corp
sql-style-guide
| corp | sql-style-guide | |
|---|---|---|
| 12 | 4 | |
| 413 | 991 | |
| -0.2% | - | |
| 4.6 | 0.0 | |
| 18 days ago | 8 months ago | |
| Apache License 2.0 | - |
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corp
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Are there database design Standards out there? As in, formal documents listing exact best practices for OLTP database design?
Here's one that covers some of your points and that I like in general: https://github.com/dbt-labs/corp/blob/main/dbt_style_guide.md Except instead of prefixing my table names with the processing stage, I keep them in schemas by processing stage (source, staging, analytics). So, I can tell my analysts to look into the analytics schema for all the final tables, and they won't be bothered by intermediate models. The table names also have a precise structure that corresponds to our specific subject.
- Looking to understand why the dbt style guide recommends to use *all lower case* for keywords, field names, and function names?
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Best practices for data modeling with SQL and dbt
I find the content more or less ripped from of dbt's own styleguide
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SQL Code Style Properties Questions
For anyone wondering this is the DBT style guide I am referencing from.
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A modern data stack for startups
While the tool choice is obvious, how to use dbt is going to be a more controversial. There's a load of great resources on dbt best practices, but as you can see from my Slack questions, there's enough ambiguity to tie you up.
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Completed my first Data Engineering project with Kafka, Spark, GCP, Airflow, dbt, Terraform, Docker and more!
Just a slight critique, but I noticed some of the dbt models are a bit hard to read. Especially your dim_users SCD2 model, which uses lots of nested subqueries and multiple columns on the same line. You may want to refer to this style guide from dbt Labs. I find CTEs are a lot easier to parse and read.
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What are some good resources for learning to write clean, production-quality code?
I really like thisthis SQL STYLE GUIDE, and if you use dbt, the dbt style guide.
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How do you format your SQL queries?
I like this one very much from dbt very much.
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Where do you like to do the L of ELT? Python or DBT?
I recommend you write one. You can take inspiration from dbt's one or Gitlab
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Confused about benefits of CTE
I've seen fishtown analytics coding conventions recommend a lot around here, but there are a few things about their recommendations of CTE use that confuse me.
sql-style-guide
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Going Full Time on My SaaS After 13 Years
Very cool to see you on HN Matt!
I know Matt from the data world where his SQL Style Guide[0] was always at odds with the one we maintained at GitLab[1]. :-D
Posts like this are great to show folks just how long it can take to do things. I love his story and am eager to watch Preceden grow!
[0] https://github.com/mattm/sql-style-guide
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What are some good resources for learning to write clean, production-quality code?
I really like thisthis SQL STYLE GUIDE, and if you use dbt, the dbt style guide.
- Recommended guide/reference on the ideal formatting for SQL code for readability?
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Y’all got any coding best practices for sql?
Matt Mazur has a great SQL style guide on github
What are some alternatives?
nodejs-bigquery - Node.js client for Google Cloud BigQuery: A fast, economical and fully-managed enterprise data warehouse for large-scale data analytics.
sqlfluff - A modular SQL linter and auto-formatter with support for multiple dialects and templated code.
terraform - Terraform enables you to safely and predictably create, change, and improve infrastructure. It is a source-available tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned.
Blazorise - Blazorise is a component library built on top of Blazor with support for CSS frameworks like Bootstrap, Tailwind, Bulma, AntDesign, and Material.
pgsink - Logically replicate data out of Postgres into sinks (files, Google BigQuery, etc)
streamify - A data engineering project with Kafka, Spark Streaming, dbt, Docker, Airflow, Terraform, GCP and much more!
spark-bigquery-connector - BigQuery data source for Apache Spark: Read data from BigQuery into DataFrames, write DataFrames into BigQuery tables.