---
title: Stream Processing with Apache Flink and NATS | Quick Guide
description: Explore stream processing with Apache Flink and NATS for real-time data handling, efficient event streaming, and scalable analytics solutions
image: https://www.xenonstack.com/hubfs/xenonstack-stream-processing-solutions.png
---

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# Stream Processing with Apache Flink and NATS | Quick Guide

[Navdeep Singh Gill](https://www.xenonstack.com/blog/author/navdeep-singh-gill) | 02 February 2026

Stream Processing with Apache Flink and NATS | Quick Guide

9:25

## Stream Processing with Apache Flink

Apache Flink is a stream processing framework developed by the Apache Software Foundation. It is an open-source platform, a streaming data flow engine that provides **communication and ****distribution **for distributed computations over [data streams](https://www.xenonstack.com/dataops/streaming-data-platform/). Apache Flink is a distributed data processing platform which is used in big data applications and primarily involves the analysis of data stored in the Hadoop clusters. It is capable of handling both the batch and stream processing jobs. It is the alternative of Map-reduce. Some of the best features of it are as follows -

> An open-source, distributed processing engine and framework of stateful computations written in JAVA and Scala. Click to explore about our, [How to Integrate AIOps for DevOps?](https://www.xenonstack.com/blog/integrate-aiops-for-devops)

**Unified framework -** Flink is a unified framework, that allows to build a single data workflow and holds streaming, batch, and SQL. Flink can also process graph with its own [Gelly](https://flink.apache.org/2015/08/24/introducing-gelly-graph-processing-with-apache-flink/) library and use the Machine learning algorithm from its FlinkML library. Apart from This, Flink also supports iterative algorithms and interactive queries.

**Custom Memory Manager** - Flink implements **its memory management inside the JVM and its features are as follows**

- C++ style memory management inside the JVM.
- User data stored in serialized bytes array in JVM.
- Memory can be quickly allocated and de allocated.

**Native Closed Loop Iteration Operators**: Flink has its dedicated support for iterative computations. It iterates on data by using streaming Architecture. The concept of an iterative algorithm is tightly bounded into the flink query optimizer.

> IoT-based smart city systems provide real-time surveillance, analytics, and decision-making tools to improve public safety. Click to explore about our, [Data Processing in IoT](https://www.xenonstack.com/blog/data-processing-in-iot)

## Use Cases of Apache Flink

It is one of the best options to develop and run several types of applications because of its extensive features. Some of the use cases of Flink are as follows -

**Event Driven Applications -** An event-driven application is a type of stateful application through which events are ingested from one or more event streams, and it also reacts to the incoming events. Event-driven applications are based on stateful stream processing applications. Some of the event-driven applications are as follows -

- Fraud Detection
- Anomaly Detection
- Web Application

**Data Analytics Application -** These types of applications extract the information from the Raw data. With the help of a proper stream processing engine, analytics can also be done in real time.

Some of the data analytics Applications are as follows -

- Quality monitoring of networks
- Analysis of product updates and experiment evaluation
- Large scale graph analysis
- Data Pipeline Applications - For converting and moving data from one system to another, **ETL, i.e. Extract, Transform and Load operation** is the general approach and even ETL jobs are periodically triggered for copying the data from the transaction database to analytical database.

 Some of the data pipeline applications are as follows -

- Continuous [ETL](https://www.xenonstack.com/insights/continuous-etl/) operations in e-commerce
- Real-time search index building in e-commerce

### Challenges for enabling IoT in Data Processing

 There are several challenges which are faced by the IoT industries when it comes to data processing some of the are as follows -

- Devices produce more amount of data that users do.
- IoT users also expect real-time information to which they can act on immediately.
- Connectivity can never be guaranteed in the IoT industries.
- Integrating and Managing IoT data.

## Solutions for Stream Processing Using Apache Flink in IoT

Several solutions behind streaming processing using Apache Flink in IoT are as follows -

1. **Real-Time Data Processing—**Many IoT use cases require immediate information and action. Apache Flink is one way to handle real-time data processing.
2. **Event Time for Ordering Data in Iot -** When data from the devices travels through a network, it is essential to account for latency and network failures. Even if the data is sent to a more stable system, the latency will increase with the distance from the data centre.
3. **Tools for Dealing with Messy Data:** Generally the pre-processing of the data is the hardest part of the process. When talking about IoT, it is even harder to control the source. Although streaming of data does not fix the problem. But it can provide several tools for it, like windowing, which is a concept of a grouping of data from a particular time together for further processing.
4. **Segmentation Allows for Parallel Processing -** Usually, the users of IoT devices are more interested in calculating on subsets of data than calculating on the complete data. So, Flink introduces the concept of grouping by key for that type of purpose. In this once a stream is partitioned, it can be executed in parallel.
5. **Local State is Crucial to Performance -** Apache Flink lets us keep data in a proper manner, where the calculations are performed with the help of local state as.
6. **Data Streaming is Conceptually Simple -** Although one has to learn how to manage state properly in Flink, once it is familiar of using flink, we can only focus on the core logic of the application, and leave the other part on the framework to handle it.

