Stream Processing with Apache Flink and Kafka

Stream Processing with Apache Flink

Apache Flink is a stream processing framework which is developed by Apache Software Foundation. It is an open source platform which is a streaming data flow engine that provides communication, and data-distribution for distributed computations over data streams. 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. Apache flink is capable of handling both the batch and stream processing jobs. It is the alternative of Map-reduce.

Some of the best features of Apache Flink are as follows –

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 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.


Use Cases of Apache Flink

Apache Flink 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 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 –

Real-Time Data Processing – Many IoT use cases require immediate information and an action to be followed. So, for handling real-time data processing Apache flink is one way.

Event Time for Ordering Data in IoT – When data from the devices travel through a network, it is essential to account for latency and network failures, even if the data was sent to a more stable system, the latency will increase with the distance from the data center.

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.

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.

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.

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.


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 NATS with other services. The NATS server is written in GO programming language. It also provides high performant and flexible messaging capabilities. The essential design principles which makes NATS easy to use are its performance and scalability.Features of NATS –

Nats 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 the Nats server very lighter for the users.
  • Clustered mode Server – NATS 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

NATS is one of the most straightforward and powerful messaging systems and offers multiple quality of 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 applications 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.


A comprehensive Approach 

The proper management of data streams has helped to enterprises to meet the demands of a real-time world. To facilitate Streaming Analytics as your Analytics Approach we advice taking the subsequent steps:



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