Programming Models for Big Data

- Overview

Big data projects require coding. Big data programming languages ​​include: Java, R, C++, and Python. Python is a popular choice among developers. 

A programming model is an abstract or existing machine or infrastructure. It is a set of abstract execution-time libraries and programming languages ​​that form a computational model. This level of abstraction can be low-level, like machine language in computers. Or a high-level programming language like Java.

So we can say that if the infrastructure that supports big data analysis is the distributed file system we mentioned, then the programming model for big data should support the programmability of operations in the distributed file system. 

What we mean is the ability to use big data to write computer programs that work efficiently on top of distributed file systems and handle all potential problems with ease. 

The big data programming model represents a programming style that provides an interface paradigm for developers to write big data applications and programs. 

The programming model is usually a core feature of a big data framework. It implicitly affects the execution model of the big data processing engine and drives the way users express and construct big data applications. 

- Programming Languages for Big Data

Big data is a collection of large and complex data sets that require programmers to analyze these data using advanced data processing software tools. With the help of big data, businesses can gain valuable information about customers or market trends to make profitable business decisions.

Understanding big data programming languages ​​can help you understand how technicians use them to retrieve, organize, store, and update large amounts of data in databases. 

Programming language, like spoken language, has its own unique structure, format and process. While spoken language is often determined by geography, the use of programming languages ​​is more dependent on coder preference, IT culture, and business goals. 

Today there are many programming languages ​​used for various purposes, but when it comes to big data, you will see the four most prominent ones: Python, R, Java, and Scala.
Some of these languages ​​are better suited for large-scale analytical tasks, while some are good at handling big data and the Internet of Things. 

• R: R is a free software environment for statistical computing and graphics. It compiles and runs on various UNIX platforms, Windows and MacOS.
• Scala: Scala combines object-oriented and functional programming in a concise high-level language. Scala's static typing helps avoid errors in complex applications, and its JVM and JavaScript runtime let you build high-performance systems and easily access a vast ecosystem of libraries.

- Big Data Programming Models

Today, data flows have become abundant thanks to emerging technologies such as cloud computing, edge computing, and the Internet of Things. Many industries, including healthcare, government, media and entertainment, manufacturing, and the Internet of Things, generate large amounts of data every day. 

These industries require analytical models to improve operational efficiency. The data generated by these industries is called big data because it is not only large in quantity, but also fast and in various formats. 

Organizations like McDonald's, Amazon, and Walmart are investing in big data applications to examine large data sets to reveal hidden patterns, unknown correlations, market trends, customer preferences, and other useful business information. 

In big data programming, users write data-driven parallel programs to execute in large-scale, distributed environments. There are many programming models for big data, each with its own focus and advantages. Software developers use programming models to build applications. 

Regardless of the programming language and supported application programming interface (API), the programming model connects the underlying hardware architecture to the software. 

IoT applications such as smart homes, wearable devices, and smart cities generate large amounts of data that need to be processed. Analyzing such large amounts of data is a major challenge. 

- Level of Abstraction

A level of abstraction is a metaphorical layer added to code to hide details of a subsystem. The purpose is to make the system more readable and testable. 

Abstraction in Computer Science is the process of removing irrelevant data, so that only the data required to solve the problem is stored and processed. 

The abstraction hierarchy has five levels of information: Functional purpose, Abstract function, Generalized function, Physical function, Physical form.  The amount of complexity by which a system is viewed or programmed. The higher the level, the less detail. The lower the level, the more detail. The highest level of abstraction is the entire system. The next level would be a handful of components, and so on, while the lowest level could be millions of objects. 

In DBMS, the physical level is the level of abstraction that defines how data is stored and organized in the database. This includes how the data is stored on disk, the access methods used to retrieve the data, and the algorithms used to perform operations. 

[Grindelwald, Switzerland]

- The Requirements for Programming Models

In the era of IoT and social media platforms, vast amounts of digital data are generated and collected from many sources, including sensors, mobile devices, wearable trackers and security cameras. 

This data, often referred to as big data, is challenging current storage, processing and analysis capabilities. New models, languages, systems and algorithms are constantly being developed to effectively collect, store, analyze and learn from big data. 

Disruptive technologies such as cloud computing, blockchain, distributed machine learning, artificial intelligence, and deep learning are used in almost every application field of today's computing systems. This greatly generates large volumes of data with varying computational requirements.

