[HN Gopher] Show HN: MetricFlow - open-source metric framework
       ___________________________________________________________________
        
       Show HN: MetricFlow - open-source metric framework
        
       Hi HN community, I'm Nick, co-founder/CEO of Transform.co. I'm
       thrilled to share MetricFlow, an open-source metric creation
       framework: https://github.com/transform-data/metricflow  MetricFlow
       strives to make what has historically been an extremely repetitive
       process, writing SQL queries on core normalized data models, much
       more DRY. MetricFlow consolidates the definitions for joins,
       aggregations, filters, etc., and programmatically generates SQL to
       construct data marts. You can think of it like LookML, but more
       powerful and ergonomic (and open source!). The project has three
       components:  1. MetricFlow Spec: The specification encapsulates
       metric logic in a more reusable set of abstractions: data_sources,
       measures, dimensions, identifiers, metrics, and materializations.
       2. DataFlow Planner: The Query Planner is a generalized SQL
       constructor. We take in data sources (ideally normalized data
       models) and generate a graph of data transformations (a flow, if
       you will) - joins, aggregations, filters, etc. We take that graph
       and render it down to db-specific SQL while optimizing it for
       performance and legibility.  3. MetricFlow Interfaces: The CLI and
       Python SDK rely on the flexibility of the Spec and Planner to build
       just about any query you could ask for on top of your data
       warehouse.  These components enable novel features that other
       semantic layers struggle to support today:  - MetricFlow enables
       the user to traverse the entire graph of a company's data warehouse
       without confining their analysis to pre-built data models (dbt),
       Explores (in Looker), or Cubes (in lots of tools).  - The Metric
       abstraction allows the construction of complex metrics that
       traverse the graph described above to rely on multiple data
       sources. We support several common metric types today, and adding
       more is a critical part of the open-source roadmap.  - The
       Materialization abstraction allows users to define and then
       programmatically generate data marts that rely on a single DRY
       expression of the metrics and dimensions.  MetricFlow is open
       source(https://github.com/transform-data/metricflow) and
       distributed through pypi (`pip install metricflow`). You can set up
       (`mf setup`) a set of sample configs and try out a tutorial (`mf
       tutorial). The docs are all
       here(https://docs.transform.co/docs/overview/metricflow-overview).
       We'd love contributions on GitHub. We're adding new Issues to share
       our roadmap in the coming days, but feel free to open your own.
       We're also opening up a Slack
       community(https://community.transform.co/metricflow-signup) to talk
       about the project and, more generally, metric tooling.  Let us know
       what you think - we'll be here answering any questions!
        
       Author : nicholashandel
       Score  : 18 points
       Date   : 2022-04-06 22:12 UTC (48 minutes ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
       ___________________________________________________________________
       (page generated 2022-04-06 23:00 UTC)