Snowflake-Native by Design
Built 100% inside Snowflake, ℼ-Snow eliminates external dependencies and leverages the platform's full capabilities for security, performance, and governance.
Enterprise Data Engineering Capabilities
At ℼby3, our Data Engineering practice helps enterprises build scalable, reliable data platforms for analytics, AI, and GenAI. We cover the full data engineering lifecycle from ingestion and transformation to orchestration, validation, and modernization enabling governed, production-ready data foundations.
With a strong focus on cloud-native, performance-optimized architectures, we design resilient data pipelines that scale efficiently. Where Snowflake is the core platform, we specialize in Snowflake-native data engineering, running pipelines entirely within Snowflake to maximize performance, security and elasticity.
To accelerate this capability at scale, ℼby3 offers ℼ-Snow, our Snowflake-native data engineering accelerator suite.
ℼ-Snow
A Snowflake-native data engineering suite for ingestion, modernization, orchestration and validation
Why Snowflake-Native Engineering Matters
To truly harness Snowflake's power, your data engineering lifecycle must operate natively within it. ℼ-Snow is ℼby3's Snowflake-native accelerators automating ingestion, transformation, orchestration, and validation entirely inside Snowflake.
Built 100% within Snowflake's architecture, ℼ-Snow eliminates external tool dependencies and delivers automation-driven, high-performance data operations that scale with your enterprise needs.
Enterprise Challenges
Fragmented Tool Ecosystems
Create integration complexities, requiring constant maintenance across multiple platforms and vendors.
Fragmented Tool Ecosystems
Create integration complexities, requiring constant maintenance across multiple platforms and vendors.
External Dependencies
Introduce latency, security gaps and unnecessary infrastructure overhead that counteracts cloud efficiency.
External Dependencies
Introduce latency, security gaps and unnecessary infrastructure overhead that counteracts cloud efficiency.
Manual Workflows
Consume engineering hours on repetitive tasks, from data validation to pipeline scheduling, slowing time-to-insight.
Manual Workflows
Consume engineering hours on repetitive tasks, from data validation to pipeline scheduling, slowing time-to-insight.
Performance Limitations
Emerge when processing happens outside Snowflake, forcing data movement and degrading the platform's native optimization capabilities.
Performance Limitations
Emerge when processing happens outside Snowflake, forcing data movement and degrading the platform's native optimization capabilities.
Governance Blind Spots
Multiply as data flows through external systems, making compliance tracking and audit trails increasingly complex.
Governance Blind Spots
Multiply as data flows through external systems, making compliance tracking and audit trails increasingly complex.
ℼ-Snow Accelerator Suite
Our suite comprises four purpose-built accelerators that work seamlessly together or independently, addressing every stage of your data engineering lifecycle:
ℼ-Ingest
Metadata-Driven Data Ingestion
ℼ-Ingest automates data onboarding through intelligent metadata frameworks, eliminating custom scripts and manual mapping. Whether handling full or incremental loads, it delivers fast, scalable and compliant ingestion entirely within Snowflake.
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Turbo-ℼ
ETL-to-ELT Transformation Modernization
Turbo-ℼ accelerates platform modernization by converting legacy ETL pipelines into optimized, cloud-native SQL stored procedures with full pushdown execution. It transforms legacy ETL transformation logic into high-performance Snowflake operations without requiring complete rewrites.
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ℼ-Flow
Event-Driven Orchestration
ℼ-Flow brings intelligent orchestration directly into Snowflake, replacing external schedulers with native task management. It converts complex job dependencies into event-driven workflows that trigger automatically based on time, data arrival or upstream completion.
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ℼ-Recon
Post-Migration Validation
ℼ-Recon automates data reconciliation and validation after migrations or transformations, ensuring accuracy and completeness without manual verification. Using advanced statistical methods, it handles heterogeneous sources and complex data structures at scale.
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Why Choose ℼ-Snow?
Automation-Driven Operations
Reduce manual intervention and operational overhead with intelligent automation across ingestion, transformation, orchestration, and validation.
Performance Optimized
Leverage Snowflake's elasticity and pushdown execution for superior processing speed and cost efficiency at any scale.
Unified Ecosystem
Manage your entire data engineering lifecycle within a single, cohesive environment—no fragmented tools, no integration headaches.
ℼ-Snow Advantage
Getting started with ℼ-Snow
At ℼby3, we recognize that every enterprise's data engineering maturity and requirements differ. Our approach to ℼ-Snow implementation is consultative and tailored to your specific context.
Whether you're modernizing a legacy platform, scaling current Snowflake operations, or building greenfield data infrastructure, our team works with you to identify the right accelerators and deployment strategy.