Popular Big Data Analytics Tools and Techniques

by TechFunnel Contributors - March 20, 2020
TechFunnel Contributors

TechFunnel Contributors | TechFunnel.com is an ambitious publication dedicated to the evolving landscape of marketing and technology in business and in life. We are dedicated to sharing unbiased information, research, and expert commentary that helps executives and professionals stay on top of the rapidly evolving marketplace, leverage technology for productivity, and ad...

Big Data Analytics Tools

The term “big data analytics tools”or “big data analytics software” is widely used to refer to the provision of a meaningful analysis of a large set of data. This software is useful in finding current market trends, customer preferences, and other information.

Below is a combination of 22 top big data analytics tools or open source analytics tools of 2020

  1. Skytree

    It is a big data analytics tool having highly scalable algorithms that helps data scientists to build more accurate models faster. It allows data scientists to visualize and understand the logic behind ML decisions. It has also been designed to solve robust predictive problems with data preparation capabilities.

  1. Lumify

    It is a big data fusion built on proven and scalable big data technologies(1) with analysis, and visualization platform that comes in 2D and 3D. It also helps users to discover connections and explore relationships in their data via a suite of analytic options.

    It also comes with specific ingest processing and interface elements for textual content, images, and videos and Its space feature allows you to organize work into a set of projects, or workspaces.

  1. Apache Spark

    Is a very powerful open source big data analytics tool. It has been designed to offer over 80 high-level operators that make it easy to build parallel apps. It is widely used in large organizations to process large datasets. It can help you to run an application in Hadoop cluster, up to 100 times faster in memory, and ten times faster on disk with its lighting Fast Processing that supports Sophisticated Analytics.

  1. Microsoft HDInsight

    Is a Spark and Hadoop service in cloud that provides big data cloud offerings in two categories, Standard and Premium. It provides a huge enterprise-scale cluster for the organization to run their big data workloads. It has reliable analytics with an industry-leading SLA, and it offers enterprise-grade security and monitoring.

    It Protect data assets and extend on-premises security and governance controls to the cloud with a High-productivity platform for developers and scientists and anyone that can handle its operation.

( Also Read: 3 Major Impacts of Big Data Analytics in Marketing Organizations )

  1. Xplenty

    Is a cloud based ETL solution that provides simple visualized data pipelines for automated data flows across a wide range of sources and destinations. Xplenty’s highly powerful on-platform transformation tools allow you to clean, normalize, and transform data while also adhering to compliance best practices.

    It is a powerful, code-free, on-platform data transformation offering that has rest API connector with the ability to pull in data from any source that has a Rest API.  It has destination flexibility which means you can send data to databases, data warehouses, and Salesforce.

    It is also an open source platform. Lastly and most importantly, it is a customer-centric company that leads with first-class support.

  1. IBM SPSS Modeler

    Is a very predictive big /open source data analytics platform. It gives predictive models and delivers to individuals, groups, systems and the enterprise. It has a wide range of advanced algorithms and analysis techniques that you can use to discover insights and solve problems faster by analyzing structured and unstructured data.

    It also uses an intuitive interface for everyone to learn easily. You have a choice to select from on-premises, cloud and hybrid deployment options so it is best you Quickly choose the best performing algorithm based on model performance.

  1. R-Programming

    Is the main language for statistical computing and graphics. It is also used for big data analysis as It provides a wide variety of statistical tests. With its Effective data handling and storage facility, it provides you with a suite of operators for calculations on arrays, in particular, matrices and also coherent, integrated collection of big data tools for data analysis.

    It is equipped with graphical facilities for data analysis which display either on-screen or on hardcopy.

  1. Elasticsearch

    It refers to a JSON-based Big data search and analytics engine. It is a properly distributed, restful search and analytics engine for solving numbers of use cases. It provides  horizontal scalability, maximum reliability, and easy management.

    It allows you to combine many types of searches such as structured, unstructured, geo, metric, and intuitive APIs for monitoring and management giving you complete visibility and control. It also uses standard restful APIs and JSON. It builds and maintains clients in many languages like Java, Python, NET, and Groovy.

    Its Real-time search and analytics feature to work big data by using the Elasticsearch-Hadoop. Finally, it gives an enhanced experience with security, monitoring, reporting, and machine learning features.

  1. PlotlyIt

    It is an analytics tool that lets its users create charts and dashboards to share online. It can easily turn any data into eye-catching and informative graphics and It also provides audited industries with fine-grained information on data provenance . Lastly, it offers unlimited public file hosting through its free community plan.

