recessionporn/bigmetadata repository overview

⁠Bigmetadata

⁠What it is

Bigmetadata comprises four parts:

ETL | Observatory | Metadata | Catalog

Everything is dockerized and can be run on a standalone EC2 instance provided Docker is available.

• ETL: Luigi tasks that extract data from anywhere on the web, transform the data in modular and observable steps, generate metadata, and upload observatory datasets in the process. While the ETL pushes transformed datasets to CartoDB, it actually is backed by its own separate Postgres/PostGIS database.

• Observatory: transformed and standardized datasets living on a CartoDB instance⁠. This is where we can pull our actual data from, whether shuffling bytes or using PL/Proxy. This is also where preview visuals using widgets or other Carto JS interfaces can get their underlying data from.

• Metadata: human and machine-readable descriptions of the data in the observatory. Table schema can be found in tasks/meta.py⁠. There are six related tables, obs_table, obs_column_table, obs_column, obs_column_tag, obs_tag, and obs_column_to_column. An overarching denomralized view can be found in obs_meta.

• Catalog: a static HTML guide⁠ to data in the observatory generated from the metadata. Docs are generated using Sphinx⁠ and hosted on GitHub pages.

⁠Installation

See QUICKSTART⁠.

⁠Tasks

Most of the common tasks have already been wrapped up in the Makefile.

• make catalog: Regenerate the catalog
• make pdf-catalog: Regenerate the PDF version of the catalog
• make deploy-catalog: Deploy the catalog to Github Pages. This will also rebuild both the HTML and PDF catalogs
• make acs: ETL the entirety of the American Community Survey (Census)
• make tiger: ETL the boundary files of the census
• make sh: Drop into the bigmetadata container to run shell scripts
• make python: Drop into an interactive Python shell in the bigmetadata container
• make psql: Drop into an interactive psql session in the bigmetadata container's database
• make sync: Sync local data and metadata to the Observatory account.

Any other task can be run using docker-compose:

docker-compose run bigmetadata luigi --module tasks.path.to.task \
  TaskName --param1 val1 --param2 val2

Or, more conveniently, make -- run (which will use the local scheduler):

make -- run path.to.task TaskName --param1 val1 --param2 val2

For example, to run QCEW numbers for one quarter:

make -- run us.bls QCEW --year 2014 --qtr 4

⁠Naming conventions

⁠Tables

In bigmetadata's postgres, tables live in the observatory schema, with an auto-generated hash for the tablename prefixed by obs (EG obs_<hash>). In obs_table, the table ID refers back to the task which created that table as well as the parameters called.

⁠Columns

Column IDs in metadata are fully-qualified, like tables, such as us.bls.avg_wkly_wage_trade_transportation_and_utilities. In tables themselves, column names are the colname from metadata, so the above would be avg_wkly_wage_trade_transportation_and_utilities in an actual table.

⁠Metadata

⁠Why favor descriptions of columns over tables in metadata?

Our users need third party data in their maps to make it possible for them to better interpret their own data.

In order to make this as simple as possible, we need to rethink the prevailing model of finding external data by where it's sourced from. Instead, we should think of finding external data by need.

For example, instead of requiring our user to think "I need race or demographic data alongside my data, I'm in the US, so I should look at the census", we should enable our user to look up race or demographic data -- and figure out which columns they need from the census without particularly worrying about delving into the source.