In today’s digital world, data is one of the most valuable assets for businesses. To harness its full potential, organizations are increasingly turning to modern data architectures such as data lakes and data warehouses. These platforms allow businesses to store, manage, and analyze vast amounts of structured and unstructured data. However, integrating legacy systems like mainframes with these modern platforms can be a significant challenge, especially when it comes to ensuring real-time insights and maintaining data quality.
Data Lakes vs. Data Warehouses: Understanding the Differences
A data lake is a centralized repository that stores raw, unprocessed data in its native format. It allows businesses to store structured, semi-structured, and unstructured data at scale, supporting advanced analytics and AI-driven initiatives. On the other hand, a data warehouse is a structured, schema-based system designed for efficient querying and reporting. It organizes data into predefined tables and columns, making it ideal for business intelligence.
As organizations continue to modernize their data strategies, the lines between data lakes and warehouses are blurring. New hybrid solutions, or “lakehouses,” combine the scalability of data lakes with the performance and structure of data warehouses, offering organizations the best of both worlds.
Challenges in Modernizing Data Strategies
Despite the benefits of data lakes and warehouses, businesses face several challenges when modernizing their data infrastructure:
- Data Silos: Legacy systems, such as mainframes, often operate in isolation, preventing integration with cloud-based platforms.
- Scalability: Handling large datasets efficiently requires platforms that can scale horizontally without sacrificing performance.
- Data Quality: Ensuring data consistency and accuracy across diverse sources remains a challenge.
- Talent Gaps: Implementing and managing modern data platforms requires specialized skills, creating a shortage of qualified professionals.
How PropelZ™ Simplifies Data Integration
VirtualZ Computing’s PropelZ platform addresses many of these challenges by providing an easy and secure way to integrate mainframe data with modern data lakes and warehouses. PropelZ automates the ETL (extract, transform, load) process, making mainframe data readily available for analysis in cloud-native environments. It also ensures secure, encrypted transfer of sensitive data, protecting against compliance risks while optimizing cloud storage for cost efficiency.
Key features of PropelZ include:
- Multi-Cloud Support: PropelZ integrates seamlessly with leading cloud platforms such as AWS S3, Azure Data Lake, Google BigQuery, Snowflake, and Databricks, giving businesses the flexibility to choose the platform that best suits their needs.
- Automated Data Pipelines: By automating the ETL process, PropelZ eliminates manual tasks, improving data availability and reducing errors.
- Scalable Storage: PropelZ leverages the scalability of cloud platforms, enabling organizations to store large volumes of mainframe data without significant infrastructure overhead.
Unlock Real-Time Insights and Drive Digital Transformation
With PropelZ, businesses can access mainframe data in real time, empowering faster, more informed decision-making. Whether it’s integrating mainframe data with Snowflake, Databricks, or Power BI, PropelZ ensures that your data is readily available for powerful analytics, machine learning, and reporting.
Learn More
To dive deeper into how PropelZ can help modernize your data architecture, download our comprehensive white paper, which explores these topics in greater detail. And take a look at our Mainframe Data Access for Data Lakes solution.