Year after year, businesses of all sizes generate more and more data, and they’re discovering new ways to utilize it to enhance operations, better understand consumers, and provide goods faster and at cheaper prices, among other things. There are various commercial tools available to assist businesses to implement a wide spectrum of data-driven analytics initiatives, from real-time reporting to machine learning applications, using big data technology. Here are 15 widely used open-source tools and technologies for managing and analyzing big data. 


Cassandra is an open-source NoSQL database that converts large amounts of real-time data into detailed analysis. It has proved fault tolerance and linear scalability on commodity hardware and cloud infrastructure. Cassandra guarantees that no data is lost as failing nodes are effectively replaced. To assure reliability, it has been subjected to replay, fuzz, property-based, fault injection, and numerous performance tests. It often powers essential cloud projects with improved performance and scalability. 


Dataddo is a no-code, cloud-based ETL platform that prioritizes flexibility. With a wide set of connectors and the option to customize metrics and properties, Dataddo makes building solid data pipelines simple and quick. Dataddo integrates smoothly with your existing data stack, so you won’t have to add any new components to your architecture or modify your core procedures. Instead of wasting time learning how to utilize yet another platform, Dataddo’s straightforward UI and rapid setup allow you to focus on integrating your data. 


ElasticSearch is yet another open-source database server that is used for full-text search and real-time data analytics through an HTTP web interface using Schema-free JSON documents. Because of its dependability and scalability with fast speed, it is one of the greatest Big Data technologies. It also provides analysts with an intelligent platform that is well-tuned for language-based queries. It delivers fast results by utilizing inverted indices for full-text searching, BKD trees, and column storage for real-time data analytics. In a 300-node cluster, the scalability can handle billions of events per second. 


KNIME, or Konstanz Information Miner, is another Java-based open-source real-time data analytics tool. It has various features, including data visualization, selective execution of analytical processes, identifying outcomes, interactive views, and customizable data models. It also provides ETL operations with a wide range of integrated tools that are simple to install in existing computer systems. 


Hadoop is one of the finest open-source software that allows for multiple sets of real-time for processing over several clusters of computers using simple programming models. By identifying any failure at the application layer, it aids scalability from single servers to thousands of machines. There are now five modules available: Hadoop Common, Hadoop Ozone, Hadoop Distributed File System, Hadoop MapReduce, and Hadoop YARN. The frameworks are developed in Java and can process real-time data of any size and format. It is cost-effective and efficient, even in the face of enormous challenges such as cyberattacks or machine failures. 


Kafka is an open-source platform for distributed event processing and streaming that is well-known for its ability to provide systems with high throughput. Every day, it can process billions of events. It’s also extremely scalable and fault-tolerant when used as a streaming platform. Its streaming method involves equally broadcasting and subscribing to streams of records to several messaging systems, as well as storing and processing the records. Kafka’s zero-downtime promise is another plus. 


MongoDB is an open-source NoSQL database for big data analytics that is cross-platform compatible. These characteristics make it ideal for firms that rely on real-time analytics and fast-moving data for decision-making. It’s also perfect for those looking to implement data-driven solutions. It can be a wonderful starting point for an organization’s digital transformation journey since it is a user-friendly, cost-effective, and dependable solution with simple installation and maintenance. It is written in JavaScript, C, and C++ and allows for the efficient administration of unstructured or dynamic data.  


Qlik effectively delivers transparent raw data integration with automatically aligned data association. By combining embedded and predictive analysis, it assists Big Data analysts in detecting prospective market trends. With the Associative Engine and a regulated multi-cloud architecture, it provides a full spectrum of real-time data analytics. By indexing every relationship within the data, the Associative Engine assures that there are no limits to the number of Big Data combinations that can be delivered. It aids in detecting in-depth insights for improved productivity. 

R Programming 

R is another Big Data technology that is employed in the programming language for statistical computing and graphics. This programming software provides Big Data engineers, statisticians, and others with a wide range of features, including linear modeling, non-linear modeling, traditional statistical tests, time-series analysis, clustering, and graphical techniques. It is a well-designed platform with a wide range of mathematical symbols and equations available. It enables successful data management by providing a big coherent and integrated set of useful tools for real-time data analyses.  


RapidMiner is a leading Big Data platform capable of providing transformative business insights to a wide range of businesses. Because of its mobility and adaptability, it aids in the upskilling of businesses. RapidMiner is a comprehensive data management, deep learning, text mining, and predictive analytics platform. Because of its interoperability with Apple, Android, NodeJS, flask, and other programming languages, it is increasingly popular among non-programmers and academics. It also has a dataset collection and enables users to import real-time data from Cloud, RDBMS, NoSQL, and other sources. 


Splunk’s strives to enable IT, DevOps, and other teams to transform various sets of real-time data from any source at any time. This Big Data technology is used in a wide range of industries, including aerospace, education, manufacturing, healthcare, retail, and many more. It aids in the transformation of data into vivid reports, graphs, customized dashboards, and other data visualization tools. 


Storm is a distributed real-time computing system that can handle unlimited data streams reliably. It can be used for real-time analytics, online machine learning, continuous computation, and extract, transform, and load (ETL) activities, according to the project’s website. Storm clusters are similar to Hadoop clusters, except that applications run indefinitely unless they are stopped. The system is fault-tolerant, ensuring data processing. 


Tableau is a powerful Big Data solution that can connect to a variety of open-source databases. The server even offers a free public option for developing proper graphics. This analytics platform has a variety of appealing characteristics such as sharing choices with anybody, moderate speed to improve extensive operation, integration with over 250 apps, and, most significantly, aids in the resolution of large real-time data analytics challenges. It is one of the most powerful, secure, and adaptable end-to-end real-time data analytics solutions on the market. It creates several Tableau product lines, including Tableau Prep, Tableau Desktop, Tableau Server, Tableau Online, and Tableau Mobile. 

These days, there are a plethora of solutions available in the market supporting big data operations. Each of these big data solutions has its own set of advantages when it comes to storing massive amounts of data, processing it quickly, and delivering analytics that may help your business expand in new directions. However, one factor influences these outcomes: choosing the appropriate tools suiting your needs, resources, and goals. 

Published On: December 17th, 2021 / By / Categories: Uncategorized /