> The process used for analyzing the huge amount of data at the moment it is used or produced.Click to explore about our, [Real Time Data Streaming](https://www.xenonstack.com/insights/real-time-data-streaming)

## What is NATS?

NATS is an open source messaging system which consists of a server, a client and a connector framework which is a java based framework used for connecting it with other services. Its server is written in GO programming language. It also provides high performant and flexible messaging capabilities. The essential design principles which makes it easy to use are its performance and scalability. Features of NATS - It provide some of the best and unique features; some of them are as follows -

- **Auto-discovery -** This is a feature to discover routes to other servers makes clustering bliss. For getting a better network between the nodes, we can combine auto-discovery and embedded servers.
- **Optional Persistence -** The NATS server provides the ability to persist messages to ensure their delivery. This feature makes its server very lighter for the users.
- **Clustered mode Server -** Its server can be clustered together and have distributed queuing across the clusters.

## Solutions for Stream Processing Using NATS in IoT

 Several solutions behind streaming processing using Nats IO in Iot are as follows -

### Multiple qualities of service (QoS)

- **At-most-once delivery -** NATS delivers messages to immediately eligible subscribers and do not preserve the messages for other subscribers.
- **At-least-once delivery -** Messages preserved until delivery to the subscribers has been confirmed, or storage has been exhausted.
- **Load balancing -** The application will produce a massive amount of requests, and we would like to use a dynamically scalable pool of worker application instances to ensure the meeting SLAs or other performance targets.
- **Fault tolerance -** The application needs to be highly influential to a network that may be beyond the control and we need the underlying application data communication to seamlessly recover from connectivity outages, so it provides a proper fault tolerance capability.

## Use Cases of NATS

It is one of the most straightforward and powerful messaging systems and offers multiple quality services. Some of the best use cases of Nats are as follows -

- **Command and control -** Sending the commands for running the applications or devices and receiving back the status from the devices or the applications like satellite telemetry and IoT.
- **Addressing discovery -** Sending the data to specific application instances or devices, or users or discovering all the application instances or devices that are connected to the Infrastructure.
- **High Throughput message Fanout -** A few numbers of publishers need to send frequently data to a much larger group of subscribers and many of them also share a common interest in specific data sets or categories.

![streamlined-operations](https://www.xenonstack.com/hubfs/streamlined-operations.svg)

The fusion of digital technology into all industry sectors, changing how you use and convey it to customers. **[Download to explore the potential of Digital Transformation.](https://app.hubspot.com/documents/8161231/view/449840166?accessId=542a4b)**

 Properly managing data streams has helped enterprises meet the demands of a real-time world. We advise you to take the following steps to facilitate streaming analytics as your analytics approach.

> - Take a [Data Assessment Strategy](https://www.xenonstack.com/readiness-assessment/big-data-assessment/) to determine your Analytics IQ
> - Learn more about XenonStack [Streaming and Real Time Analytics Solutions](https://www.xenonstack.com/platform-engineering/real-time-analytics/)Services

## Next Steps Apache Flink and NATS

Talk to our experts about implementing compound AI system, How Industries and different departments use Agentic Workflows and Decision Intelligence to Become Decision Centric. Utilizes AI to automate and optimize IT support and operations, improving efficiency and responsiveness.

[Contact Us](https://www.xenonstack.com/contact-us/) [Talk To Specialist](https://www.xenonstack.com/talk-to-specialist/)

### More Ways to Explore Us

### [Streaming Analytics Architecture, Tools and Best Practices](https://www.xenonstack.com/blog/stream-analytics)

### [Navigating the Data Streaming Landscape in 2025](https://www.xenonstack.com/blog/data-streaming-2025)

### [Apache Fink 2.0 - Real-Time Data Processing](https://www.xenonstack.com/insights/apache-flink-real-time-data)

 

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## Explore Related Topics

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[FinOps](https://www.xenonstack.com/blog/tag/finops)

[Data Foundry](https://www.xenonstack.com/blog/tag/data-foundry)

[XAI](https://www.xenonstack.com/blog/tag/xai)

[Autonomous Agents](https://www.xenonstack.com/blog/tag/autonomous-agents)

[MetaSecure AI](https://www.xenonstack.com/blog/tag/metasecure-ai)

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## Navdeep Singh Gill

Global CEO and Founder of XenonStack

Navdeep Singh Gill is serving as Chief Executive Officer and Product Architect at XenonStack. He holds expertise in building SaaS Platform for Decentralised Big Data management and Governance, AI Marketplace for Operationalising and Scaling. His incredible experience in AI Technologies and Big Data Engineering thrills him to write about different use cases and its approach to solutions.

