Big Data Technologies

  • Mister siswa
  • 2022 December 03T10:11
  • Big Data

Prior to the development of big data technologies, conventional programming languages and simple structured query languages were used to manage the data. Due to the constant expansion of each organization's information and data as well as the domain, these languages were not effective enough to handle the data. Because of this, it has become crucial to manage such massive amounts of data and implement a reliable technology that meets all the requirements and needs of clients and large organizations and is in charge of data production and control. Big data technologies are the current vogue term for all these requirements.

In this article, we'll talk about the top technologies that have developed new applications to support Big Data's ascent to greater heights. Let's first quickly understand big data technology before talking about big data technologies.

What is Big Data Technology?

Software-utility is the definition of big data technology. This technology's main purpose is to evaluate, process, and extract data from a sizable collection of exceedingly complicated structures. For conventional data processing tools, this is quite challenging to handle.

The Internet of Things (IoT), deep learning, machine learning, artificial intelligence (AI), and other technologies that are greatly amplified by big data are among the bigger notions of rage in technology. In addition to these technologies, big data technologies concentrate on the analysis and management of substantial amounts of batch-related and real-time data.

Types of Big Data Technology

Let's first talk about the board classification of big data technologies before we begin the list. Big Data technology is mostly divided into the two categories below:

Operational Big Data Technologies

The fundamental daily data that individuals used to process is mostly included in this kind of big data technology. Daily data from online transactions, social media platforms, and any specific organization or company are typically included in operational big data, which is typically required for analysis using software based on big data technology. The data, which is used as an input by various analytical big data technologies, may also be referred to as the raw data.

The following list includes some specific instances of operational big data technologies:

  • System for reserving tickets online for cinemas, trains, planes, and buses, among other things.
  • E-commerce platforms like Amazon, Flipkart, Walmart, etc. are used for online trading or shopping.
  • Information found online on social networking platforms like Facebook, Instagram, WhatsApp, etc.
  • Information on executives or staff of global corporations.

Analytical Big Data Technologies

Big Data Analytics is frequently referred to as an enhanced form of Big Data Technologies. Comparing this form of big data technology to operational big data, it is a little more complex. When performance criteria are in use and significant real-time business choices are made based on reports produced by evaluating operational-real data, analytical big data is mostly used. This indicates that this form of big data technology covers the actual analysis of massive data that is crucial for business choices.

The following is a list of typical applications for analytical big data technologies:

  • Data on stock marketing
  • Data from weather forecasts and time series analysis
  • Medical records that allow clinicians to check on a patient's health status personally
  • maintaining the databases for space missions, where every detail of a trip is crucial

Top Big Data Technologies

The top big data technologies can be divided into the following four categories:

  • Data Storage
  • Data Mining
  • Data Analytics
  • Data Visualization

Data Storage

Let's start by talking about the top data storage technologies for big data:

  • Hadoop: Hadoop is one of the top technologies used when dealing with large amounts of data.This method is heavily dependent on the map-reduce architecture and mostly deals with batch data processing. It also has the ability to work in groups to finish tasks. The major purpose of the Hadoop framework's introduction was to store and analyze data in a distributed data processing environment alongside common hardware and a straightforward architecture for programming execution.
  • In addition, Hadoop is the greatest option for quickly and cheaply storing and analyzing data from various machines. Hadoop is regarded as one of the fundamental elements of big data technologies because of this. In December 2011, the Apache Software Foundation released it. Java is the programming language used to create Hadoop.
  • MongoDB: In terms of storage, MongoDB is yet another crucial part of big data technology. MongoDb is a NoSQL database, hence neither relational nor RDBMS properties apply to it. Traditional RDBMS databases that employ structured query languages are not the same as this. MongoDB instead makes use of schema documents. MongoDB's data storage is structured differently from conventional RDBMS databases. As a result, MongoDB can store enormous volumes of data. Its foundation is a straightforward, cross-platform document-oriented design. The MongoDB database employs JSON-like documents with a schema. Operational data storage solutions, which are common in financial firms, are ultimately aided by this. Because of this, distributed systems can use MongoDB instead of conventional mainframes to handle a variety of high-volume data types.MongoDB was released in February 2009 by MongoDB Inc. It was created using a combination of the languages C++, Python, JavaScript, and Go.
  • RainStor: Popular database management system RainStor was created to handle and analyze the needs of big data in enterprises. It makes use of deduplication techniques to manage managing and storing enormous amounts of data for reference.A RainStor Software Company created RainStor in 2004. It functions exactly like SQL. For their large data demands, businesses like Barclays and Credit Suisse use RainStor.
  • Hunk: Hunk is most useful when virtual indexes are used to retrieve data on distant Hadoop clusters. This makes it easier for us to evaluate data using the spunk search processing language. Hunk additionally enables us to report and visualize enormous amounts of data from NoSQL and Hadoop data sources.Splunk Inc. unveiled Hunk in 2013. The Java programming language serves as its foundation.
  • Cassandra: One of the top big data technologies among the best NoSQL databases is Cassandra. It is distributed, open-source, and offers a wide range of column storage possibilities. It is open source and consistently delivers high availability. This finally aids in handling data on huge commodity groups efficiently. Fault-tolerant techniques, scalability, MapReduce support, distributed nature, eventual consistency, query language property, tunable consistency, multi-datacenter replication, etc. are some of Cassandra's key features. The Apache Software Foundation created Cassandra in 2008 for the Facebook inbox search function. The Java programming language serves as its foundation.

