Solution overview
Traditionally, real-time transactions and data analysis are handled by separate database systems. They use ETL or streaming platforms to act as a bridge from transaction databases to data warehouses. This means customers have to bring in multiple products, and the data can’t keep up with timing needs. On top of that, it makes maintenance a lot more complicated. HexaDB-H (Hybrid Edition) uses a new HTAP computing and storage structure. It lets one cluster with one set of data handle both real-time transactions and real-time analysis—no delays between them at all. Plus, the hybrid storage engine and load management tools make sure computing and storage resources get allocated efficiently to business requests with different priorities.
Challenges
The IT structure is complicated, and app development is slow
When developing apps based on databases and middleware with different architectures, the interfaces don’t match and the data structures aren’t compatible. This makes development and maintenance really costly
Having lots of system components drives up costs and risks
A single business ends up using tons of component instances. Each one needs its own monitoring and fault management, which makes management inefficient and risks hard to control
Increasing latency between database and warehouse
From the latest transaction data to data analysis, there's a T+1 delay. And streaming processing has its own scenario limitations. This issue with data timeliness is holding back business development
Solution Advantages
Simplified architecture
Deploying a single HexaDB cluster handles both transactions and analysis, resulting in a unified, streamlined IT infrastructure that significantly cuts operational costs.
Mixed workload processing
Real-time transactions and online analytics share the same dataset, eliminating tedious data extraction, transformation, and loading processes—with zero data redundancy.
Load management
Real-time transactions and online analytics leverage pooled resources, where business tasks of varying priorities get corresponding resource access privileges, ensuring they don’t interfere with one another.
Ultra-low elapsed time
Data from real-time transactions can instantly be combined with massive volumes of historical data for real-time analytics. The HTAP architecture drastically boosts the timeliness of data analysis.
Stable performance
Thanks to its well-engineered HTAP computing and storage architecture, whether processing transactions or running analytics, it delivers performance on par with the original segregated architecture.
Customer Cases
