seeknal 2.2.2

All-in-one platform for data and AI/ML engineering

Seeknal

Transform data with SQL and Python. Build ML features with point-in-time joins. Materialize to PostgreSQL and Iceberg — all from one CLI.

Seeknal is an all-in-one platform for data and AI/ML engineering. Define pipelines in YAML or Python, run them through a safe draft → dry-run → apply workflow, and materialize outputs to PostgreSQL and Apache Iceberg simultaneously. Python 3.11+ required.

Quick Start

pip install seeknal

seeknal init --name my_project
seeknal draft --name my_pipeline --type transform
seeknal dry-run
seeknal apply

Explore your data interactively or search docs from the terminal:

seeknal repl          # Interactive SQL on pipeline outputs
seeknal docs query    # Search documentation from the CLI

SELECT customer_id, COUNT(*) as order_count
FROM target.my_transform
GROUP BY customer_id;

Key Features

Dual Pipeline Authoring — Write pipelines in YAML, Python decorators, or both:

from seeknal.pipeline import source, transform

@source(name="orders", source="csv", table="data/orders.csv")
def orders():
    pass

@transform(name="order_metrics", inputs=["source.orders"])
def order_metrics(ctx):
    df = ctx.ref("source.orders")
    return ctx.duckdb.sql(
        "SELECT customer_id, SUM(amount) as total FROM df GROUP BY customer_id"
    ).df()

Multi-Target Materialization — Write to PostgreSQL and Iceberg from a single node:

materializations:
  - type: postgresql
    connection: local_pg
    table: analytics.my_table
    mode: upsert_by_key
    unique_keys: [id]
  - type: iceberg
    table: atlas.namespace.my_table

Environment Management — Isolated namespaces with per-environment profiles:

seeknal env plan dev --profile profiles-dev.yml
seeknal env apply dev
seeknal run --env dev

Feature Store — Point-in-time joins, automatic versioning, offline and online serving. Powered by DuckDB (single-node, <100M rows) or Apache Spark (distributed).

from seeknal.featurestore.duckdbengine.feature_group import FeatureGroupDuckDB, FeatureLookup, Materialization, HistoricalFeaturesDuckDB
from seeknal.entity import Entity

fg = FeatureGroupDuckDB(
    name="user_features",
    entity=Entity(name="user", join_keys=["user_id"]),
    materialization=Materialization(event_time_col="event_time"),
)
fg.set_dataframe(df).set_features()
fg.write(feature_start_time=datetime(2024, 1, 1))

# Point-in-time join (prevents data leakage)
hist = HistoricalFeaturesDuckDB(lookups=[FeatureLookup(source=fg)])
training_df = hist.to_dataframe(feature_start_time=datetime(2024, 1, 1))

Interactive SQL REPL — Auto-registers parquets, PostgreSQL, and Iceberg sources at startup. Query pipeline outputs, explore data, iterate on SQL — all without leaving the terminal.

Documentation

Getting Started	Installation, configuration, first pipeline
CLI Reference	All commands and flags
YAML Schema	Pipeline YAML reference
CLI Docs Search	Search documentation from the terminal (seeknal docs)
Tutorials	YAML Pipelines · Python Pipelines · Mixed
Guides	Python Pipelines · Testing & Audits · Iceberg Materialization · Training to Serving
Concepts	Point-in-Time Joins · Virtual Environments · Glossary

Install from Source

For development or contributing:

git clone https://github.com/mta-tech/seeknal.git
cd seeknal
uv venv --python 3.11 && source .venv/bin/activate
uv pip install -e ".[all]"

Contributing

Contributions are welcome! See CONTRIBUTING.md for setup, code style, testing, and PR guidelines.

License

Seeknal is Apache 2.0 licensed.