metricflow
datafluent_pg
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| metricflow | datafluent_pg | |
|---|---|---|
| 4 | 3 | |
| 1,073 | 27 | |
| 2.5% | - | |
| 9.8 | 5.4 | |
| 3 days ago | almost 3 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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.
metricflow
- MetricFlow allows you to define, build, and maintain metrics in code.
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Show HN: MetricFlow – open-source metric framework
Three things:
First, MetricFlow does not currently support MySQL. We launched with support for BigQuery, Redshift, and Snowflake. I have opened an issue to add support for MySQL (and similar issues for other SQL engines are coming): https://github.com/transform-data/metricflow/issues/27
Second, what we call a data source is more similar to a table in a database, rather than the underlying database service itself. Metricflow itself is useful when you're using a single SQL engine - indeed, that's all we support today - but it is most useful when you're in a world where joins are a thing. That said, if you have one big data table you might still find it useful to have declarative metric definitions defined in Metricflow. Suppose, for example, you had a big NoSQL style table filled with JSON objects. You might define a few data sources that normalize those JSON objects into top level elements (identifiers, dimensions, aggregated measures) using the sql_query data source config attribute, and then that'd allow you to support structured queries on the data consumption end while pushing unstructured blobs from your application layer. This will be slow at query time, and only as reliable as the level of discipline exerted in your application development workflow, but it's possible.
Third, if we did support MySQL you'd basically connect to it via standard connection parameters - we have a config file where you can store the required information and then we'll manage the connections for you. However, I'm not familiar with uxwizz, and a quick perusal of their documentation did not turn up how one goes about connecting to the underlying DB. It's likely I just missed this, but at any rate I don't know how it is done. If they don't support standard MySQL client connections you'd need to write an adapter of some kind against whatever DB connection APIs they provide, in which case you'd likely need to roll a custom implementation of MetricFlow's SqlClient interface and initialize the MetricFlowEngine with that.
datafluent_pg
What are some alternatives?
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
dbt_metrics - Macros for calculating metrics
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
dictum - Describe business metrics with YAML, query and visualize in Jupyter with zero SQL
dbt - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. [Moved to: https://github.com/dbt-labs/dbt-core]