Data Connections

Prometheux connects to a wide range of data sources — local and cloud files, relational and graph databases, data warehouses, vector stores, and HTTP APIs — so you can read source data into your concepts and write results back out. Connections also work across cloud and distributed file systems such as S3 and HDFS, supporting modern data lake and data migration scenarios. Under the hood, every connection is expressed as a @bind annotation that ties a concept to a data source for reading or writing. The connection covers the source type, its options (host, credentials, file path, query, etc.), the database or path, and the table or file name.

For the complete syntax, every supported option, and end-to-end examples, see the full reference: Connecting to data sources.

Supported connectors

Files and object storage

Connector	Use it for
`csv`	CSV files (reading and writing big CSV files)
`parquet`	Parquet columnar files, well suited to data lakes
`iceberg`	Apache Iceberg tables (catalog-managed, time travel, branches/tags)
`excel`	Excel spreadsheets, including specific sheets
`json`	JSON files with nested structures, queryable via SQL or `struct:get`
`cobol`	Legacy COBOL / EBCDIC mainframe extracts (with a copybook)
`text`	Plain text files
`binary`	Binary files (PDF, images, etc.)

Relational databases

Connector	Database
`postgresql`	PostgreSQL (including Supabase via the Transaction Pooler)
`mysql`	MySQL
`mariadb`	MariaDB
`oracle`	Oracle
`sqlserver`	SQL Server
`db2`	DB2
`sqlite`	SQLite
`h2`	H2
`sybase`	Sybase
`teradata`	Teradata

Warehouses, query engines, and cloud

Connector	Source
`snowflake`	Snowflake
`databricks`	Databricks
`redshift`	Amazon Redshift
`bigquery`	Google BigQuery
`hive`	Hive
`presto`	Presto
`dynamodb`	Amazon DynamoDB

Graph, vector, and APIs

Connector	Source
`neo4j`	Neo4j graph database (query with Cypher)
`qdrant`	Qdrant vector database
`api`	HTTP APIs (e.g. consuming data over REST)

How a connection works

A connection binds a concept to a data source. The same @bind mechanism is used both to read source data into a concept and to write a concept’s results back to a destination.

@bind("concept_name",
      "datasource_type option_1='value_1', option_2='value_2', …, option_n='value_n'",
      "database_name",
      "table_name").

Common connection options across database connectors include:

url — connection URL (e.g. jdbc:postgresql://localhost:5432/prometheux)
protocol — connection protocol (jdbc, odbc, jdbc-odbc, bolt)
host — database host
port — database port (e.g. 5432 for PostgreSQL, 7687 for Neo4j)
database — database name
username / password — credentials

Syntax matters. Options must be comma-separated and each value must be quoted (host='localhost', not host=localhost). Use username= rather than user=, and end the annotation with a dot. See Connecting to data sources for correct and incorrect examples.

Example: reading a CSV file

@bind("myCsv", "csv useHeaders=true", "/path_to_csv/folder", "csv_name.csv").
myAtom(X,Y,Z) <- myCsv(X,Y,Z).
@output("myAtom").

Example: connecting to PostgreSQL

@bind("customer_postgres", "postgresql host='postgres-host', port=5432, username='prometheux', password='myPassw'",
      "prometheux", "customer").

customer_postgres_test(CustomerID, Name, Surname, Email) <-
        customer_postgres(CustomerID, Name, Surname, Email).

@output("customer_postgres_test").

Managing credentials

Sensitive credentials (database usernames and passwords, AWS access/secret keys, API keys) can be supplied directly as options in the connection, which keeps everything self-contained in one place. For better security and reusability, credentials can instead be stored externally:

In a px.properties file, centralizing sensitive information without hardcoding it in each connection.
Via REST APIs for dynamic configuration management, setting individual credentials or updating many at once through API endpoints.

When connecting to a database you only need to read from, create a dedicated read-only user scoped to the specific tables you need. This follows the principle of least privilege and limits exposure.

Learn more

Connecting to data sources — full connector reference, every option, and complete examples for each source type
Annotations — @bind, @qbind, @mapping, and @model
SQL integration — querying bound sources with SQL in rule bodies

Introduction

Get Started

Platform

Integrations

Examples

Vadalog Reference

Resources

Supported connectors

Files and object storage

Relational databases

Warehouses, query engines, and cloud

Graph, vector, and APIs

How a connection works

Example: reading a CSV file

Example: connecting to PostgreSQL

Managing credentials

Learn more

​Supported connectors

​Files and object storage

​Relational databases

​Warehouses, query engines, and cloud

​Graph, vector, and APIs

​How a connection works

​Example: reading a CSV file

​Example: connecting to PostgreSQL

​Managing credentials

​Learn more

Supported connectors

Files and object storage

Relational databases

Warehouses, query engines, and cloud

Graph, vector, and APIs

How a connection works

Example: reading a CSV file

Example: connecting to PostgreSQL

Managing credentials

Learn more