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09 February 2026

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XenonStack Agentic Foundry powers enterprise agentic systems with unified infra, analytics, workflows, and security to drive automation and compliance.

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## AI Engineering

[Composite AI](https://www.xenonstack.com/artificial-intelligence/composite-ai/) [Decision AI](https://www.xenonstack.com/artificial-intelligence/decision-ai/) [AI Quality](https://www.xenonstack.com/artificial-intelligence/ai-quality/) [Generative AI](https://www.xenonstack.com/artificial-intelligence/generative-ai/) [Multimodal AI](https://www.xenonstack.com/artificial-intelligence/multimodal-ai/) [AI Assurance](https://www.xenonstack.com/artificial-intelligence/explainable-ai/) [MLOps](https://www.xenonstack.com/artificial-intelligence/mlops/) [Physical AI](https://www.xenonstack.com/artificial-intelligence/physical-ai/) [Augmented Engineering](https://www.xenonstack.com/artificial-intelligence/ai-augmented-software-development/) [Generative BI](https://www.xenonstack.com/artificial-intelligence/generative-bi/)

## Data Foundry

[Streaming Data Platform](https://www.xenonstack.com/dataops/streaming-data-platform/) [Data Lakehouse](https://www.xenonstack.com/dataops/delta-lake/) [Data Catalog](https://www.xenonstack.com/dataops/data-catalog/) [Data Observability](https://www.xenonstack.com/dataops/data-observability/) [Cloud Data Warehouse](https://www.xenonstack.com/dataops/cloud-data-warehouse/) [Data Engineering](https://www.xenonstack.com/dataops/data-engineering/) [MetaData Management](https://www.xenonstack.com/dataops/metadata-management/) [Data Quality](https://www.xenonstack.com/dataops/augmented-data-quality/) [Real Time Analytics](https://www.xenonstack.com/dataops/real-time-analytics/) [Data Modernization](https://www.xenonstack.com/dataops/data-modernization/)

## Platform Engineering

[Cloud Native](https://www.xenonstack.com/cloud-native/platform-engineering/) [Automation As Code](https://www.xenonstack.com/cloud-native/automation-as-code/) [Observability](https://www.xenonstack.com/cloud-native/observability/) [FinOps](https://www.xenonstack.com/cloud-native/finops/) [Application Modernization](https://www.xenonstack.com/cloud-native/application-modernization/) [DevSecOps](https://www.xenonstack.com/cloud-native/devsecops/) [Site Reliability Engineering](https://www.xenonstack.com/cloud-native/site-reliability-engineering/) [Progressive Delivery](https://www.xenonstack.com/cloud-native/progressive-delivery/) [GitOps](https://www.xenonstack.com/cloud-native/gitops/) [Compliance as code](https://www.xenonstack.com/cloud-native/compliance-as-code/) [Value Stream Management](https://www.xenonstack.com/cloud-native/value-stream-management/) [Policy as Code](https://www.xenonstack.com/cloud-native/policy-as-code/) [Telemetry Pipeline](https://www.xenonstack.com/cloud-native/telemetry-pipeline/)

## Agentic AI

[Agentic Analytics](https://www.xenonstack.com/agentic-ai/enterprise-systems/) [Agentic AI Systems](https://www.xenonstack.com/agentic-ai/agentic-ai-system/) [Process Intelligence](https://www.xenonstack.com/agentic-ai/business-process-operations/) [Developer Experience](https://www.xenonstack.com/agentic-ai/developer-experience-platform/) [Autonomous Operations](https://www.xenonstack.com/agentic-ai/autonomous-operations/) [AI Vision at EDGE](https://www.xenonstack.com/agentic-ai/edge-and-vision-ai/) [Compound AI System](https://www.xenonstack.com/agentic-ai/compound-ai-system/) [GUI Agents](https://www.xenonstack.com/agetic-ai/gui-agents/) [CMDB Management](https://www.xenonstack.com/agentic-ai/cmdb-management/) [ITSM](https://www.xenonstack.com/agentic-ai/itsm/) [Network Automation](https://www.xenonstack.com/agentic-ai/network-automation/)

