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 denormalized 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 deploy-html-redirect: Deploy a redirect (https://carto.com/data/) to Github Pages
• make catalog: Regenerate the catalog
• make deploy-html-catalog: [DEPRECATED] Deploy the catalog to Github Pages
• 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 rebuild-all: Task to clean and rebuild all the *-all tasks. You can disable the heavy tasks, which by now is us-all adding RUN_HEAVY_TASKS=false

The ETL tasks have also already been wrapped up in the Makefile:

• make [docker-]au-all: ETL the entirety of the Australian data
• make [docker-]br-all: ETL the entirety of the Brazilian data
• make [docker-]ca-all: ETL the entirety of the Canadian data
• make [docker-]es-all: ETL the entirety of the Spanish data
• make [docker-]eu-all: ETL the entirety of the Eurostat data
• make [docker-]fr-all: ETL the entirety of the French data
• make [docker-]mx-all: ETL the entirety of the Mexican data
• make [docker-]uk-all: ETL the entirety of the United Kingdom data
• make [docker-]us-all: ETL the entirety of the United States data

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

Or using Docker:

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

If you want to use the local scheduler, you can add SCHEDULER=--local-scheduler to the make task

Execution watcher

We've added a new script to watch for containers execution and notify when it ends. Is located in scripts/watch_containers.py

usage: python3 watch_containers.py [-h] [--since SINCE]
                                   [--pooling-time POOLING_TIME]
                                   [--notification-channel {stdout,slack,logfile}]
                                   name

Let me explain the options:

• name: Name, complete or a part of it, of the container to watch
• since: [optional] Date in the YYYY-mm-dd HH:MM:SS UTC format since the script starts looking for docker containers to watch. Default value is utcnow() value
• pooling-time: [optional] How many seconds we check for containers completion. Default value is 60 seconds
• notification-channel: [optional] Where to send the notifications: stdout or slack. Default value is stdout

For the slack notification you need to have an environment variable called SLACK_WEBHOOK that contains the URL for the incoming webhook you've created to send the messages.

Setting up local directories

docker-compose.yml allows configuring two environment variables:

• DO_LOCAL_POSTGRESQL_LIB_DIR (default: ./postgres/data).
• DO_LOCAL_POSTGRESQL_TMP_DIR (default: ./tmp).

That's useful if you have an external HD for storing DO.

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.