Data Mining

Now let's talk about the top data mining technologies for big data:

  • Presto: Presto is a SQL query engine that is distributed and open-source that was created to process interactive analytical queries against massive data sources. Data sources can range in size from gigabytes to petabytes. Relational databases, proprietary data storage systems, Cassandra, Hive, and Presto can all be used to query data. The Apache Software Foundation created Presto in 2013, a Java-based query engine. Companies like Repro, Netflix, Airbnb, Facebook, and Checkr are utilizing and benefiting from big data technology..
  • RapidMiner: When it comes to creating, delivering, managing, and maintaining predictive analytics, RapidMiner is described as the data science software that gives us a very strong and powerful graphical user interface. We can develop sophisticated workflows and scripting support in a number of programming languages using RapidMiner. Ralf Klinkenberg, Ingo Mierswa, and Simon Fischer created RapidMiner, a Java-based centralized solution, in 2001 at the AI department of the Technical University of Dortmund. Its original name was YALE (Yet Another Learning Environment). Boston Consulting Group, InFocus, Domino's, Slalom, and Vivint are a few examples of businesses that utilize RapidMiner effectively. SmartHome.
  • ElasticSearch: Elasticsearch is regarded as a crucial tool for information discovery. Usually, it combines the core elements of the ELK stack (i.e., Logstash and Kibana). Simply said, ElasticSearch is a search engine that functions similarly to Solr and is based on the Lucene library. Additionally, it offers a multi-tenant, entirely distributed search engine. The JSON documents on this search engine's HTTP web interface are schema-free and entirely text-based. Shay Banon created ElasticSearch in 2010, which is mostly written in the Java computer language. Elastic NV has been in charge of it since 2012. Numerous eminent businesses, like LinkedIn, Netflix, Facebook, Google, Accenture, StackOverflow, etc., use ElasticSearch.

Data Analytics

Let's talk about some of the most popular big data technologies that fall under data analytics:

  • Apache Kafka: Popular streaming software is Apache Kafka. The three main functions of this streaming platform are publisher, subscriber, and consumer. This type of platform is referred to as a "distributed streaming platform." It can accept and handle real-time streaming data and is further described as a direct messaging, asynchronous messaging broker system. This system resembles an enterprise messaging system or messaging queue almost exactly. Additionally, Kafka offers a retention time and permits the producer-consumer method for data transmission. Kafka has undergone numerous improvements and now has some extra tiers or attributes, like schema, Ktables, KSql, registry, etc. It was created in 2011 by the Apache software community and is written in Java. Twitter, Spotify, Netflix, Yahoo, LinkedIn, and other well-known businesses use the Apache Kafka platform.
  • Splunk: One of the well-known software platforms for gathering, relating, and indexing real-time streaming data in searchable repositories is called Splunk. Utilizing linked data, Splunk may also create graphs, alerts, summarised reports, dashboards, etc. Web analytics and business insight generation are its key benefits. Additionally, Splunk is utilized for compliance, application management, security, and control. In the year 2014, Splunk Inc. released Splunk. It is written with XML, Python, C++, and AJAX. Splunk is used effectively by businesses like Trustwave, QRadar, and 1Labs for their analytical and security requirements.
  • KNIME: KNIME is used to visualize data flows, carry out particular tasks, and evaluate models, outcomes, and interactive views. Additionally, it enables us to complete all of the analysis procedures at once. It has an extension mechanism that allows for the insertion of additional plugins, adding new features and functionalities. Java is used to create KNIME, which is based on Eclipse. It was created by KNIME Company in 2008. Harnham, Tyler, and Paloalto are a some of the businesses that use KNIME.
  • Spark: One of the key technologies on the list of big data technologies is Apache Spark. One of such fundamental technologies, it is widely utilized by leading businesses. The in-memory computing capabilities that Spark is recognized for providing contribute to the overall speed of the operating process. A generalized execution paradigm is also provided to support more applications. Additionally, it has top-level APIs (like Python, Java, and Scala) to make development easier. Additionally, Spark enables processing and handling of real-time streaming data through the use of batching and windowing procedures. In the end, this aids in the generation of datasets and data frames built upon RDDs. The result is the production of Spark Core's essential parts. Machine learning and data science are analyzed and processed with the aid of tools like Spark MlLib, GraphX, and R. The languages Java, Scala, Python, and R are used to create Spark. In 2009, the Apache Software Foundation created it. Big data technology is being effectively utilized by businesses like Amazon, ORACLE, CISCO, Verizon Wireless, and Hortonworks.
  • R-Language: Programming language R is described as being primarily utilized in statistical computation and graphics. Leading statisticians, practitioners, and data miners use this free software environment. The development of statistically based software and data analytics is where language is most helpful. R-Foundation first launched the R language in February 2000. It has been created in Fortran. R-Language is used by organizations like Barclays, American Express, and Bank of America for their data analytics requirements.
  • Blockchain: A number of applications relating to various industries, including finance, supply chains, manufacturing, etc., can use the blockchain technology. It is typically utilized in processing processes like escrow and payments. This aids in lowering the likelihood of fraud. Additionally, it increases financial secrecy, speeds up transaction processing overall, and globalizes markets. Additionally, it is employed in any business network environment to meet the demands of shared ledger, smart contract, privacy, and consensus. Stuart Haber and W. Scott Stornetta, two researchers, initially presented blockchain technology in 1991. However, the debut of Bitcoin in January 2009 was the first actual use of the blockchain. This particular database is built using Python, C++, and JavaScript. Among the leading businesses utilizing Blockchain technology are ORACLE, Facebook, and MetLife.

