Snowflake vs Amazon Redshift for Data Virtualization

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Snowflake and Amazon Redshift are leading cloud data warehouse platforms, each offering features to query data without moving or copying it (i.e., data virtualization).

This comparison focuses on how Snowflake and Redshift handle external data access, including supported virtualization features, external data sources and formats, ideal use cases, integration with BI tools and catalogs, performance, pricing, and cloud ecosystem support. The goal is to provide a structured, up-to-date overview of their capabilities in data virtualization.

What Makes These Data Warehouses Unique?

Redshift – AWS-Integrated Excellence

Amazon Redshift provides a fully managed data warehouse that gives you complete control within the AWS ecosystem. This makes it perfect for organizations already invested in Amazon’s cloud infrastructure.

Key benefits of Redshift:

• Seamless AWS service integration with other AWS services

• Massively parallel processing power for complex queries

• High-performance querying capabilities for large datasets

• Robust support for data security and access management

Redshift’s architecture leverages compute nodes organized in clusters, distributing your data across these nodes for parallel processing. This approach gives you granular control over your compute resources, enabling detailed optimization of query processing and workload management.

For companies deeply embedded in the AWS ecosystem, Redshift offers native integration with services like S3, Glue, and Kinesis Data Firehose, creating a cohesive data processing environment with unified security policies.

Snowflake – Multi-Cloud Flexibility

Snowflake offers a cloud-agnostic approach to data warehousing that provides remarkable flexibility. Snowflake users enjoy a platform that adapts to their existing infrastructure rather than forcing changes.

Key benefits of Snowflake:

• Separation of storage and compute resources

• Pay-as-you-go pricing model with on demand pricing

• Support for diverse data types including semi structured data

• Automatic scaling capabilities without manual intervention

The Snowflake data cloud represents a significant departure from traditional data warehousing solutions. Built as a true software as a service offering, Snowflake’s virtual data warehouse approach separates the storage layer from compute resources, allowing each to scale independently. This architecture enables near-instantaneous scaling—both up and down—in response to changing workload demands.

Available across multiple cloud providers including AWS, Microsoft Azure, and Google Cloud Platform, Snowflake offers true cloud flexibility that appeals to organizations seeking to avoid vendor lock-in.

Redshift vs Snowflake: What’s the Difference?

Key Strengths of Snowflake for Data Virtualization

• Seamless External Tables & Cloud Data Lake Integration: Snowflake external tables allow you to query data directly from S3, Azure Blob, or GCS without loading it into Snowflake. This provides a true logical virtualization layer over cloud object storage, enabling a “data lakehouse” architecture.

• Cross-Cloud Support: Unlike Redshift, which is AWS-only, Snowflake runs on AWS, Azure, and GCP, allowing you to virtualize data stored in different cloud providers within one platform.

• Federated Query Support: Snowflake has evolving capabilities to query external databases (including RDS and Aurora) using external functions and external tables, though it’s not as mature as Redshift’s federated query features yet.

• Data Sharing & Marketplace: Snowflake’s Secure Data Sharing enables live, governed sharing of virtualized data with other Snowflake accounts or third parties without copying data. The Data Marketplace lets organizations access third-party datasets as virtual tables.

• Automatic Performance Optimization: Snowflake handles clustering, caching, and query pushdown automatically, making virtualization queries faster and requiring less manual tuning.

Key Strengths of Amazon Redshift for Data Virtualization

• Redshift Spectrum: Spectrum allows querying vast amounts of data directly on S3 using standard SQL, without loading it into Redshift. It can query data stored in open formats like Parquet, ORC, and JSON. This is widely used for extending warehouse queries to data lakes.

• Federated Query: Redshift can directly query live data in RDS and Aurora, enabling operational analytics by virtualizing transactional databases in real time.

• Materialized Views and Result Caching: Redshift supports materialized views and automatic caching to accelerate queries, including those that join external and internal data.

• Deep AWS Ecosystem Integration: As part of AWS, Redshift integrates tightly with AWS Glue Data Catalog for metadata management, AWS IAM for security, and other AWS services (Lambda, S3, CloudTrail) facilitating governance and automation.

• Mature Federated Query Support: Redshift’s federated query capability to RDS/Aurora is mature and well-documented, enabling hybrid transactional-analytical processing scenarios.

Factory Thread – Real-Time Operational Virtualization for Industrial Environments

While Snowflake and Redshift offer strong virtualization features for cloud data lakes and external databases, Factory Thread brings a real-time, no-code virtualization layer purpose-built for manufacturing and operational data. It connects ERP, MES, SQL, flat files, and cloud APIs without replication—delivering live data as a service to analytics tools like Power BI, Tableau, or custom apps.

Key strengths of Factory Thread for data virtualization:

• True Real-Time Federation: Factory Thread creates virtualized views across on-prem and cloud sources (like Siemens Opcenter, Rockwell Plex, SAP) without moving data, enabling real-time monitoring and decision-making.

• No-Code Integration & Orchestration: Build and schedule data flows with a drag-and-drop interface or describe them in plain English using AI.

• On-Prem + Edge Deployments: Unlike Snowflake and Redshift, Factory Thread supports edge and local environments natively, making it ideal for plants, warehouses, and facilities.

• Secure, Compliant Architecture: Offers built-in encryption, role-based access, and audit trails suitable for regulated industries.

• Unified Access Layer: Publish OData/REST endpoints directly from virtualized flows—allowing BI tools and applications to consume live data without loading it into a warehouse.

Ideal for:

✔ Real-time dashboards and alerts
✔ Factory-floor analytics and supply chain visibility
✔ Integrating legacy systems with modern cloud tools
✔ Minimizing data latency in manufacturing decisions

Factory Thread isn’t just an alternative—it’s a specialized solution for organizations where time-to-decision is critical, infrastructure is hybrid, and operational data lives across many platforms. It complements (or replaces) traditional cloud warehouses by offering instant insight without storage duplication or lag.