---
title: "How Apache Flink Processes Streaming Data"
description: "Apache Flink is a distributed stream-processing framework that ingests continuous data, applies transformations in parallel, and outputs results with fault tolerance and low latency."
---

# How Apache Flink Processes Streaming Data

**Type:** [How it works](https://yomu.fyi/visual-explainers/category/how-it-works.md)

![Diagram showing Apache Flink's pipeline: data sources feed into a JobManager, which coordinates TaskManagers running parallel operators, with state checkpoints written to storage and results flowing to sinks.](https://yomufyi.s3.us-east-1.amazonaws.com/visual-explainers/756c45b1-bf6f-4f18-b600-ad78ab9edbbe/image-b85e52fb-437c-4a09-a586-e9ef2ae3b34a.webp)

> Apache Flink is a distributed stream-processing framework that ingests continuous data, applies transformations in parallel, and outputs results with fault tolerance and low latency.

## Explanation

Apache Flink processes unbounded streams of data in real time by distributing computation across a cluster of machines. Data flows from sources like Kafka or databases through a directed acyclic graph of operators—filters, aggregations, joins, and sinks—each running in parallel on task slots. Flink maintains keyed state for windowed aggregations and handles failures by checkpointing state to durable storage, allowing exact-once semantics. This architecture enables use cases from real-time personalization and anomaly detection to CDC pipelines and metrics aggregation, making it a practical choice when batch processing is too slow and consistency matters.

**Tags:** [Apache Flink](https://yomu.fyi/topic/apache-flink.md), [Architecture](https://yomu.fyi/topic/architecture.md), [Data Pipelines](https://yomu.fyi/topic/data-pipelines.md), [Distributed Systems](https://yomu.fyi/topic/distributed-systems.md), [Streaming](https://yomu.fyi/topic/streaming.md)

## Related reading

### [Real-Time Intelligence with IBM Time Series Models on Confluent](https://yomu.fyi/post/real-time-intelligence-with-ibm-time-series-models-on-confluent.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Nicholas Fuller, Sean Falconer, Ayhan Sebin, Bilge Zeren Aksu, Mountu Jinwala, Pankaj Dayama, Roman Vaculin, Siddharth Bedekar, Julian Payne
- Published: Sep 2, 2026
- Type: Announcement

IBM and Confluent are making IBM Granite Time Series foundation models available in Early Access on Confluent Cloud, bringing forecasting and other time-series capabilities into streaming workflows. The announcement addresses the cost and delay of bespoke models by offering models that generalize across previously unseen series and can be used for forecasting, anomaly detection, similarity search, classification, gap-filling and optimization. Native inference runs directly in Apache Flink through Confluent’s AI\_FORECAST and AI\_DETECT\_ANOMALIES functions, with Flink maintaining keyed, fault-tolerant state for each series and results written to Kafka topics. Confluent manages serving, scaling and runtime operations while retaining schemas, lineage, access controls and replayable data within the platform. Early Access starts on Confluent Cloud on AWS at no charge, with Confluent Platform planned next for on-premises and hybrid environments.


### [Rerouting the Stream: How Lyft Moved to the Apache Flink Operator](https://yomu.fyi/post/rerouting-the-stream-how-lyft-moved-to-the-apache-flink-operator.md)
- Company: [Lyft](https://yomu.fyi/company/lyft.md)
- Author: Maheep Myneni
- Published: Aug 31, 2026
- Type: Problem & solution

Lyft migrated its fleet of hundreds of critical streaming jobs from an internally developed Flink Kubernetes operator to the open-source Apache Flink Kubernetes operator. The legacy system imposed maintenance burden, feature gaps, outdated dependencies, fragile savepoint handling, and a rigid memory model that complicated varied workloads, including Apache Beam applications. Lyft used an incremental migration designed to preserve deployment patterns and team workflows while addressing CRD translation, state-machine behavior, and operational compatibility. The new platform added explicit last-state upgrades, autoscaling, automatic memory tuning, and safer upgrade mechanisms, though autotuning required trade-offs with in-place autoscaling and node capacity planning. After the multi-quarter transition, maintenance burden was minimized, the autoscaler right-sized a fleet previously overprovisioned by a few million dollars annually, and Lyft could contribute fixes upstream instead of maintaining its own operator.


### [The Hugo evolution: Engineering Grab's unified, one-click data ingestion platform with Apache Flink](https://yomu.fyi/post/the-hugo-evolution-engineering-grab-s-unified-one-click-data-ingestion.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Shuguang Xiang
- Published: May 22, 2026
- Type: Problem & solution

Hugo evolved from a batch-oriented Spark platform into Grab’s unified, self-service data ingestion system for near real-time pipelines. The earlier expansion across Kafka Connect, Sprinkler, and Hugo fragmented onboarding, forcing users to translate configurations and manage manual schema changes across systems. A custom automation layer and Apache Flink now orchestrate one-click MySQL CDC and Kafka ingestion, retiring Kafka Connect and Sprinkler for the data lake. For CDC, Flink reads the binlog directly, uses automated schema detection and dynamic validation, and writes to queryable Hive tables through Spark compaction; for Kafka, it fetches Protobuf schemas from Confluent Schema Registry at runtime and resumes from the latest checkpoint after restart. Onboarding now takes approximately three minutes for MySQL CDC and six for Kafka, although schema updates still require manual restarts; new pipeline adoption in the last year also exceeded the previous five years combined.


### [Enhancing Flink deployment with shadow testing](https://yomu.fyi/post/enhancing-flink-deployment-with-shadow-testing.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Tee Long Lang
- Published: May 7, 2026
- Type: Problem & solution

Grab’s Coban data streaming team addresses production-only Apache Flink deployment failures, which can leave an application unavailable for about 10 minutes while the system observes it and rolls it back. Its Shadow Testing stage runs a new version in parallel with the current Main version, replaying production data without affecting Main outputs and allowing behavior and regressions to be assessed before promotion. The Shadow deployment uses a shadow Kubernetes namespace, a shadow-prefixed application name, isShadow=true, a distinct Job ID, isolated sinks, and connector-specific separation such as suffixed Kafka consumer groups and shifted Debezium Server ID ranges. By default, Shadow runs for one hour while its job-manager status is monitored; users then continue to Main deployment, which receives a 10-minute health check and automatic rollback if unhealthy. The framework isolates Kafka and S3 outputs, prefixes metrics and logs for observability, and plans more connector support.


### [The complete stream processing journey on FlinkSQL](https://yomu.fyi/post/the-complete-stream-processing-journey-on-flinksql.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Calvin Tran
- Published: Jun 12, 2025
- Type: Problem & solution

The post describes replacing per-user Zeppelin notebooks with a shared FlinkSQL gateway to improve interactive stream processing over Kafka data and support production pipelines. Zeppelin notebooks were limited by support for Flink 1.17, roughly five-minute cold starts, and isolation from internal platforms, while current Flink was 1.20. The replacement uses compute, integration, and query layers: a shared FlinkSQL gateway submits SQL through its REST API to session-cluster jobs, while a custom control plane handles authentication, authorization, session management, orchestration, and paginated results for UI and headless API clients. An interactive UI uses a Hive Metastore catalog to expose Kafka topics as tables, and the platform returns results in one minute rather than five; a configuration-based deployment tool provisions resources and deploys SQL-defined Flink pipelines within ten minutes. After release, the post reports an uptick in created pipelines and interactive queries, but it does not quantify that increase.