As organizations accelerate their cloud migration strategies, Amazon Web Services (AWS) has become a leading platform for handling data at scale. However, the challenge remains: how can businesses ensure that data is not only stored securely but also consistently defined and easily accessible across various teams and tools? This is where Cube’s universal semantic layer comes in, complementing AWS’s powerful infrastructure to offer a unified, optimized, and governed analytics solution.

AWS offers a comprehensive cloud ecosystem with services like Amazon S3, Redshift, and Athena that help businesses store, process, and analyze massive amounts of structured and unstructured data. However, as datasets grow, ensuring that insights are easily accessible, consistent, and scalable across different teams and departments becomes a challenge.

Cube’s universal semantic layer addresses this by creating a centralized data access point that abstracts the complexity of raw data and makes it easy for business users to query data from AI, BI, spreadsheets, and embedded analytics with consistent, trusted metrics.

A Perfect Pair: Combining the Power of AWS with Cube’s Universal Semantic Layer

Unified Data Storage Meets Simplified Data Access

AWS provides unmatched data storage capabilities via services like Amazon S3 for data lakes, Redshift for data warehousing, and Athena for ad-hoc querying. However, accessing this data can be challenging, especially when multiple teams use different tools and frameworks to analyze the same data.

Cube simplifies this process by creating a universal semantic layer that centralizes the definitions of metrics, KPIs, and business rules. By integrating Cube with AWS, companies can provide a unified interface for all business users, ensuring everyone accesses the same trusted definitions and insights, regardless of the front-end tool they prefer.

Seamless Integration of Data Engineering and Business Teams

AWS allows data engineering teams to build and maintain robust data pipelines, ensuring data is ingested, cleaned, and stored efficiently. But when it comes to business users consuming this data for decision-making, there’s often a gap in how easily the data can be interpreted and accessed. Cube bridges this gap by providing a user-friendly semantic layer on top of AWS’s data infrastructure. While AWS powers data storage and compute, Cube abstracts this raw data into meaningful business definitions, allowing non-technical teams to self-serve their analytics needs without needing to understand the underlying complexities of the data.

Real-Time Insights and Query Acceleration at Scale

AWS supports real-time data processing and analysis with services like Kinesis and Redshift Spectrum, enabling businesses to act on streaming data and large-scale analytics. But real-time data is only valuable if decision-makers can quickly make sense of it.

Cube’s pre-aggregation and caching features ensure that real-time data from AWS can be quickly queried and visualized, even at scale. Cube accelerates complex queries, reducing the time it takes to surface insights from AWS-based data, and ensuring that business leaders can act on the most up-to-date information.

Faster Time to Insight with Optimized Costs

AWS’s ability to elastically scale resources ensures that organizations can handle growing datasets and workloads without compromising on performance. However, as data volumes grow, query costs and infrastructure complexity can increase.

Cube optimizes the AWS stack by offloading query processing through caching and pre-aggregating commonly used metrics. This reduces the load on AWS’s underlying services, improving performance while lowering costs, especially for frequently accessed or large datasets. With Cube, teams can enjoy faster time to insight while keeping their cloud costs under control.

Governance, Security, and Compliance at Scale

AWS provides a strong foundation for data governance, security, and compliance through features like IAM (Identity and Access Management), encryption, and detailed auditing tools. However, as data is accessed by multiple business units and tools, ensuring consistent data governance can be challenging.

Cube’s universal semantic layer enforces consistent governance policies across all downstream analytics tools, ensuring that users only access the data they’re authorized to see. Whether data is being consumed through modern BI platforms, such as Tableau or Power BI, or spreadsheets, such as Microsoft Excel or Google Sheets, Cube ensures that governance is applied uniformly, minimizing the risk of data misuse while maintaining compliance with industry standards like GDPR, HIPAA, or SOC 2.

Conclusion

By integrating Cube’s universal semantic layer with AWS’s cloud infrastructure, organizations can unlock a unified, scalable, and governed data experience. AWS provides the elastic compute and storage backbone for storing and processing massive datasets, while Cube ensures that this data is accessible, trusted, and consistent across the entire organization.

With this powerful combination, organizations can:

  • Democratize access to insights, empowering all teams to access the data they need without relying on engineering bottlenecks.
  • Reduce time to insight through Cube’s query acceleration, caching, and pre-aggregation capabilities.
  • Ensure governance, security, and compliance across their entire data pipeline, from raw storage in AWS to self-service analytics tools.

Whether you’re building real-time dashboards, performing complex financial analysis, or generating reports for regulatory compliance, Cube and AWS together provide the foundation for scalable, secure, and reliable data analytics. Cube is available on the AWS Marketplace. Contact sales to learn more about how Cube and AWS work together.