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Efficient data management is becoming crucial for companies day by day in this tech-driven era. The emergence of AI has generated an essential sub-field known as Big Data that deals with multiple sets of complex real-time data analytics. This transformation of data into business insights is done by some popular Big Data technologies installed into the existing computer systems. Big Data technologies are divided into four fields for efficient utilization— data storage, data mining, data visualization, and data analytics. Companies, that are still concerned with which Big Data technology is suitable for managing the data explosion, can dig into this article.
Hadoop is one of the best open-source software that allows distributed processing of multiple sets of real-time data across several clusters of computers with simple programming models. It helps in scalability from single servers to thousands of machines by detecting any failure at the application layer. There are five current projects available in modules— Hadoop Common, Hadoop Distributed File System, Hadoop YARN, Hadoop MapReduce, and Hadoop Ozone. The frameworks are written in Java that can process any size and format of real-time data. It is cost-effective and provides efficient service even in severe unfavorable conditions such as cyberattacks or machine crashes.
MongoDB is a document-oriented distributed database facilitating the data management of unstructured or semi-structured real-time data for application developers. It is one of the most popular open-source data analysis tools that is utilized to create the most innovative products and services in the global market. It helps to store data in JSON-like documents that allow flexible and dynamic schemas. There is a multi-cloud database service for MongoDB known as MongoDB Atlas that provides top-notch automation and built-in practices to provide continuous availability, elastic scalability as well as support with regulatory compliance. It also provides a powerful query language for aggregation, geo-based search, text search, graph search, ad hoc queries, indexing, and many more facilities.
R is another Big Data technology used for statistical computing and graphics in the programming language. This programming software provides a diverse range of functionalities to Big Data engineers, statisticians, etc.- linear modeling, non-linear modeling, classical statistical test, time-series analysis, clustering also graphical techniques. It is a well-designed platform with the availability of various mathematical symbols and formulae. It facilitates effective data management that has a large coherent and integrated collection of effective tools for real-time data analytics.
Tableau is a robust Big Data technology that can be connected to several open-source databases. The server even provides a free public option to create appropriate visualization. This analytics platform consists of various attractive features like sharing options with anybody, moderate speed to enhance extensive operation, integrated with more than 250 applications, and most importantly assists to solve big real-time data analytics issues. It is one of the most powerful, secure, flexible end-to-end real-time data analytics platforms. It generates a series of Tableau product lines— Tableau Prep, Tableau Desktop, Tableau Server, and Tableau Online as well as Tableau Mobile.
Cassandra is an open-source NoSQL database that transforms multiple sets of real-time data into in-depth analysis. It has linear scalability with proven fault-tolerance on both commodity hardware and cloud infrastructure. Cassandra ensures no data loss while the failed nodes can be replaced efficiently. It has been tested with replay, fuzz, property-based, fault injection as well as multiple performance tests to ensure reliability. It tends to power critical deployments with enhanced performances and scalability in the cloud.
Qlik provides transparent raw data integration efficiently with automatically aligned data association. It helps Big Data analysts to detect the potential market trends by integrating embedded and predictive analysis. It supports a full range of real-time data analytics with the Associative Engine and a governed multi-cloud architecture. The Associative Engine ensures to deliver unlimited combinations of Big Data by indexing every relationship within the data. It helps to detect the in-depth insights for better workflow. QlikView consists of multiple attractive products for the global market— Qlik Replicate, Qlik Compose, Qlik Gold Client, Qlik Enterprise Manager, Qlik Catalog, and Qlik Gold Client for Data Protection.
Splunk aims to empower IT, DevOps, and other teams to transform their multiple sets of real-time data from any source at any time. This Big Data technology is providing service to a diverse range of industries— aerospace, education, manufacturing, healthcare, retail, and many more. It helps to transform the data into colorful reports, graphs, personalized dashboards, and other data visualization facilities.
ElasticSearch is also an open-source database server utilized for performing full-text search and real-time data analytics with HTTP web interface and Schema-free JSON documents. It is one of the best Big Data technologies due to its reliability and scalability with high speed. It also offers the analysts a smart platform that is highly optimized for language-based searches. It provides rapid results with the implementation of inverted indices for full-text querying, BKD trees, and a column store for real-time data analytics. The scalability can manage kajillions of events per second in a 300-node cluster.
KNIME or Konstanz Information Miner is another open-source real-time data analytics technology written in Java. It consists of several functionalities— data visualization, selective execution of analysis steps, detecting outcomes, interactive views as well as personalized data models. It also offers ETL operations with a broad spectrum of integrated tools that are easy to install in the existing computer systems.
RapidMiner is a top-notch Big Data platform proficient in delivering transformational business insights to various industries. It helps to upskill organizations with portability and extensibility. RapidMiner provides an integrated environment for data preparation, deep learning, text mining as well as predictive analytics. It is more popular among non-programmers and researchers due to its compatibility with Apple, Android, NodeJS, flask, and many more. It also provides its dataset collection and allows the user to load real-time data from Cloud, RDBMS, NoSQL, and so on.
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