dbt-core
prql
dbt-core | prql | |
---|---|---|
86 | 106 | |
8,985 | 9,489 | |
3.0% | 1.3% | |
9.7 | 9.9 | |
3 days ago | 3 days ago | |
Python | Rust | |
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.
prql
- Prolog language for PostgreSQL proof of concept
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SQL is syntactic sugar for relational algebra
> I completely attribute this to SQL being difficult or "backwards" to parse. I mean backwards in the way that in SQL you start with what you want first (the SELECT) rather than what you have and widdling it down.
> The turning point for me was to just accept SQL for what it is.
Or just write PRQL and compile it to SQL
https://github.com/PRQL/prql
- Transpile Any SQL to PostgreSQL Dialect
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Show HN: Open-source, browser-local data exploration using DuckDB-WASM and PRQL
Hey HN! We’ve built Pretzel, an open-source data exploration and visualization tool that runs fully in the browser and can handle large files (200 MB CSV on my 8gb MacBook air is snappy). It’s also reactive - so if, for example, you change a filter, all the data transform blocks after it re-evaluate automatically. You can try it here: https://pretzelai.github.io/ (static hosted webpage) or see a demo video here: https://www.youtube.com/watch?v=73wNEun_L7w
You can play with the demo CSV that’s pre-loaded (GitHub data of text-editor adjacent projects) or upload your own CSV/XLSX file. The tool runs fully in-browser—you can disconnect from the internet once the website loads—so feel free to use sensitive data if you like.
Here’s how it works: You upload a CSV file and then, explore your data as a series of successive data transforms and plots. For example, you might: (1) Remove some columns; (2) Apply some filters (remove nulls, remove outliers, restrict time range etc); (3) Do a pivot (i.e, a group-by but fancier); (4) Plot a chart; (5) Download the chart and the the transformed data. See screenshot: https://imgur.com/a/qO4yURI
In the UI, each transform step appears as a “Block”. You can always see the result of the full transform in a table on the right. The transform blocks are editable - for instance in the example above, you can go to step 2, change some filters and the reactivity will take care of re-computing all the cells that follow, including the charts.
We wanted Pretzel to run locally in the browser and be extremely performant on large files. So, we parse CSVs with the fastest CSV parser (uDSV: https://github.com/leeoniya/uDSV) and use DuckDB-Wasm (https://github.com/duckdb/duckdb-wasm) to do all the heavy lifting of processing the data. We also wanted to allow for chained data transformations where each new block operates on the result of the previous block. For this, we’re using PRQL (https://prql-lang.org/) since it maps 1-1 with chained data transform blocks - each block maps to a chunk of PRQL which when combined, describes the full data transform chain. (PRQL doesn’t support DuckDB’s Pivot statement though so we had to make some CTE based hacks).
There’s also an AI block: This is the only (optional) feature that requires an internet connection but we’re working on adding local model support via Ollama. For now, you can use your own OpenAI API key or use an AI server we provide (GPT4 proxy; it’s loaded with a few credits), specify a transform in plain english and get back the SQL for the transform which you can edit.
Our roadmap includes allowing API calls to create new columns; support for an SQL block with nice autocomplete features, and a Python block (using Pyodide to run Python in the browser) on the results of the data transforms, much like a jupyter notebook.
There’s two of us and we’ve only spent about a week coding this and fixing major bugs so there are still some bugs to iron out. We’d love for you to try this and to get your feedback!
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Pql, a pipelined query language that compiles to SQL (written in Go)
> Looks like PRQL doesn't have a Go library so I guess they just really wanted something in Go?
There's some C bindings and the example in the README shows integration with Go:
https://github.com/PRQL/prql/tree/main/prqlc/bindings/prqlc-...
- FLaNK Stack 26 February 2024
- FLaNK Stack Weekly 19 Feb 2024
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PRQL as a DuckDB Extension
Can someone tell me why PRQL is better? I went here: https://github.com/PRQL/prql
It looks nice, but what's the strengths compared to SQL?
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Shouldn't FROM come before SELECT in SQL?
PRQL [1] is a compile-to-SQL relational querying language that puts FROM first.
[1] https://prql-lang.org
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Vanna.ai: Chat with your SQL database
https://prql-lang.org/ might be an answer for this. As a cross-database pipelined language, it would allow RAG to be intermixed with the query, and the syntax may(?) be more reliable to generate
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.
malloy - Malloy is an experimental language for describing data relationships and transformations.
metricflow - MetricFlow allows you to define, build, and maintain metrics in code.
Preql - An interpreted relational query language that compiles to SQL.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
bustub - The BusTub Relational Database Management System (Educational)
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
tresql - Shorthand SQL/JDBC wrapper language, providing nested results as JSON and more
citus - Distributed PostgreSQL as an extension
spyql - Query data on the command line with SQL-like SELECTs powered by Python expressions
dagster - An orchestration platform for the development, production, and observation of data assets.
toydb - Distributed SQL database in Rust, written as a learning project