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A framework for transforming tabular (CSV, SQL) and hierarchical data (JSON, XML) into property graphs and ingesting them into graph databases (ArangoDB, Neo4j)
A framework for transforming tabular (CSV, SQL) and hierarchical data (JSON, XML) into property graphs and ingesting them into graph databases (ArangoDB, Neo4j).
GraphCast works with property graphs, which consist of:
The Schema defines how your data should be transformed into a graph and contains:
Resources are your data sources that can be:
Full documentation is available at: growgraph.github.io/graphcast
pip install graphcast
from suthing import ConfigFactory, FileHandle
from graphcast import Schema, Caster, Patterns
schema = Schema.from_dict(FileHandle.load("schema.yaml"))
conn_conf = ConfigFactory.create_config({
"protocol": "http",
"hostname": "localhost",
"port": 8535,
"username": "root",
"password": "123",
"database": "_system",
}
)
patterns = Patterns.from_dict(
{
"patterns": {
"work": {"regex": "\Sjson$"},
}
}
)
schema.fetch_resource()
caster = Caster(
schema,
)
caster.ingest_files(
path="./data",
conn_conf=conn_conf,
patterns=patterns,
)
To install requirements
git clone git@github.com:growgraph/graphcast.git && cd graphcast
uv sync --dev
Spin up Arango from arango docker folder by
docker-compose --env-file .env up arango
and Neo4j from neo4j docker folder by
docker-compose --env-file .env up neo4j
To run unit tests
pytest test
Contributions are welcome! Please feel free to submit a Pull Request.
FAQs
A framework for transforming tabular (CSV, SQL) and hierarchical data (JSON, XML) into property graphs and ingesting them into graph databases (ArangoDB, Neo4j)
We found that graphcast demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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