choice is Postgres, it's a huge help to replicate everything into an analytics datastore. A similar tool has helped my company extract expensive work out of our primary database, which is super useful for scaling.
The project is 90% there, about 10hrs and some testing away from being useable. Once there, I'll be hitting up some start-up friends and seeing if they want to give it a whirl.
What are some alternatives?
dbt-fal - do more with dbt. dbt-fal helps you run Python alongside dbt, so you can send Slack alerts, detect anomalies and build machine learning models.
pastty - Copy and paste across devices
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
dupver - Deduplicating VCS for large binary files in Go
airflow-dbt - Apache Airflow integration for dbt
DataflowTemplates - Cloud Dataflow Google-provided templates for solving in-Cloud data tasks
nodejs-bigquery - Node.js client for Google Cloud BigQuery: A fast, economical and fully-managed enterprise data warehouse for large-scale data analytics.
debezium-examples - Examples for running Debezium (Configuration, Docker Compose files etc.)
ngods-stocks - New Generation Opensource Data Stack Demo
xact - Model based design for developers
MetabaseMonitoringToolkit - Set of queries designed to measure how users are consuming queries and dashboards.
thgtoa - The Hitchhiker’s Guide to Online Anonymity