Every programming language has its own set of features. If one wants to pioneer in any particular domain of technology, it is very crucial to have a strong command over any programming language. Coding can be said as the most initial and primary thing in a developer’s toolkit.
It is widely known that Python is not only useful but also the most used languages around the globe. Besides Python, there are also several languages which are used by the developers while working on Big data. In this article, we list the programming languages which are best suited for working on Big data.
This open source programming language always takes the throne in such cases because of its easy-to-use in nature and the huge community. You can work in Big data projects with a much faster manner because of the enormous number of libraries available.
Pydoop, a Python interface to Hadoop enables you to do MapReduce programming via a pure Python client for Hadoop pipes. It is a package which provides a Python API for Hadoop MapReduce and HDFS in order to solve the complex problems with minimal efforts. It has several features specially designed for Hadoop as mentioned below.
- It has a rich HDFS API, which allows you to connect to an HDFS installation, read and write files along with getting information on the global file system.
- It has a MapReduce API which allows you to write pure Python record
- This interface transparently supports reading as well as writing Avro (Data Serialisation system) records in MapReduce applications.
- It has easy installation-free usage.
This open source programming language is developed by keeping the statisticians in mind and it offers great data visualisation capability. As we know that this language is built for the statisticians by the statistician, it provides several prominent features which are useful while doing any Big data project and they are mentioned below:
- This free and open source code can be accessed by anybody as well as modify and improve it.
- This language supports great visualisation, manipulation, statistical modelling, imputation, analysis, etc.
- Some of the packages designed to handle Big data are such as bigmemory (creates, store, access and manipulate massive matrices which are allocated to shared memory and may use memory-mapped files), fast file access (ff) (provides data structures which are stored on disk but behave as if stored in RAM by transparently mapping only a section ), etc.
pbdR (Programming with Big Data in R) is a set of highly scalable R packages which includes high-performance, high-level interfaces to MPI, ZeroMQ, ScaLAPACK, and many more for distributed computing and profiling in data science.
Runs on Java Virtual Machine (JVM), this popular language has the capability to unify data science techniques into an existing code database and is used to write codes with high productivity. Java is the language behind Hadoop and which is why it is crucial for the big data enthusiast to learn this language in order to debug Hadoop applications.
Java Data Mining Package (JDMP) is a Java library for machine learning and Big Data Analytics which facilitates the access to data sources and machine learning algorithms and provides visualisation modules.
Scala or Scalable Language is a high-level, open sourced programming language. This is a compiled language which helps to execute faster outcomes, supports both object-oriented as well as functional programming, enables to explain algorithms at a higher level of abstraction, runs on Java Virtual Machine which made possible to directly run Java codes, use libraries, etc.
Apache Spark is written in Scala which is a unified analytics engine for large-scale data processing which has several features such as it runs workloads with high performance, offers over 80 high-level operators, combine SQL, streaming and complex analytics, etc.
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A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.