USE CASE

Real-Time Lakehouse on Confluent Cloud

NorthRiver Bank — Fraud Detection & Customer 360

NorthRiver Bank needed to spot fraud in seconds while giving teams fresh, trustworthy data for analytics and ML. Batch ETL and nightly refreshes left analysts and models a day behind. The bank standardized on Confluent Cloud to stream operational events from core systems, enrich them in flight, and publish analytics-ready tables—without building and maintaining fragile pipelines.

NorthRiver Bank struggled with several pressing data challenges:

  1. Slow fraud detection — Batch ETL meant fraudulent activity could go undetected for hours, leaving the bank exposed to significant risk.

  2. Stale analytics and ML data — Nightly refreshes left customer insights and models a full day behind, reducing their accuracy and effectiveness.

  3. Siloed data products — Different teams built separate pipelines for operations, analytics, and machine learning, creating inconsistencies across use cases.

  4. Fragile and costly pipelines — Hand-coded ETL jobs were error-prone, hard to scale, and expensive to maintain under regulatory pressure.

  5. Compliance and governance gaps — Without standardized schema enforcement and lineage, data quality and auditability were inconsistent.

Proposed Architecture 

  1. Sources → Confluent Cloud | Kafka

    • Core banking apps, mobile/online banking, ATMs, card networks, and security logs stream events into Confluent Cloud.

    • Kafka Connect (with Debezium CDC) captures database changes; Confluent Schema Registry enforces schemas; Stream Governance provides lineage, quality, and policy controls.

  2. Streaming transforms → Confluent Cloud for Apache Flink

    • Stateful Flink jobs consume Kafka topics from Confluent Cloud to enrich transactions (device fingerprint, geo/IP, customer risk tier), run anomaly rules/CEP, and compute rolling KPIs.

    • Results are written back to Confluent Cloud as curated topics (e.g., fraud_alerts, customer_insights) with exactly-once guarantees.

  3. Publish to lakehouse → Confluent Tableflow

    • Confluent Tableflow continuously materializes curated Kafka topics into open table formats (Apache Iceberg or Delta Lake) in cloud object storage (e.g., Amazon S3).

    • Tableflow automates file sizing, partitioning, compaction, and schema evolution, and registers tables in the bank’s Glue (or other) catalog so engines can query them immediately.

  4. Analytics & AI

    • Snowflake (Iceberg), Databricks, Trino/Starburst, and Athena query the same fresh, governed tables for fraud operations, customer 360 dashboards, and model training—no custom ETL.

Why Confluent?

  1. Managed, elastic Kafka with built-in Schema Registry, Kafka Connect, and Stream Governance simplifies operations and compliance.

  2. First-class Flink on Confluent Cloud delivers low-latency enrichment and exactly-once processing without standing up clusters.

  3. Tableflow turns Kafka topics into query-ready Iceberg/Delta tables—zero bespoke batch jobs.

  4. Open & portable: data lands in open formats, queryable by your favorite engines and BI tools.

Outcomes for NorthRiver Bank

  1. Fraud detection latency reduced from hours to seconds (stream scoring + alerting).

  2. One data product for ops, analytics, and ML—stream and historical views stay in sync.

  3. Lower TCO & risk: fewer moving parts, governed schemas, automated table optimization.

  4. Faster delivery: new data products published via Tableflow in minutes, not weeks.