Data Sources

A Data Source in Canso is a reference to raw data that is either generated and owned by users or defined by users and owned by Canso's Pre-processing pipelines. Currently, Canso supports tabular data.

Introduction

Data Sources provide an abstraction over datasets owned by users, enabling:

• A standardized way of declaring raw data for feature and pre-processing table calculations.

• A uniform user experience when defining features, allowing Data Scientists to focus on feature logic without worrying about underlying data, DB connections, or access.

• Reusability in defining and materializing features and pre-processing jobs.

• Improved understanding of raw data.

Data Sources can be of 2 types at the very least

1. Batch Data Sources

2. Streaming Data Sources

Data Source Types

Batch Data Sources

Batch Data Sources are typically Data Warehouses (BigQuery, Redshift, etc.) or Object Storages (S3, GCS, etc.). In Canso currently we are supporting S3 Data Source only.

Batch Data Source Attributes (S3 & GCS)

S3 and GCS Batch Data Sources, can be described by the following attributes. These attributes define how data is stored and accessed in object storage, allowing for standardized and reusable data processing.

Attribute	Description	Example
data_source_name	Unique Name of the datasource	raw_us_orders
bucket	Bucket Name	mycompany_data
base_key	Fixed Key Component	raw_txns/orders/us
varying_key_suffix_format	Varying Time-based Key Component suffixed to Fixed Key Component	%Y-%m-%d/%H %Y-%m-%d/%H-%M
varying_key_suffix_freq	Frequency of varying_key_suffix_format	30min 3H 12H 1D
time_offset	Optional time offset in seconds	3600
file_type	File Type	CSV, PARQUET
description	Description of what the data source contains	Daily Raw orders placed by users in the US
owner	Team that owns the data source	['[e-mail]', '[e-mail]']
schema_path	Path where PySpark and BQ Schemas of the data are persisted	-
event_timestamp_field	Column indicating the event time	ordered_at
event_timestamp_format	Format of the event_timestamp_field	PySpark supported data/time formats
created_at	Time when the Data Source was created	datetime(2021, 1, 1, 0, 0, 0)

The path/key to data files for a data source are obtained using

• bucket

• base_key

• varying_key_suffix_format

• varying_key_suffix_freq

• time_offset

Canso uses the concept of Data Spans, which supports various ways in which a user's data in stored in Object Storage. To understand more about how Data Spans are used, see Data Spans

Streaming Data Sources

Canso supports Kafka as a data source. This can be used for real-time data processing and feature generation. Kafka data sources enable:

• Real-time data ingestion.

• Stream processing for immediate feature extraction.

• Continuous updates to machine learning models based on live data streams.

Streaming Data Source Attributes (Kafka)

Attribute	Description	Example
name	Unique Name of the streaming datasource	user_activity_stream
description	Description of what the streaming data source contains	Real-time user activity data from the app
owners	Tenants that owns the data source	['[e-mail]', '[e-mail]']]
topic	Kafka topic from which the data is read	user_activity_topic
schema	Schema definition of the streaming data	{"user_id": "STRING", "activity_type": "STRING", "timestamp": "TIMESTAMP"}
timestamp_field	Field indicating the event time	timestamp
timestamp_format	Format of the timestamp_field	yyyy-MM-dd HH:mm:ssXXX
bootstrap_server	Kafka bootstrap server	12.345.678.910:9092
read_configs	Configuration settings for reading from Kafka	{"datasource": "streaming_data_source", "watermark_delay_threshold": "10 seconds", "starting_timestamp": None, "starting_offsets_by_timestamp": {}, "starting_offsets": "earliest", "fail_on_data_loss": False, "include_headers": True}
cloud_provider	Cloud provider where Kafka is hosted	AWS

Special notes on attributes

• read_configs: The standard set of attributes we support when registering a data source. Users can provide some configurations at the time of feature registration. Currently, this option is not enabled, but we will be adding support for them soon.

Working with Data Sources

Once a DataSource has been defined, it can be

1. Registered for re-usability across different teams

2. Referenced to create ML features

Example objects of Data Sources

• S3 Data Source

• Kafka Data Source

Tool Tips

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