{{ DESCRIPTION }}
Project description
datapackage-pipelines-fiscal
Extension for datapackage-pipelines used for loading Fiscal Data Packages into:
- S3 (or compatible) storage, in a denormalized form
- a database in normalized form.
- Metadata will be stored in an elasticsearch instance (if available), OpenSpending compatible
- A
babbage
model will also be generated for querying the database using its API
This extension works with a custom source spec and a set of processors. The generator will convert the source spec into a set of inter-dependent pipelines, which when run in order will perform data processing and loading to selected endpoints (based on environment variables).
fiscal.source-spec.yaml
Each source-spec contains information regarding a single Fiscal Data Package.
Top level properties are:
title
Title, or Display name, of the data package
dataset-name
[OPTIONAL]
A slug to be used as the data package's name.
If not provided, a slugified version of the title will be used.
resource-name
[OPTIONAL]
A slug to be used as the main resource's name in the final data package.
If not provided, the dataset name will be used.
owner-id
The id of the owner of this datapackage.
This identifier is used to generate various paths and storage names.
sources
Contains a non-empty list of data sources for the fiscal data package.
Each data source has these properties:
url
: The location of the dataname
: [OPTIONAL] A name for this source (will later be used as an intermediate resource name)
Other tabulator
parameters can also be added as properties here, e.g. sheet
, encoding
, compression
etc.
fields
Contains a non-empty list of fields for the fiscal data package.
Each field definition has these properties:
header
: Thename
of the field in the resulting resourcetitle
[OPTIONAL]: The display name of the field in the resulting resourcecolumnType
: The ColumnType of the fieldoptions
: Extra options to be added to the field, e.g. json-table-schema properties such asdecimalChar
etc.
measures
[OPTIONAL]
Extra information for measure normalization processing. (Measure normalization is the process of reducing the number of measures to one while multipltying the number of rows and adding extra columns to contain values for identifying the original measure).
Contains the following sub-properties:
currency
: The currency code of the output measure columntitle
[OPTIONAL]: The title for the output measure columnmapping
: Unpivoting map.
The unpivoting map is a map from a measure's name to its unpivoting data.
"Unpivoting data" is a map from an extra column's name to a value
Example:
measures:
currency: GTQ
mapping:
APPROVED:
PHASE_ID: "0"
PHASE: Inicial
RELEASED:
PHASE_ID: "1"
PHASE: Vigente
COMMITTED:
PHASE_ID: "2"
PHASE: Comprometido
currencies
[OPTIONAL]: List of currency codes to convert to ('USD' by default). See next section for details
currency-conversion
[OPTIONAL]
Instructions for adding an extra column or columns with measure values in another currency.
-
date_measure
[OPTIONAL]: Column name from which a date can be extracted. If not provided, a guess will be made according to the ColumnType. -
title
[OPTOINAL]: Title for the currency-converted measure columns.
datapackage-url
[OPTIONAL]
Contains the URL for a source datapackage from which this data came from. If provided, metadata for this datapackage will be loaded from this URL.
deduplicate
[OPTIONAL]
If true
, then the source data will be processed to remove duplicate rows (i.e. rows which have the same values in the primary key). Measure values for these rows will be summed in order to generate a single output row.
postprocessing
[OPTIONAL]
A list of extra processors (and parameters) that will be applied to the data.
Format is as in any pipeline-spec.yaml
Generated Pipelines
./denormalized_flow
- Loads external metadata
- Collects all data from all sources
- Combines different sources onto one unified stream
- Does measure normalization
- Does currency conversion
- Does row deduplication
- Does extra processing steps
Outputs:
- Denormalized data (local file)
- List of fiscal years in a separate resource (local file)
- Updates os package registry (if configured)
./finalize_datapackage_flow_splitter
(depends on ./denormalized_flow
)
- Loads denormalized package
- Writes separate per-year filtered copies of the data
./finalize_datapackage_flow
(depends on ./finalize_datapackage_flow_splitter
)
- Loads all resources from the
splitter
pipeline as well as the full denormalized dataset
Outputs:
- Stores results in S3 bucket
- Zip file with the datapackage (in case an S3 bucket is not configured)
- Updates os package registry (if configured)
./dimension_flow_{hierarchy}
(depends on ./denormalized_flow
)
- Loads denormalized data
- Picks only hierarchy columns
- Add auto-incrementing id column
- Remove duplicates
Outputs:
- Normalized hierarchy data (local file)
./normalized_flow
(depends on ./denormalized_flow
and all ./dimension_flow_{hierarchy}
)
- Loads denormalized data as fact table
- Loads all normalized hierarchy data
- Creates babbage model
- Replaces all hierarchy columns in fact table with corresponding ids from normalized hierarchy tables
Outputs:
- Normalized fact table (local file)
- Updates os package registry (if configured)
./dumper_flow_{hierarchy}
(depends on corresponding ./dimension_flow_{hierarchy}
)
- Loads normalized hierarchy data
- Fixes nulls in primary key (replacing them with empty strings)
Outputs
- Saves data as a single table in an SQL database
./dumper_flow
(depends on ./normalized_flow
)
- Loads normalized fact table data
- Fixes nulls in primary key (replacing them with empty strings)
Outputs
- Saves data as a single table in an SQL database
./dumper_flow_update_status
(depends on ./dumper_flow
)
Outputs
- Updates os package registry (if configured) that the package was loaded successfully
Environment variables
DPP_DB_ENGINE
- connection string for an SQL database to dump data into
ELASTICSEARCH_ADDRESS
[OPTIONAL] - connection string for an elasticsearch instance (used for package registry updating)
S3_BUCKET_NAME
[OPTIONAL] - S3 bucket for uploading data. If not provided, local ZIP files will be created instead.
AWS_ACCESS_KEY_ID
- S3 credentials (required if S3 bucket was specified)
AWS_SECRET_ACCESS_KEY
- S3 credentials (required if S3 bucket was specified)
Dependencies
In order to fully run the fiscal datapackage flow you need to have os-types
installed, using npm:
$ npm install -g os-types
This external node.js utility is used to perform fiscal modelling for the processed datapackage.
Contributing
Please read the contribution guideline:
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