Every industry has a database
that won’t sit still.
Marketplaces, multiplayer games, fleets of trucks, banks, hospitals, telcos, power grids, etc all depend on changing data. These scenarios show how Remac can fit common deployment patterns.
These are modeled scenarios, not customer deployments, testimonials, or measured customer results.
A 40-million-SKU catalog that's finally consistent everywhere.
One source of truth feeds the warehouse, search index, and event stream without a nightly ETL or 15-minute re-index cycle.
A nightly job loaded the warehouse and a cron re-indexed Elasticsearch every 15 minutes. Buyers searched for items that existed but weren't indexed yet, and merch dashboards trailed a day behind.
One Remac data path reads the catalog's replication slot and fans selected changes out to three sinks: the warehouse, search index, and a Kafka topic for downstream services.
The same committed change can update search, analytics, and downstream services through one declared processing path.
Current leaderboards and convergent game state across regions.
CDC sends selected player changes to Redis and replicates game tables to PostgreSQL and MySQL nodes across regions.
Leaderboard data lived in Redis with TTL-based expiry. A short TTL caused miss storms on hot keys, while a long TTL served stale standings. Three regions also used a mix of PostgreSQL and MySQL replicas that required separate maintenance.
Remac sends selected row changes to Redis and replicates selected tables to PostgreSQL and MySQL nodes across regions through one configured path.
Each required sink can advance independently while its selected row state converges in source commit order.
Fleet telemetry in the lake, and a recovery story that holds up.
One Remac data path supports continuous data-lake ingestion through logical processing and point-in-time recovery through the physical path.
Custom scripts and Airflow DAGs landed vehicle telemetry in S3. Nightly backups risked a full day of loss. Services polled each other for the latest vehicle state.
Remac streams CDC to S3 as JSONL and to Kafka for downstream services. It also captures the required WAL for point-in-time recovery.
Logical and physical processing can share one operational system while they keep separate recovery and delivery boundaries.
More patterns across data-intensive industries.
These patterns fit systems that need selected committed changes moved, retained, replicated, or replayed.
Stream selected committed transaction changes to the warehouse so fraud systems can work with live data.
Capture selected committed changes below the application layer, with available source and transaction context.
Impressions and clicks to Kafka in real time, not hourly batches.
One source of learner state, fanned out to every service in order.
High-volume CDR and session events streamed straight to the lake.
Deliver selected meter changes continuously without a separate Airflow ingestion path.
Preserve selected committed changes to public records with available operation, position, time, and transaction context.
Converge selected room, rate, and availability state across search and sales channels.
Send one selected change to required services and warehouses under one declared delivery policy.
Feature store updates as soon as a watch or like lands.
Edge and plant-floor changes to the cloud, in order, with retries.
Converge selected stock state across stores, the warehouse, and online systems.
Illustrative patterns only. Actual outcomes depend on the source, workload, sink, configuration, and declared processing policy.
One processor. Six transaction log functions.
Move committed data through one controlled path, with explicit ordering, delivery, and recovery boundaries.