What are the requirements for a big data programming model?

• First, such a big data programming model should support common big data operations, such as splitting large amounts of data. This means partitioning the data into and out of computer memory, and then synchronizing the model of the dataset.
• Access to data should be achieved in a fast manner. It should allow fast distribution to nodes within the rack, which may be data nodes to which we move computation. This means scheduling many parallel tasks at once.
• It should also enable reliability and fault tolerance of computation. This means it should enable programmable replication and file restoration when needed.
• It should be easily extensible to generate distributed data annotations. It should also be able to add new resources to take advantage of distributed computers and scale to more or faster data without loss of performance. This is called scaling out if needed.
• Since there are many different types of data, such as documents, graphs, tables, key-values, etc., the programming model should be able to operate on specific collections of these types. Not every type of data can be supported by a particular model, but a model should be optimized for at least one type.

- Programming Models for Big Data Analytics

Big data analytics refers to advanced and efficient data mining and machine learning techniques applied to large amounts of data. 

A programming model is a set of programming languages and runtime libraries that form a computing model. It is the fundamental style and interfaces for developers to write applications and computing programs. 

Research work and results in the field of big data analysis are constantly emerging, and more and more new efficient architectures, programming models, systems, and data mining algorithms have been proposed.

Programming models are often a core feature of big data frameworks because they implicitly influence the execution model of big data processing engines and also drive the way users express and build big data applications and programs. 

Some popular programming models for big data analysis include:

• MapReduce: A programming model that can process large amounts of data. It uses a parallel, distributed algorithm on a cluster to process and generate big data sets.
• C++: A powerful language for big data projects that can improve processing speed. It can also allow system programming and help write big data frameworks and libraries.
• Magic Memory: A programming model that simplifies the process of big data analysis.

Other popular programming models for big data analysis include:

• Directed Acyclic Graph (DAG)
• Message Passing
• Bulk Synchronous Parallel (BSP)
• Workflow
• SQL-like

Some programming languages for big data include: Scala, Java, R, Python.

- Why Does Big Data Require Programming Models and Algorithms?

Efficient big data management is the grand vision of modern computing as it empowers millions of intelligent, connected devices that can communicate with each other and increasingly control our world. 

Big data analytics is not a single computing paradigm; rather, it serves as an enabling computing technology for various industries such as smart cities, transportation, intelligent systems, energy management systems, healthcare applications, and more. 

Technically, Electronic Health Records (EHR) and eHealth applications are considered as one of the potential examples of Big Data applications, which generate large amounts of data per second and, if processed efficiently, can control the full functionality of electronic devices. health service. 

Therefore, to cope with the growing demand for big data innovation, global data scientists should start to pay attention to advanced big data programming models and algorithms that can quickly learn and automate the big data analysis process in real-time data-intensive applications.

In addition, it should facilitate effective communication, forecasting and decision-making processes. Although the existing big data analysis methods can perform reasonable calculations, the improvement of the current technology application work efficiency has greatly reduced the operability of the traditional big data programming model, and it is necessary to improve the security function.

Exploring high-level programming models for big data is the only way to properly handle these massive amounts of data.  

- Programming Models and Algorithms for Big Data

Here are some programming models and algorithms for big data:

• MapReduce: A popular algorithm for processing big data on clusters. It's used for parallelizable problems across large volumes of structured and unstructured data.
• Support Vector Machine: A set of machine learning algorithms that are used in data mining, data science, and predictive analytics. They are flexible and can generate accurate forecasts.
• Cluster algorithm: A major topic in big data analysis. The goal is to separate an unlabeled dataset into subsets, each with a unique characteristic of its data structure.
• Supervised learning algorithms: Use labeled data to create models that can classify big data and make predictions on future outcomes.
• Apache Spark framework: An open-source framework that provides a unified interface for programming clusters. It has built-in modules that support SQL, machine learning, stream processing, and graph computation.
• Naive Bayes: A model used for large data sets with hundreds or thousands of data points and a few variables. It's fast and easy to implement than other classification algorithms.
• Streaming algorithms: Extract only a small amount of information about the dataset, which preserves its key properties. They are typically allowed to make only one pass over the data.
• KNN algorithm: A supervised classification algorithm that uses labeled data to classify data based on their similarities.

[More to come ...]