Real-time Analytics
🍃 MongoDB Real-time Analytics
Native Change Streams: MongoDB's built-in change detection enables sub-second analytics by streaming data changes directly to your analytics pipeline. No external infrastructure required for real-time processing.
Implementation: Use Change Streams to trigger instant analytics on data modifications, combined with the Aggregation Framework for complex real-time computations without ETL overhead.
Implementation: Use Change Streams to trigger instant analytics on data modifications, combined with the Aggregation Framework for complex real-time computations without ETL overhead.
- Built-in Change Streams for instant notifications
- Aggregation Framework for complex analytics
- Sub-second latency for real-time dashboards
- Single platform for OLTP and OLAP workloads
- Dynamic schema handles evolving analytics needs
- No external ETL or streaming infrastructure
Setup Complexity:
Low
🐘 PostgreSQL Real-time Analytics
External Streaming Required: PostgreSQL requires external tools like Kafka or Debezium for change detection and streaming. Analytics workloads often need separate OLAP systems like Clickhouse or external data warehouses.
Implementation: Set up logical replication, configure external streaming infrastructure, design ETL processes, and manage separate analytics databases for complex real-time workloads.
Implementation: Set up logical replication, configure external streaming infrastructure, design ETL processes, and manage separate analytics databases for complex real-time workloads.
- Logical replication for change capture
- External tools required (Kafka, Debezium)
- Separate OLAP systems for complex analytics
- ETL processes increase latency
- Schema changes require pipeline updates
- Multiple systems to maintain and monitor
Setup Complexity:
High
