metricflow VS dictum

Compare metricflow vs dictum and see what are their differences.

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metricflow dictum
4 2
1,073 19
2.5% -
9.8 0.0
4 days ago over 1 year ago
Python Python
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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

Posts with mentions or reviews of metricflow. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-06.
  • MetricFlow allows you to define, build, and maintain metrics in code.
    1 project | /r/CKsTechNews | 6 Apr 2022
  • Show HN: MetricFlow – open-source metric framework
    4 projects | news.ycombinator.com | 6 Apr 2022
    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.

dictum

Posts with mentions or reviews of dictum. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-22.

What are some alternatives?

When comparing metricflow and dictum you can also consider the following projects:

dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io

dbt_metrics - Macros for calculating metrics

sheetfu - Python library to interact with Google Sheets V4 API

Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]

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]

superset - Apache Superset is a Data Visualization and Data Exploration Platform

datafluent_pg - Build a better understanding of your data in PostgreSQL.

Flight-Test-Data-Analytics-Module-01 - Code to support Module 01 of the Daedalus Aerospace Flight Test Data Analytics course.

gitbi - Lightweight BI app based on git repo

chinook-database - Sample database for SQL Server, Oracle, MySQL, PostgreSQL, SQLite, DB2