Datasource configuration
CloudQuant Data Liberator supports a wide range of datasource types for ingesting time series data. Each datasource requires a connection (how to reach the data) and a dataset (what data to extract and how to interpret it).See Supported Data Formats for the canonical list of file extensions and data source categories Liberator can ingest, including formats added in 2.1 and 2.2.
Supported datasource types
File-based sources
Database sources
File and data formats
Beyond the delimited text examples in each connection guide, Liberator also ingests Parquet, Arrow IPC, Excel, XML, HDF5, ZIP archives, database tables, and API payloads. PSV (2.1+) and PCAP/PCAPng FIX capture (2.2+) are documented in Supported Data Formats.Architecture: connection + dataset
Every datasource in CloudQuant Data Liberator is composed of two parts:Connection
Defines how to reach the data — credentials, endpoints, paths, and transport protocol.Dataset
Defines what to extract — which table/files, timestamp columns, key columns, schema, and data frequency.Common configuration concepts
Timestamp configuration
All datasources require timestamp configuration to map source data into CloudQuant Data Liberator’s microsecond timestamp (muts) format:
Supported datetime formats
Key column configuration
Thedata_key_column field defines the symbol/key used for filtering queries:
Schema definition
Each column in a dataset schema requires:string, int64, uint64, double, float, bool, date32, date64, time64
Column groups:
key— Symbol/key columnstime— Timestamp columnsvalue— Data columnsmeta— System columns (_seq,muts, etc.)