  1. Splice Machine

    It is an open source big data analytics tool. Their architecture has been designed to be portable across public clouds such as AWS, Azure and Google. It has the capacity to dynamically scale from a few to thousands of nodes to enable applications at every scale.

    The Splice Machine optimizer can  automatically evaluate every query to the distributed HBase regions, reduce management, deploy faster, and reduce risk. It can consume fast streaming data, develop, test and deploy machine learning models.

  1. Talend

    It is a big open source data analytical  tool that simplifies and automates big data integration. Its graphical wizard can generate native code using MapReduce and Spark. It  allows big data integration, master data management and checks data quality as fast as possible by accelerating time to value for big data projects.

  1. OpenRefine

    Is a powerful tool for working with messy data: cleaning it, transforming it from one format into another, and extending it with web services and external data. OpenRefine can help you explore large data sets with ease.

  1. Orange

    is a open source data visualization and data analysis for novice and expert, it provides interactive workflows with a large toolbox to create interactive workflows to analyse and visualize data. Orange is packed with different visualizations, from scatter plots, bar charts, trees, to dendrograms, networks and heat maps.

( Also Read: Big Data Analytics and The Future of Marketing & Sales)

  1. Pentaho

    It is a tool that addresses the barriers that block your organization’s ability to get value from all your data. The platform simplifies preparing and blending any data and includes a spectrum of tools to easily analyze, visualize, explore, report and predict.

    Open, embeddable and extensible, Pentaho is architected to ensure that each member of your team — from developers to business users — can easily translate data into value.

  1. Weka

    It is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a data set or called from your own JAVA code. It is also well suited for developing new machine learning schemes, since it was fully implemented in the JAVA programming language, plus supporting several standard data mining tasks.

  1. NodeXL

    It is a data visualization and analysis software of relationships and networks. NodeXL provides exact calculations. It is a free and open-source network analysis and visualization software. It is one of the best statistical tools for data analysis which includes advanced network metrics, access to social media network data importers, and automation.

  1. Gephi

    It is also an open-source network analysis and visualization software package written in Java on the NetBeans platform. Think of the giant friendship maps you see that represent linkedin or Facebook connections.

  1. Solver

    Solver purposely specializes in providing world-class financial reporting, budgeting and analysis with push-button access to all data sources that drive company-wide profitability. Solver provides BI360, which is available for cloud and on-premise deployment, focusing on four key analytics areas.

  1. Qlik

    Allows you create visualizations, dashboards, and apps that answer your company’s most important questions. Now you can see the whole story that lives within your data.

  1. Tableau

    It democratizes visualization in an elegantly simple and intuitive tool. It is exceptionally powerful in business because it communicates insights through data visualization. In the analytics process, Tableau’s visuals allow you to quickly investigate a hypothesis, sanity check your gut, and just go explore the data before embarking on a treacherous statistical journey.

  1. Semantria

    Semantria is a tool that offers a unique service approach by gathering texts, tweets, and other comments from clients and analyzing them meticulously to derive actionable and highly valuable insights.

  1. Trackur

    Trackur’s automated sentiment analysis looks at the specific keyword you are monitoring and then determines if the sentiment towards that keyword is positive, negative or neutral with the document.

What should you look for when selecting Big Data Analytics tools for your business?

  • Analytic Capabilities

    There are several types of analytics capabilities with different models for various types of analysis including:  predictive mining, decision trees, time series, neural networks, path analysis, market basket analysis, and link analysis.

  • Integration

    Most Often, additional statistical tools and programming languages (such as R) are needed by organization to conduct other forms of custom analysis.

  • Data Import and Export

    Inputting data in and out of various tools is a critical feature and understanding how difficult or easy as the case may be to connect the analytics tool to the big data repository is a key consideration.

  • Visualization

    Seeing the numbers is a thing, but having data displayed in a graphical format, often makes the data more useable.

  • Scalability

    Big Data can be big to start with, and generally has a tendency to grow even bigger over time. Organizations need to consider and understand the scalability options for the analytics tools they choose.

  • Collaboration

    Analysis, sometimes is a solitary exercise, but more often than not it involves collaboration.

Final Thoughts

These are the most popular big data analysis tools used for marketing. Any marketer who really wants to thrive in this 20th century should employ its use.

TechFunnel Contributors

TechFunnel Contributors | TechFunnel.com is an ambitious publication dedicated to the evolving landscape of marketing and technology in business and in life. We are dedicate...

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