Maximize the value of your Snowflake investment
Optimize performance, reduce costs and create AI-ready data ecosystems with actionable models, governance frameworks, and automated tools tailored for Snowflake.
Avoid costly Snowflake migration failures
Rushing Oracle, Postgres or other data into Snowflake, Redshift or Databricks without proper planning leads to performance issues, high costs and compliance risks.
This brief shows how to build a scalable, efficient data foundation.
- Optimize upfront – Reverse-engineer Oracle models and create optimized Snowflake/Redshift schemas to prevent costly mistakes.
- Automate and accelerate – Generate schema scripts to save days of effort, reduce errors and ensure consistency.
- Seamless data movement – Move data in real-time with error correction, recovery and minimal downtime using Kafka-based integration.
- Boost performance and cut costs – Tune schemas, identify inefficient queries and use root cause analysis to reduce compute spend.
- Monitor and govern data flows – Track lineage, security and data consistency across cloud and hybrid environments.
Scalable data architectures and cross-platform governance
Maximize value from your Snowflake investment with tools designed for performance, governance and AI readiness.
Frequently Asked Questions
DataVault 2.0 provides a standardized approach to organizing and integrating data across platforms, enabling consistent governance, traceability and compliance. It simplifies managing lineage, ownership and metadata, critical for enterprise-wide data oversight.
Yes, Snowflake enables cross-platform data management by integrating with legacy systems and other cloud platforms. When combined with frameworks like DataVault 2.0, it ensures seamless collaboration and compliance across the data estate.
Real-time performance monitoring tools like Foglight provide actionable insights to optimize compute usage, reduce costs and enhance query performance in Snowflake, helping you maximize ROI.
Snowflake’s scalability and support for clean, structured data make it an ideal hub for AI applications. Tools like erwin Data Modeler and DataVault 2.0 ensure Snowflake’s data is consistent, AI-ready and traceable for compliance.
Many implementations lack proper architectural planning, resulting in inefficiencies and underutilized resources. Strong data modeling practices and cross-platform governance help organizations optimize Snowflake’s scalability, performance and overall value.
Ready to maximize Snowflake?
Discover how to cut costs, enhance performance and build AI-ready data ecosystems based on the Snowflake platform.