RisingWave

Step 1: Ingest transaction data from Kafka.

Step 2: Define materialized views to calculate real-time risk scores based on transaction patterns.

Step 3: Set up alerts to trigger when risk scores exceed a predefined threshold.

Step 4: Integrate with a notification service to send alerts to fraud analysts.

Step 1: Ingest event data from various sources.

Step 2: Define materialized views to track key metrics and identify anomalies.

Step 3: Configure alerts based on predefined thresholds.

Step 4: Integrate with notification services to send alerts.

Step 1: Ingest data from IoT devices using MQTT or Kafka.

Step 2: Define materialized views to aggregate telemetry data and identify anomalies.

Step 3: Integrate with monitoring systems to visualize real-time metrics.

Step 4: Feed insights into AI systems for predictive maintenance.

Step 1: Ingest data from multiple sources, such as Kafka and Postgres.

Step 2: Define materialized views to join and enrich the data using SQL.

Step 3: Handle schema evolution and out-of-order events using RisingWave's built-in features.

Step 4: Serve the enriched data to downstream applications.

Step 1: Configure RisingWave to ingest data from Kafka or Postgres.

Step 2: Define materialized views to transform and prepare the data for Iceberg.

Step 3: Configure RisingWave to continuously ingest data into Iceberg tables.

Step 4: Utilize Iceberg's features for compaction, schema management, and incremental ingestion.