Most developers feel confident in coding with Java. Kafka - It uses a TCP based protocol for message set abstraction to naturally group messages to form linear writes.Ģ.Hadoop - This framework uses the MapReduce algorithm for storing data in a distributed file system.There are many frameworks in Java for data handling too, including: It helps in classification, clustering, and recommendation. Apache Mahout - This is a scalable and distributed algebra framework.It has built-in libraries such as numpy and MATLAB. It’s a toolkit for scientific computing, signal processing, and linear algebra. ND4J - It stands for N Dimension-array objects for Java.It can be integrated with Hadoop and Spark.
Deeplearning4J - It's an open-source, deep-learning toolkit for Java to deploy neural nets.Examples of popular machine learning frameworks are: These frameworks provide the basic functionality to developers and help them save time and money. Java has many excellent frameworks for data science. There are many reasons why data scientists should learn Java. Software development companies like BairesDev build and maintain business-critical applications using Java. Many big companies like Uber, Spotify, and Airbnb are based on Java. As mentioned above, Java has many uses in the machine learning and artificial intelligence domain. But as a data scientist, you should know how to use Java as it offers a host of other services to create a business application.
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