Beyond Fragmented Data Flows: How GBase 8c Makes Enterprise Architecture "Lighter"

Consider a typical business scenario: a customer transfer must be recorded in the transaction system, risk control must analyze massive transaction streams in real time, regulatory reporting must extract data from multiple systems to build reports, and the new AI customer service deployment requires a separate LLM retrieval database. The same business data sits scattered across several systems, synchronized back and forth — one small discrepancy and the books no longer reconcile. When something breaks at 2 a.m., operations teams must troubleshoot system by system. GBase 8c's multi-model integration brings all of these scenarios into a single database: data stored once, queries routed along different paths — no dual writes, no synchronization.

Growing Data Types, Growing Architectural Complexity

In the early stages of enterprise IT, a single MySQL instance often sufficed. As data volumes grew, an analytics database was added. Then, to support full-text search, Elasticsearch was deployed. Now, with the rise of large model applications, a vector database is needed too.

The consequence: data written to the transactional database must be synchronized to the analytics database; the search engine must import data from the business database to build indexes; and the vector database requires a fresh round of cleansing to store embeddings. The cost of integrating these systems often exceeds the cost of building them.

Is it possible for a single database to host all of these data forms?

Six Storage Modalities, Unified in One Engine

GBase 8c's multi-model integration centers on six storage modalities unified within a single engine:

Row-store — for transactional workloads

High-concurrency writes and transactional consistency for traditional OLTP business operations.

Column-store — for analytical workloads

Large-scale scans and aggregation for reporting and data warehouse queries.

Row-column hybrid — for HTAP workloads

A single copy of data supports both transactions and analytics — no need to deploy two separate databases.

Vector storage — for semantic retrieval

Supports vectors up to 64,000 dimensions with built-in IVFFlat and HNSW indexes. LLM recommendations and similarity search run directly inside the database.

Time-series storage — for monitoring metrics

Automatic partitioning, data compression, and continuous aggregation for device monitoring and IoT data scenarios.

GIS storage — for geospatial information

Geometry types and GiST indexes for location queries and spatial analysis — no separate GIS system required.

These capabilities once required six separate systems. Now they are unified in one database.

No Dual Writes, No Synchronization

The Real Value of Integration

The most immediate benefit of multi-model integration is not the breadth of features — it is the elimination of data synchronization.

In a traditional architecture, data written to MySQL by the transaction system must be replicated to the analytics database via CDC, then exported to the search engine, then embedded and loaded into the vector database. If any link in that chain fails, the data no longer aligns.

GBase 8c's approach: no dual writes, no synchronization. Transactions go to row-store, analytics to column-store, vector retrieval to vector indexes, and full-text search to tokenized indexes — the same data, with different queries following different physical paths, without moving data between systems.

Take full-text search. Previously, this required a separate Elasticsearch deployment plus a data synchronization pipeline to import business data. GBase 8c's full-text search supports Chinese word segmentation (zhparser), n-gram tokenization, and trigram fuzzy matching (pg_trgm), along with relevance scoring and multi-factor ranking. What Elasticsearch once provided can now be queried directly via SQL in GBase 8c.

Take vector retrieval. Previously, this required a separate vector database: export business data, cleanse it, compute embeddings, and write them in. GBase 8c's vector engine supports a pgvector-compatible interface — simply add a vector-type column to a business table and build an HNSW index. Scalar filtering and vector retrieval then complete within the same transaction.

In real-world deployments, the most visible change from multi-model integration is fewer systems and a shorter operations chain.

Three Deployment Forms for Different Business Scales

Beyond storage modalities, GBase 8c's "multi-form" design is also reflected in deployment options:

Primary-standby architecture — for small and mid-sized workloads

Two machines with primary-standby failover: simple deployment, low operational cost.

Distributed architecture — for large-scale core workloads

Data sharding and elastic scaling for financial core and government core systems.

Storage-compute separation — for cloud-native scenarios

Independent scaling of compute and storage: rapid expansion during business peaks, scale-down during idle periods to save costs.

The same database scales from an initial two machines to dozens of nodes, and then to an elastic cloud cluster — with no product replacement and no data migration.

To be objective: multi-model integration does not mean one database must cover every conceivable scenario. But for the vast majority of enterprises, day-to-day workloads — transactions, analytics, search, vectors, time-series, and GIS — are well served by GBase 8c. Fewer systems mean fewer data synchronization steps, and the integration cost between teams naturally comes down.

As business grows, data accumulates, and systems multiply, it may be time to rethink: can we deploy fewer systems, remove data synchronization steps, and reduce operations headcount? GBase 8c's answer: one database is enough.