Many of you might have heard about real-time streaming. Stream Processing is a critical part of a big data stack in organizations. Today, there are many fully managed Stream Processing frameworks that can very well process large amounts of data. With the increasing demand for real-time data, many big and small companies are adapting event-driven architectures equipped with Stream Processing technologies.
In this article, we will shed light on these frameworks by analyzing their strength and weakness and help you choose the suitable framework for your big data requirements.
What is Stream Processing?
Stream Processing is the near-real time processing of in-motion data. Stream Processing is different from Batch processing as in the latter, data is collected over time and analyzed. Stream Processing allows you to query and analyze data streams and react to critical events in milliseconds.
Big Data Stream Processing frameworks
Stream Processing frameworks to process big data are used in several applications. Developers use Stream Processing engines that allow developers to write code to process the streaming data. There are three major types of Processing engines-
- Open Source Compositional Engine
- Managed Declarative Engine
- Fully-Managed self-Service Engines
Let us now compare some of the most popular Stream Processing frameworks suitable to meet several business needs.
Most Popular Stream Processing frameworks
Apache Spark is a popular Stream Processing framework that has replaced MapReduce as the core engine inside Hadoop. It is a Batch processing framework that has the capability to stream processes. Spark has an in-memory processing engine that conducts analytics, ETL machine learning, and graph processing on data in motion as well as at rest. Spark streaming implements a distributed and fault-tolerant method for processing large amounts of data.
Spark is easy to use and applications are written in Java, Scala, Python and R. Spark can be used as a single framework or can be combined with Hadoop to fulfill business requirements. Its foundation is a spark core that relies on RDD to dispatch tasks. Other elements include Spark SQL, Spark MLlib and GraphX. It is fast, supports multiple languages and easy to do Batch processing.
One of the newest and most promising Stream Processing frameworks, Flink is written in Java and Scala and is a hybrid framework and can also manage Batch processing. In Flink all processing actions are oriented as real-time applications. It exposes several APIs for streaming data like DataStream API. Flink offers support for both event-time management and state management.
Flink does not provide a storage system and has to be used in combination with other frameworks. The interface in flink is easy to navigate and does not require a steep learning curve. You can also integrate with clusters like Hadoop YARN and kubernetes. It has a clean datastream API and documentation.
Apache NiFi is an open-source software project based on Java. It was not a stream data processing framework in the basic sense, but can be used to build real-time data processing applications. Apache NiFi developers do not need to code with a high-level API as data flows can be configured from a GUI.
NiFi works on a flow-based programming model and uses the concept of directed graphs, transformation, and mediation logic. It is an event processing framework and can help users collect and analyze data in real-time.
A distributed Stream Processing framework emerged from LinkedIn to run atop YARN. It uses the Apache Kafka messaging system and architecture to offer fault tolerance and state storage. It offers replicated storage to provide reliable persistence. It can eliminate back pressure allowing data to be processed later.
Samza uses Kafka to handle streams. It also works in combination with other frameworks like Kafka for messaging and Hadoop YARN for fault tolerance and security. The Stateful streaming processing distinguishes it from other streaming technologies.
A distributed Stream Processing framework that has low latency and is apt for near real-time workloads. The use of micro-Batches provides flexibility and it comes with very wide language support.
Storm is written in Clojure and can be used with other programming languages. Storm defines small operations and later composes them into a topology that transforms data.
An open-source Batch and Stream Processing framework that can be used for the processing of big data sets. Hadoop depends on clusters and is designed in a way keeping in mind the fact that hardware will help and the Hadoop framework will handle it. There are four modules in Hadoop- Hadoop Common, HDFS, Hadoop Yarn, and Hadoop MapReduce.
Hadoop splits files into large blocks of data and distributes them across the nodes in a cluster. The advantage with Hadoop is that it can be used with both a traditional data center and through the cloud.
A stream processing Java API powered by Apache Kafka and allows developers to access, filtering, grouping etc without writing any code. It is easy to integrate it with any service to make it a fault-tolerant application.
It offers a low latency value upto 10 milliseconds and reduces the need for multiple integrations. It is often used as a replacement for traditionally used message brokers.
There are many excellent Stream Processing frameworks options but require expertise and hard-work and a correct partner. Each of the frameworks has their advantages and disadvantages and you must analyze each framework before investing into it. Though you have read about all the frameworks, our personal suggestion is to use Apache NiFi. Since every business has different requirements and needs there is no one-size-fits-all. A good big data provider is what you need to evaluate your requirements and offer solutions accordingly.
Ksolves is a leading Apache NiFi development company in India and the USA working with 350+ experienced Apache NiFi experts delivering customized and budget-friendly results. Don’t just believe our words, give it a try and you will see amazing results. If you are interested in more information, write to us in the comments below or call us for your free demo.