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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

The data_key_column field defines the symbol/key used for filtering queries:

Schema definition

Each column in a dataset schema requires:
Column types: string, int64, uint64, double, float, bool, date32, date64, time64 Column groups:
  • key — Symbol/key columns
  • time — Timestamp columns
  • value — Data columns
  • meta — System columns (_seq, muts, etc.)

Auto-generated columns

CloudQuant Data Liberator automatically generates these columns if not present in source data:

File name date extraction

For file-based sources, dates can be extracted from filenames: