dbt-core
monosi
Our great sponsors
dbt-core | monosi | |
---|---|---|
86 | 20 | |
8,881 | 320 | |
3.4% | 1.3% | |
9.7 | 0.0 | |
6 days ago | over 1 year ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dbt-core
- Dbt
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Relational is more than SQL
dbt integration was one of our major goals early on but we found that the interaction wasn't as straightforward as had hoped.
There is an open PR in the dbt repo: https://github.com/dbt-labs/dbt-core/pull/5982#issuecomment-...
I have some ideas about future directions in this space where I believe PRQL could really shine. I will only be able to write those down in a couple of hours. I think this could be a really exciting direction for the project to grow into if anyone would like to collaborate and contribute!
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How to Level Up Beyond ETLs: From Query Optimization to Code Generation
> Could you share more specific details? Happy to look over / revise where needed.
Sure thing! I'd say first off, the solutions may look different for a small company/startup vs. a large enterprise. It can help if you explain the scale at which you are solving for.
On the enterprise side of things, they tend to buy solutions rather than build them in-house. Things like Informatica, Talend, etc. are common for large enterprises whose primary products are not data or software related. They just don't have the will, expertise, or the capital to invest in building and maintaining these solutions in-house so they just buy them off the shelf. On the surface, these are very expensive products, but even in the face of that it can still make sense for large enterprises in terms of the bottom line to buy rather than build.
For startups and smaller companies, have you looked at something like `dbt` (https://github.com/dbt-labs/dbt-core) ? I understand the desire to write some code, but often times there are already existing solutions for the problems you might be encountering.
ORM's should typically only exist on the consumer-side of the equation, if at all. A lot of business intelligence / business analysts are just going to use tools like Tableau and hook up to the data warehouse via a connector to visualize their data. You might have some consumers that are more sophisticated and may want to write some custom post-processing or aggregation code, and they could certainly use ORM's if they choose, but it isn't something you should enforce on them because it's a poor place to validate data since as mentioned there are different ways/tools to access the data and not all of them are going to go through your python SDK.
Indeed in a large enough company, you are going to have producers and consumers that are going to use different tools and programming languages, so it's a little bit presumptuous to write an SDK in python there.
Another thing to talk about, and this probably mostly applies to larger companies - have you looked at an architecture like a distributed data mesh (https://martinfowler.com/articles/data-mesh-principles.html)? This might be something to bring to the CTO more than try to push for yourself, but it can completely change the landscape of what you are doing.
> More broadly is the issue of the gap of what you think the role is, and what the role actually is when you join. There are definitely cases where this is accidental. The best way I can think of to close the gap is to maybe do a short-term contract, but may be challenging to do under time constraints etc.
Yeah this definitely sucks and it's not an enviable position to be in. I guess you have a choice to look for another job or try to stick it out with the company that did this to you. It's possible there is a geniune existential crisis for the company and a good reason why they did the bait-and-switch. Maybe it pays to stay, especially if you have equity in the company. On the other hand, it could also be the case that it is the result of questionable practices at the company. It's hard to make that call.
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Python: Just Write SQL
I really dislike SQL, but recognize its importance for many organizations. I also understand that SQL is definitely testable, particularly if managed by environments such as DBT (https://github.com/dbt-labs/dbt-core). Those who arrived here with preference to python will note that dbt is largely implemented in python, adds Jinja macros and iterative forms to SQL, and adds code testing capabilities.
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Transform Your Data Like a Pro With dbt (Data Build Tool)
3). Data Build Tool Repository.
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What are your thoughts on dbt Cloud vs other managed dbt Core platforms?
dbt Cloud rightfully gets a lot of credit for creating dbt Core and for being the first managed dbt Core platform, but there are several entrants in the market; from those who just run dbt jobs like Fivetran to platforms that offer more like EL + T like Mozart Data and Datacoves which also has hosted VS Code editor for dbt development and Airflow.
- How do I build a docker image based on a Dockerfile on github?
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Dbt vs. SqlMesh
Ahh I misunderstood, yes column level lineage is useful. DBT prefers leveraging macros which sort of breaks this pattern. I think the DBT way would be to better separate fields into upstream models and use table tracking https://github.com/dbt-labs/dbt-core/discussions/4458
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DBT core v1.5 released
Here’s the PR, which includes a what/how/why: https://github.com/dbt-labs/dbt-core/issues/7158
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DBT Install
I've attached a link to their documentation. DBT is becoming increasingly popular within the Data Engineering community with over 5k stars on github.
monosi
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Open source data observability tools with UI?
I also found https://github.com/monosidev/monosi but it seems there are no activities in the repository from last year.
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Databricks monitoring/observability
I'm building an open source data observability platform - https://github.com/monosidev/monosi that visualizes metadata collected from data warehouses. Databricks is currently not supported (contributions welcome!), but it may help to take a look at how we approach the anomaly detection & visualization aspects.
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Monitor PostgreSQL for anomalies in ingested data
Building an open source tool that lets you monitor PostgreSQL instances form anomalies in data coming in - https://github.com/monosidev/monosi
- Open Source Data Observability for BigQuery
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Metadata extraction and management
It’s open source, check out the repository here - https://github.com/monosidev/monosi
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How to Monitor Supabase with Monosi
🎉 Congratulations, you've just set up and scheduled a data monitor on your Supabase instance. You can now add more monitors to other tables in your database. Find more information on how to use Monosi here.
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Setting up data monitoring for PostgreSQL
Now that you’ve worked through an example using a public PostgreSQL instance, you can further extend this to your own data store. For more information, get started here.
- Monosi v0.0.3 Released! Open source Data Observability now with a Web UI, Postgres Support, & more.
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Sunday Daily Thread: What's everyone working on this week?
Continuing to build out & stabilize Monosi (open source data observability) - https://github.com/monosidev/monosi
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Data pipeline suggestions
Observability: Monosi
What are some alternatives?
airbyte - The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.
datahub - The Metadata Platform for your Data Stack
metricflow - MetricFlow allows you to define, build, and maintain metrics in code.
jitsu - Jitsu is an open-source Segment alternative. Fully-scriptable data ingestion engine for modern data teams. Set-up a real-time data pipeline in minutes, not days
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
castled - Castled is an open source reverse ETL solution that helps you to periodically sync the data in your db/warehouse into sales, marketing, support or custom apps without any help from engineering teams
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
soda-spark - Soda Spark is a PySpark library that helps you with testing your data in Spark Dataframes
citus - Distributed PostgreSQL as an extension
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
dagster - An orchestration platform for the development, production, and observation of data assets.
great_expectations - Always know what to expect from your data.