8 Big Data Programming Languages (With Key Features)
Updated 15 November 2022
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Big data is a collection of complex data sets that are very large in volume, and programmers require advanced data processing software tools to analyse this data. With the help of big data, businesses can gain valuable information about customers or market trends, allowing them to make profitable business decisions. Learning about programming languages for big data can help you understand how technical experts use them to retrieve, organise, store and update massive amounts of data in databases. In this article, we review eight big data programming languages and share their key features.
8 Big Data Programming Languages
The following is a list of big data programming languages, along with their features:
Python is a general-purpose programming language that programmers use when integrating data analysis with web applications. Data scientists and technical analysts prefer using this open-source programming language because it offers them multiple data manipulation and plotting libraries, such as Pandas and Matplotlib. Data visualisation and machine learning become easy with Python, as it can support varied analyses and integrate with third-party packages. This high-level programming language may also work as a programming interface for an analytics system.
Python has an Application Programming Interface (API) with which users can utilise the language with fast big data engines. With the help of its other libraries, such as NumPy, users can perform scientific computing and mathematical functions efficiently. Seaborn and Folium are some powerful packages that Python holds to help users with their data visualisation functions. Python also has a large community that regularly shares various online resources within forums, helping users to find solutions quickly.
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R is an open-source programming language that users utilise to work with graphics data visualisation and statistics. There is a wide range of graphical tools in R, along with open-source packages that help users visualise, model, manipulate and load data. Its robust environment allows a technical analyst to perform several types of data analyses. This programming language is highly flexible, as a user can run it on almost all operating systems.
Its packages, such as ggplot2, help users to enhance the abilities of data visualisation within this statistical programming language. By using these packages, users can create appealing graphs to show patterns within big data systems. If a user employs sampling in R, they can reduce the size of the data. This helps them to work on small samples, improve iteration speeds and decrease run times. Users can also integrate R with other high-level programming languages, such as C++ or Java.
Related: 18 Big Data Examples (Common Uses In Different Industries)
Scala, also referred to as scalable language, is an efficient programming language for processing data quickly. It supports both functional programming and object-oriented programming (OOP), so it becomes easy for users to utilise languages based on these programming models. Several front-end developers prefer using Scala, as it combines compact syntax with impressive development tools. Fintech companies also utilise Scala to work with data architectures and cloud-based technologies. Scala has several features that a user can employ to write algorithms for machine learning and devise solutions for complex analytics.
Scala has several libraries to support work with big data and machine learning. With the Apache Spark library, a user can process large-scale data efficiently. By using the PredictionIO library, which is a machine-learning server, a user can develop and deploy powerful machine-learning models. Another Scala library is DeepLearning.scala, which uses OOP and functional programming to create neural networks or programs for deep learning. Its developers created Scala for the Java Virtual Machine, also known as JVM, so interacting with code in Java is very easy.
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Programmers use Java to write production code that enables them to use big-data algorithms. Java for big data is helpful when programmers are implementing a theoretical model that they have created in Python. Big-data analysis is easy with Java, as it helps data scientists to process big data, manage higher prediction load and resize intricate ecosystems. Java also works as the base for many big-data tools, such as Spark, Storm or Mahout. Java is the foundation of Scala's Apache Spark library, so knowing how to work with it may also help users to write in Scala comfortably.
Java and Scala form the basis for most platforms that store and process data such as Hadoop Distributed File System, also known as HDFS, which is a big-data platform for data storage and processing. In Java, debugging is quick and easy, and a user can run Java on almost all computer systems. There are many extract, transform and load (or ETL) applications that use Java, such as Apache Kafka and Apache Camel. With the help of these applications, a user can extract, transform and load information in big-data environments easily.
Related: What Is Big Data Hadoop? (Definition And Career Opportunities)
SQL stands for Structured Query Language, which is useful when working with complex datasets outside a relational-database environment. With SQL, a user can perform various operations, such as modifying tables and updating or removing records. It can help a data scientist to work with structured data. There are big-data platforms, such as Spark and Hadoop, that offer extension for querying using this domain-specific language. SQL is an effective tool with which data scientists can wrangle and prepare data, so they use the language when working with different big-data tools.
Users can also use SQL with Python, which helps them to combine the database-management tool with the data-science language to utilise a wide range of libraries, such as SQLite, PostgreSQL and MySQL. A data scientist may require connecting an SQL database to Python to store data that a web application may be recording. This facilitates seamless communication between different data sources. There are also some massive data systems on Hadoop that use an SQL-on-Hadoop engine, making the language suitable for working with big data.
Related: What Is SQL? Definition And Benefits
When technical experts are working with complex machine-learning algorithms, they may often process data sets in terabytes and petabytes. To complete such tasks quickly, they may use C++, as this platform can process data in gigabytes in just a few seconds. Conducting predictive analytics in real time and keeping records consistent are some other benefits of using C++. A data scientist may use the programming language to code libraries and big-data frameworks. There are many deep-learning algorithms and neural networks that data scientists can write in C++.
It is a powerful general-purpose programming language that simultaneously consumes fewer resources and remains a cost-effective tool for computing big data. Managing resources in the language is also efficient, and data scientists get several templates to develop generic code. The language's native libraries may not be as good as the ones present in Java, but there are a variety of third-party libraries that are present for C++.
Related: Data Science Vs Machine Learning: Differences And Challenges
Go, also referred to as Golang, is an open-source programming language that helps developers build simple and efficient software tools. Its presence is usually evident in DevOps and web servers, but it is also a useful language for businesses that make data-driven decisions. A business can use Go to integrate computationally exhaustive algorithms with all its levels of organisational structure. With Go, pre-processing, transforming, analysing, modelling and validating become easy. This language allows users to write controllers for events that occur asynchronously.
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Julia's performance is comparable to C++, which means it is fast, reliable and efficient. It is a programming language that offers robust statistical applicability and an interactive command line. C, R, Java and Python form the basis for many of its libraries. These libraries help data scientists to perform artificial intelligence development with ease. Doing high-level statistical work on this platform is unchallenging. It also outperforms some other languages when working with linear algebra, as it supports several machine-learning equations and matrixes.
Julia comes with primitives for distributed computing, multithreading and parallel computing. Its design applies to cloud computing, as it offers excellent distributed computation. Many data scientists prefer using Julia given its incredible machine-learning speed. It also has quick loading times and can support many external libraries, including the ones that exist in Python and C. With this general-purpose and high-level programming language, writing, executing and implementing code is very fast for data scientists performing scientific calculations.
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