Data Infrastructure & Migration Solution

We extract requirements through domain interviews, touchpoint mapping, and data flow tracing, then formalize them into logical and physical data models. We design consistency strategies (PKs, indexes, foreign keys, history preservation) for the operating environment on a project-by-project basis. Data modeling for enterprise workflows is where our company began, and the capability we've spent the longest refining.

• Domain Interviews & Touchpoint Mapping — Complex business analysis expertise
• Logical Data Modeling — Defining entities, relationships, and historicity
• Physical Data Modeling — Consistency, indexing, and partitioning strategies
• Data Dictionary (D/A), Specification & Migration Mapping documentation

We run large-scale migrations across heterogeneous data stores on our own ETL batch engine (core: DBridge). We express column mappings, filters, aggregations, and joins using meta-definition-based SQL, securing throughput via worker pools and logical partitioning. Progress, retries, and history are monitored via the operator console.

• 4 Relational DBs + Graph DB + FTS Search Engine — 6 data stores supported
• Multi-process and logical partitioning for high-volume throughput
• Meta SQL-based transforms — define column mapping, filtering, aggregation, and joins in SQL
• Stability — progress logging and idempotent retries

Explore Core Engine — DBridge →

In addition to structured RDBs (DW), we load data into FTS search engines, Graph DBs, and Vector DBs using the same ETL flow. With optional Korean morphological analysis and AI embeddings, we run unstructured text through the same pipeline as well. We integrate keyword, semantic, and relationship search over the same dataset.

• Synchronous loading of 4 indexes — RDB, FTS, GDB, Vector DB
• Unstructured text processing — Korean morphological analyzer & AI embedding (optional)
• Integrates keyword, semantic, and relationship search on identical data
• Index distribution strategy is designed separately for each project's data model

For batch rule definition, execution history, progress monitoring, and anomaly alerts, we build the operations console to order for each project. The core engine's meta tables and history tables are exposed via standard APIs, while screen layouts, permissions, and statistics are configured to match the deployment environment's requirements.

• Batch rule definition & execution history screens
• Statistics on progress, elapsed time, and failure reasons
• Permissions, audit logs, and anomaly alerts — configured based on deployment environment
• Operations consoles are custom-developed per SI project (no generalized standard UI)