## AI Agents

[DataBricks AI Agents](https://www.xenonstack.com/ai-agents/databricks-ai-agents/) [SnowFlake AI Agents](https://www.xenonstack.com/ai-agents/snowflake-ai-agents/) [ServiceNow AI Agents](https://www.xenonstack.com/ai-agents/servicenow-ai-agents/) [AWS AI Agents](https://www.xenonstack.com/ai-agents/aws-ai-agents/) [Microsoft Azure AI Agents](https://www.xenonstack.com/ai-agents/microsoft-azure-ai-agents-actions/) [SalesForce AI Agents](https://www.xenonstack.com/ai-agents/salesforce-agents-actions/) [MySQL AI Agents](https://www.xenonstack.com/ai-agents/mysql-agents-actions/) [PostgreSQL AI Agents](https://www.xenonstack.com/ai-agents/postgresql-agents-actions/) [Datadog AI Agents](https://www.xenonstack.com/ai-agents/datadog-agents-actions/) [DynaTrace AI Agents](https://www.xenonstack.com/ai-agents/dynatrace-agents-actions/) [Splunk AI Agents](https://www.xenonstack.com/ai-agents/splunk-agents-actions/) [BigQuery AI Agents](https://www.xenonstack.com/ai-agents/bigquery-agents-actions/) [SAP AI Agents](https://www.xenonstack.com/ai-agents/sap-agents-actions/) [Infor AI Agents](https://www.xenonstack.com/ai-agents/infor-agents-actions/) [Oracle AI Agents](https://www.xenonstack.com/ai-agents/oracle-agents-actions/) [WorkDay AI Agents](https://www.xenonstack.com/ai-agents/workday-agents-actions/) [Jira AI Agents](https://www.xenonstack.com/ai-agents/jira-agents-actions/)

## Industry

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## Enterprise Support

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## Solutions

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## Company

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## Agent SRE for Reliability and Observability Solutions

 AI continuously monitors systems for risks before they escalate. It correlates signals across logs, metrics, and traces. This ensures faster detection, fewer incidents, and stronger reliability

- ![Performance Icon](https://www.xenonstack.com/hubfs/performance.svg)Proactive detection of performance and availability issues
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[Explore Agent SRE](https://agentsre.ai/)

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## Physical Surveillance with Vision AI Agent Technology

 AI converts camera feeds into instant situational awareness. It detects unusual motion and unsafe behavior in real time. Long hours of video become searchable and summarized instantly

- ![Motion Icon](https://www.xenonstack.com/hubfs/motion.svg)Real-time detection of suspicious motion or intrusion
- ![Video Search Icon](https://www.xenonstack.com/hubfs/video-search.svg)Natural language video search and instant playback
- ![Summary Icon](https://www.xenonstack.com/hubfs/summary.svg)Smart summaries for audits, investigations, and compliance

[See Vision AI in Action](https://www.xenonstack.ai/)

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## Agentic Data Intelligence Across Your Full Data Stack

 Your data stack becomes intelligent and conversational. Agents surface insights, detect anomalies, and explain trends. Move from dashboards to autonomous, always-on analytics

- ![Connects warehouses](https://www.xenonstack.com/hubfs/data-icon.svg)Connects to warehouses, lakes, and streaming sources
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[See in Action](https://elixirdata.co/)

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## Intelligent Diagnostic for Self-Healing System Automation

 Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment

- ![Diagnostics Icon](https://www.xenonstack.com/hubfs/diagnostic.svg)Automated diagnostics for recurring errors
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[See in Action](https://agentanalyst.ai/)

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✕

## Agentic GRC - Monitoring Risk and Compliance Controls

 AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready

- ![Controls Icon](https://www.xenonstack.com/hubfs/controls.svg)Continuous control checks across infrastructure and SaaS
- ![Audit Icon](https://www.xenonstack.com/hubfs/audit.svg)Automated evidence collection for audits
- ![Risk Icon](https://www.xenonstack.com/hubfs/risk.svg)Risk scoring and prioritized remediation recommendations

[Explore Agent GRC](https://agentgrc.ai/)

![monitoring-risk-and-compliance](https://www.xenonstack.com/hubfs/enhanced-security-measures.svg)

✕

## Agentic Finance and Procurement Intelligent Agents

 Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter

- ![Visibility Icon](https://www.xenonstack.com/hubfs/visibility.svg)Real-time visibility into spend and commitments
- ![Anomaly Icon](https://www.xenonstack.com/hubfs/anomaly.svg)Anomaly detection on invoices and vendor performance
- ![Workflow Icon](https://www.xenonstack.com/hubfs/workflow.svg)Intelligent workflows for approvals and sourcing decisions

[Optimize Finance & Procurement](https://www.xenonify.ai/)

![agentic-finance-and-procurement](https://www.xenonstack.com/hubfs/responsible-ai-aviators-banner-illustration.svg)

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