Apache Flink (Daryl)

Overview

Apache Flink is a powerful open-source framework and distributed processing engine for performing stateful ingest and computations (transformations) on unbounded (streaming) and bounded (batch) data streams. It excels at low-latency, high-throughput stream processing, making it ideal for real-time analytics, event-driven applications, and continuous data pipelines. When used with CrateDB, Flink can ingest, transform, and enrich streaming data before persisting it into CrateDB’s distributed SQL engine. Together, they provide a scalable, end-to-end solution for both data streaming and analytical workloads.

Benefits of CrateDB + Apache Flink

• Real-time data ingestion – Stream data from Kafka, MQTT, or custom sources through Flink and insert or upsert directly into CrateDB for instant availability.

• Scalable stream processing – Flink’s distributed engine and CrateDB’s shared-nothing architecture scale horizontally to handle growing data volumes and concurrent analytics.

• Low-latency analytics – Flink processes events in milliseconds, while CrateDB’s columnar storage and distributed SQL queries provide sub-second response times.

• Unified batch and stream handling – Use the same Flink jobs to process both historical (bounded) and live (unbounded) data, storing results in CrateDB for consistent querying.

• Stateful transformations and enrichment – Apply windowed aggregations, joins, and enrichments in Flink before persisting processed results in CrateDB.

• Simplified integration – Both systems offer native SQL interfaces and JDBC compatibility, making it easy to connect existing tools and pipelines.

• Fault tolerance and reliability – Flink’s checkpointing and CrateDB’s data replication ensure resilience against node or network failures.

• Operational visibility – CrateDB can serve as both a sink for processed data and a query layer for monitoring Flink job metrics, results, and derived analytics.

• Flexible deployment options – Both run seamlessly on Kubernetes, Docker, or bare metal, supporting hybrid or cloud-native architectures.

Ingestion Options

Bounded/Batch

Unbounded/Stream

Job & Task Managers

How to Define Jobs

Flink SQL

Using JARs for custom Java code

Pipelines

Parallelism

Dashboard

Backpressure