Apache Flink
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. It provides a REST API for job management, cluster operations, metrics collection, and checkpoint management for real-time streaming and batch processing workloads.
APIs
Apache Flink REST API
The REST API provides programmatic access to monitor and control Flink jobs and clusters. It supports job submission, cluster management, metrics retrieval, checkpoint managemen...
Apache Flink Monitoring API
Monitoring REST API for accessing job metrics, checkpoints, and cluster statistics for Apache Flink deployments.
Capabilities
Apache Flink Job Management
Unified capability for managing and monitoring Apache Flink streaming and batch jobs — submitting, tracking, monitoring metrics, and managing the cluster. Designed for data engi...
Run with NaftikoFeatures
Single engine for both unbounded stream processing and bounded batch workloads with a unified API.
Rich stateful processing with managed state backends (RocksDB, heap), exactly-once guarantees, and state versioning.
End-to-end exactly-once processing guarantees with distributed snapshots and transactional sinks.
Native event-time support with watermarks for out-of-order event handling in streaming workloads.
Automatic fault-tolerance via checkpointing and manual savepoints for job migration and upgrades.
JobManager HA via ZooKeeper or Kubernetes for zero-downtime cluster operations.
Horizontally scalable TaskManagers with fine-grained resource management and dynamic slot allocation.
Comprehensive REST API for job submission, monitoring, metrics collection, and cluster administration.
Declarative SQL and Table API for streaming analytics with connector ecosystem support.
Use Cases
Process and analyze event streams in real time for dashboards, alerts, and operational intelligence.
Build scalable ETL pipelines for data lake ingestion, transformation, and enrichment.
Detect fraudulent transactions in real time using stateful pattern matching over event streams.
Process high-volume IoT device telemetry with stateful aggregations and time-window computations.
Serve ML model predictions at scale with streaming feature computation and online inference.
Integrations
Kafka source and sink connectors for high-throughput event streaming ingestion and output.
HDFS integration for batch data reading and writing in distributed storage.
Hive catalog integration and batch SQL queries over Hive tables.
Native Kubernetes deployment with FlinkDeployment CRD and the Flink Kubernetes Operator.
Iceberg table format integration for lakehouse workloads with ACID guarantees.
Elasticsearch sink connector for real-time search index updates from Flink jobs.
Kinesis source and sink connectors for AWS-native streaming pipelines.