VirtualZ’s Mainframe Data Access for Data Lakes Solution
In today’s data-driven world, businesses must harness their mainframe data effectively to remain competitive.
VirtualZ™’s Mainframe Data Access for Data Lakes solution simplifies this process, allowing organizations to seamlessly extract and load mainframe data into popular cloud-based data lakes like AWS S3, Azure Data Lake, and Google BigQuery.
Built on the innovative PropelZ™ platform, this solution is designed to support advanced analytics, empowering businesses to store, access, and process mainframe data in modern cloud environments.
Key Features
- Multi-Cloud Support: PropelZ ensures compatibility with leading cloud data warehouse and data lake solutions, including AWS S3, Azure Data Lake, Hadoop, Teradata, and Google BigQuery. This flexibility allows organizations to choose the platform that best meets their needs.
- Automated Data Pipeline: With PropelZ, the extraction, transformation, and loading (ETL) of mainframe data into cloud data lakes is automated. This eliminates manual processes and ensures that data is always ready for analysis.
- Secure Data Transfer: Data integrity is paramount. PropelZ guarantees secure, encrypted transfers of mainframe data to cloud-based data lakes, protecting sensitive information throughout the entire process.
- Data Optimization: The solution optimizes mainframe data for efficient storage in cloud environments, improving access speeds and minimizing space usage. This means faster analytics and better performance.
- Scalable Storage: By leveraging the inherent scalability of cloud storage, VirtualZ’s solution allows organizations to store large volumes of mainframe data without incurring significant infrastructure overhead. This is crucial for businesses experiencing growth or seasonal spikes in data needs.
Benefits
- Cost Savings: Transitioning mainframe data to scalable cloud-based data lakes significantly reduces on-premises storage expenses. Organizations can allocate their resources more effectively.
- Simplified Data Management: Integrating mainframe data with cloud-native data lakes streamlines data management. This enhances ease of access and governance, allowing businesses to focus on deriving insights rather than managing data.
- Accelerated Insights: Real-time access to mainframe data from cloud platforms enables faster and more efficient analytics and reporting. Organizations can make informed decisions swiftly, staying ahead of the competition.
- Increased Flexibility: With multi-cloud compatibility, businesses can select the best platform for their operational and analytical needs, ensuring they are not locked into a single vendor.
- Enhanced Data Availability: Ensuring that mainframe data is readily available for cloud-native applications improves operational efficiency, driving better business outcomes across the board.
Conclusion
VirtualZ’s Mainframe Data Access for Data Lakes solution, powered by PropelZ, represents a significant leap forward in modernizing data architecture.
By integrating mainframe data into cloud-native data lakes such as AWS S3, Azure Data Lake, and Google BigQuery, organizations can create a scalable, cost-effective strategy for storing, managing, and analyzing vast amounts of data in real-time.
This approach not only accelerates digital transformation but also enhances overall operational efficiency, allowing businesses to unlock the full potential of their mainframe data.
