Today’s businesses benefit most when they can respond to events in real time – as they happen. Real-time data analysis is an important part of businesses. Here is a Discussion on Apache Free Software Solutions for Data Streaming. Different data stream processing solution is suitable for different purposes. Real-time data analytics increaingly becomin important. Data-derived insights are useful, but the value of some of these insights may decreases quite rapidly over time like just for web analytics.
Real-time data stream processing can handle large volumes of data efficiently thus providing data insights within few milliseconds. The stream processing technology stores streaming data in error free way, is scalable to large computer pools and is characterized by high reliability. Thus events (such as financial transactions, user behavior on websites, data from IoT sensors) can be processed reliably and immediately with very little delay. Traditional databases, on the other hand, are based on the approach that companies gain insights through business intelligence (BI) analytics and then take action. Stream processing thus differs from previously used data analysis technologies in that it processes data directly at the time of generation.
Apache Free Software Solutions for Data Streaming
Four different open source- based technologies are currently dominating the stream processing segment: Apache Spark, Apache Storm, Apache Flink and Kafka Streams, a subcomponent of Apache Kafka. We have installation guides for them :
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- Install Apache Spark on Ubuntu Single Cloud Server With Hadoop
- How To Install Apache Flink on Ubuntu Server
- Install Apache Kafka on Ubuntu 16.04
We also have guides on other streaming engines :
- Apache Apex – unified platform for big data stream and batch processing.
- Apache Gearpump– lightweight real-time distributed streaming engine built on Akka.
- Apache Samza – distributed stream processing framework that build on Kafka(messaging, storage) and YARN(fault tolerance, processor isolation, security and resource management).
- Apache Storm – distributed real-time computation system. Storm is to stream processing what Hadoop is to batch processing.
- Apache S4 – general-purpose, distributed, scalable, fault-tolerant, pluggable platform that allows programmers to easily develop applications for processing continuous unbounded streams of data.
Other someway related software we have installation guides are :
Individual Apache Solutions for Data Streaming
Apache Spark
Apache Spark is an open-source engine designed specifically for processing large amounts of data and analysis, as well as accelerated analysis on Hadoop . Spark offers the ability to access data from a variety of sources, including OpenStack Swift, Amazon S3 and Cassandra, as well as Hadoop Distributed File System (HDFS). Spark is designed as a batch processor that performs stream processing by splitting the stream into small micro-batches. We have previously published article on Apache Spark Alternatives To Overcome Integrity Issues.
Apache Storm
Apache Storm is a framework for distributed stream processing computation that, like Spark, is being developed as a project of the Apache Software Foundation. Storm was one of the first open source systems for continuous data stream processing and works using existing queuing and database technologies to handle complex data streams. Key applications include real-time analytics, machine learning, and continuous computing.
Apache Flink
Apache Flink serves as a framework and distributed processing engine for stateful calculations of unlimited and limited data streams. Flink is designed to run in all common cluster environments, performing in-memory speed calculations and on any scale. In recent years, Apache Flink has established itself as one of the most competitive stream processing engines in the open source environment.
Kafka
Kafka Streams is a client library for application creation and microservices that stores input and output data in Kafka clusters. It combines the simplicity of writing and delivering standard client-side Java and Scala applications with the benefits of Kafka server-side clustering technology. The streamlined Kafka Streams library supports message processing in microservices and real-time event processing.
Conclusion
We deliberately avoided the rest of the software and limited to only four solutions. Among the four popular stream processing technologies, Apache Flink is currently the one with the highest priority. Apache Flink was recently talked about as it serves as a base to support stateful stream processing and its extension with fast, serializable ACID transactions (Atomicity, Consistency, Isolation, Durability) directly to streaming data. Flink is stream-native and robust, which allows access to constructs in terms of state and time, fault-tolerant and high-performance. Each of the other technologies mentioned here will have some of these attributes, but Flink delivers the complete package.
Apache Spark seems sufficient at first glance or even in the proof-of-concept phase for most stream processing purposes. However, in practice, it often requires laborious reconciliation of workload, cluster, and spark-specific configurations such as micro-batch interval and micro-batch size. While Spark focused on fast batch processing, Flink is designed from the ground up to process continuous data streams, stream processing.
Apache Storm makes a difference between Storm Core and Storm Trident. While Storm Trident is more micro-based, Storm Core is more eventful than Apache Flink. But Flink is essentially event-driven and does not distinguish between streaming and batching. In addition, Flink is significantly more efficient in terms of throughput than Storm.
Kafka Streams was developed to read, process and rewrite data streams from Kafka into Kafka. Kafka Streams was developed as a library, which in the end is not as powerful, robust and performant as Apache Flink.
Apache Flink is gaining ground in the data stream environment and has the fastest growing adoption rate. Large technology companies that need to work in real-time due to their business model, such as Alibaba, Uber, and Netflix already rely on Apache Flink. Other companies use Apache Flink to run mission-critical applications such as real-time analytics, machine learning, search and content ranking, and real-time fraud detection. Other use cases, especially for the financial services sector, include master data management, capital risk management, and real-time recommendations in e-commerce.