Data Visualization

Let's talk about the top Big Data Technologies under Data Visualization:

  • Tableau: One of the quickest and most potent data visualization tools is Tableau, which is utilized by the top business intelligence sectors. It makes it possible to analyze data much more quickly. Dashboards and worksheets are two ways that Tableau aids in the creation of visuals and insights. A firm called TableAU created and maintains Tableau. It first appeared in May 2013. It is composed in a variety of languages, including Python, C, C++, and Java. Cognos, QlikQ, and ORACLE Hyperion are a few of the leading businesses adopting this product.
  • Plotly: Plotly is great for efficiently and quickly plotting or making graphs and associated components, as the name implies. It includes a number of comprehensive libraries and APIs, including those for MATLAB, Python, Julia, REST API, Arduino, R, Node.js, etc. This facilitates interactive graph decorating in Pycharm and Jupyter notebook. The Plotly startup unveiled Plotly in 2012. It is JavaScript-based. Some of the businesses that benefit from Plotly are Paladins and Bitbank.

Emerging Big Data Technologies

In addition to the big data technologies already discussed, there are a number of new ones. Among these, the following technologies are crucial:

  • TensorFlow: Researchers may implement the cutting-edge in machine learning thanks to TensorFlow, which combines a number of thorough libraries, adaptable ecosystem tools, and community resources. Additionally, this finally enables developers to create and deploy applications that use machine learning in certain contexts. The Google Brain Team unveiled TensorFlow in 2019. It mostly uses Python, CUDA, and C++. For their business needs, organizations like Google, eBay, Intel, and Airbnb are embracing this technology.
  • Beam: A portable API layer in Apache Beam makes it easier to create and manage complex parallel data processing pipelines. It also enables the execution of created pipelines across a variety of execution engines or runners, in addition to the aforementioned. The Apache Software Foundation unveiled Apache Beam in June 2016. It was created using Java and Python. This technology is used by some well-known businesses, including Amazon, Oracle, Cisco, and Verizon Wireless.
  • Docker: Docker is a specialized tool created with the intent of making the creation, deployment, and execution of programs employing containers easier. Containers typically assist developers in correctly packaging applications, including all necessary parts like libraries and dependencies. Typically, all components are bound together by containers and sent as a single unit. Docker was first released by Docker Inc. in March 2003. It has a Go language foundation. This technology is used by organizations including Business Insider, Quora, Paypal, and Splunk.
  • Airflow: A workflow automation and scheduling system is what is known as Airflow in technology. Data pipelines are mostly controlled and maintained using this technology. It includes workflows made up of several tasks and created using the Directed Acyclic Graphs technique. Workflows can be defined by developers in the code to make versioning, testing, and maintenance simple. In May 2019, the Apache Software Foundation unveiled Airflow. Its foundation is the Python language. Utilizing this cutting-edge technology are businesses like Checkr and Airbnb.
  • Kubernetes: Google released Kubernetes as an open-source cluster and container management platform in 2014. It is described as being vendor-neutral. It offers a framework for activities involving application containers, automation, deployment, scaling, and host clusters. In July 2015, the Cloud Native Computing Foundation unveiled Kubernetes. The language of writing is Go. Utilizing this technology effectively are organizations like American Express, Pear Deck, PeopleSource, and Northwestern Mutual.

These technologies are new. They are not constrained, though, as the big data ecosystem continues to grow. Because of this, new technologies are being developed at a very rapid rate to meet the demands and needs of the IT businesses.

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