A declarative, configuration-driven Snowpark framework with built-in geospatial capabilities. Focus on what needs to happen, not how everything is wired together.
Define pipelines using YAML instead of scattered Python files. Easy to read, review, and reason about with minimal code required.
Version-controlled schemas in YAML/JSON. Schema evolution is explicit, reviewable, and never hidden in code.
Keep SQL clean and readable with parameter substitution. Business logic stays close to data logic without string chaos.
Databases, schemas, and tables auto-created when needed. Remove repetitive setup work with safer deployments.
Run pipelines via a simple CLI interface designed for both local development and automation.
Configuration over code. Clear separation of logic, schema, and infrastructure concerns for maintainable pipelines.
Built-in Apache Sedona integration for spatial data processing. Work with geometries, perform spatial joins, and analyze location data natively.
# Define your pipeline in YAML - that's it!
pipeline:
name: "customer_analytics"
source: "northwind_api"
destination:
database: "analytics"
schema: "gold"
transformations:
- name: "aggregate_sales"
sql_file: "sql/customer_sales.sql"
output_table: "customer_sales_summary"
# No boilerplate. No hidden logic. Just clear, declarative configuration.New team members can understand and contribute to pipelines in hours, not weeks. Clear structure means less time untangling code.
Opinionated structure encourages best practices. Every pipeline follows the same patterns, making collaboration seamless.
Configuration-based pipelines are easier to review in pull requests and simpler to debug when issues arise.
Add new pipelines without reinventing the wheel. The framework handles infrastructure, you handle business logic.
Snowdeck seamlessly integrates Apache Sedona, bringing enterprise-grade geospatial capabilities to your Snowflake data pipelines.
Access 100+ spatial SQL functions for geometry creation, transformation, and analysis.
Perform efficient spatial joins on large datasets with optimized query execution.
Analyze point-in-polygon, distance calculations, and proximity queries at scale.
Framework handles Sedona installation and configuration automatically.
-- Find all stores within 5km of customer locations
SELECT s.store_name, c.customer_name,
SEDONA.ST_Distance(s.location, c.location) AS distance_km
FROM stores s
CROSS JOIN customers c
WHERE SEDONA.ST_DWithin(s.location, c.location, 5000)
ORDER BY distance_km;Join teams who are making data engineering predictable, maintainable, and dare we say... boring (in the best way).