Today Big Data is the hottest buzzword around. With the amount of data generated every minute by businesses or consumers worldwide, there is a considerable value to be found in big data analytics. Globally, enterprises realize the importance of big data analytics and harnessing it to reshape their business models. Choosing the right big data analytics tool is always challenging as no tool fits every need. This article discusses the best Big data analytics tools and their key features based on their popularity and performance.
First, let’s start by defining what big data analytics is.
Big data analytics is a process used to extract meaningful insights from raw data like hidden patterns, unknown relations, customer preferences, and market trends. It practices advanced analytic techniques against large, diverse data sets from different sources and sizes from terabytes to zettabytes.
Analysis of big data allows you to make better and faster business decisions, preventing fraudulent activities, and much more. Big Data analytics is fueling everything we do online right now in every industry.
Big data is analyzed by collecting structured, semi-structured, and unstructured data from data lakes and parsing out the most relevant to your current informational need.
After that, leverage machine learning and statistics to parse through the data ecosystem and compile user behaviour analytics, predictive analytics, and other metrics. This process also includes natural language processing, text analytics, and so forth.
[Related Article: Bigdata vs Data Science vs Data Analytics]
Below listed are the best big data analytics tools -
Xplenty is one of the most advanced data pipeline platforms available today. This cloud-based ETL platform helps you organize and prepare the data for advanced business intelligence, customer data, and analytical purposes. Xplenty has built its reputation as a leader in ETL innovation since its launch in 2012. From startups to fortune500 companies, everyone uses Xplenty for a wide variety of purposes.
Xplenty is a complete toolkit to build data pipelines with no code and low code capabilities. Its solutions are valuable for marketing, sales, support, and IT departments. It inputs data from more than 120 data stores like Salesforce, Facebook, Google, and more to integrate them into Amazon Redshift, MySQL, PostgreSQL, etc.
Below listed are the most impressive features of Xplenty:
1. Data Integration:
Xplenty allows you to integrate your data solution stack with ease through no-code and low-code options. Additionally, the API component supports the advanced customizations needed. Even you can connect the Xplenty to the monitoring system using service hooks.
2. Flexible and scalable cloud platform:
Xplenty handles monitoring, deployments, scheduling, maintenance, and security while you remain focused on data. By taking advantage of Xplenty’s flexible and scalable platform, you can manage replication tasks and complex tasks.
3. Excellent customer support: Sometimes, data integration can be tricky because of complex file formats, connectivity issues, and more. Xplenty provides you with excellent customer-centric support to tackle challenges via telephone, email, chat messaging, and online meetings.
Data security and compliance: Keeping information secure is the most crucial aspect that Xplenty does. It features advanced security technology that adheres to the strictest compliance standards of your industry. Whether your vertical is financial services, insurance, accounting, medical care, or retail, Xplenty has you covered with the features like SSL/TLS encryption, Constant verification, Accredited compliance, Firewall-based access control, and much more.
The pros and cons of using Xplenty are listed below:
Pricing: Xplenty includes both free and paid plans. The free trial lasts for 14 days, and paid plans include both standard and enterprise editions.
Tableau is a simple and intuitive visual analytic platform built with best practices for data exploration and analysis. Users can access the full suite with a minimal learning curve by leveraging easy point-and-click AI-driven statistical modeling and drag-and-drop visualizations. It’s a bit steeper to learn this platform, but it’s well worth it once you do I
Tableau has been around since the initial days of big data analytics and continues to grow in the industry with its compelling features. No matter the size, it handles any amount of data. Best of all, Tableau is interactive and can work on any device and share data through shared dashboards.
The pros and cons of using Tableau are listed below:
Pricing: Tableau costs $70/user/month and offers a free 14-day trial.
Zoho Analytics is a self-service Business Intelligence and data analytics platform that allows you to analyze data visually, build stunning data visualizations, and uncover hidden insights in minutes. It is trusted by great brands like Hyundai, Suzuki, IKEA, HP, and more. This tool comes with many pre-built visualizations distributed by function (social media, finance, IT, sales) to help you get started quickly.
It supports more than 500 out-of-the-box integrations for your businesses and transforms huge raw data sets into actionable reports and dashboards. It also helps track key business metrics, check longtime trends, identify outliers, and unearth hidden insights.
The pros and cons of using Zoho Analytics are listed below:
Pricing: Zoho Analytics offers a free 15-day trial and provides premium plans based on the users and rows.
Splunk, trusted by 91 of the fortune 500 companies, including Coca-Cola, Intel, and more. It supports machine learning-centric visibility, risk behavior detection, anomaly observation, detection of entity profiling, scoring, and high fidelity behavior-based alerts.
It handles any data type and draws meaningful insights from it. Splunk supports dedicated solutions to DevOps, IT, Security, and Big data. A single-user license works for 1,000 to 4,999, and it can be deployed on-premises, through mobile devices, or even through the cloud!
Automatically notices if any of the data patterns are abnormal and let you know with the real-time monitoring feature.
The pros and cons of using Splunk are listed below:
Pricing: Splunk offers a free plan that allows you to index only 500 MB/day and cost from $2000/year for 1 GB/day.
SAS Visual Analytics is a powerful big data analytic tool that allows you to discover and share insights that inspire action. With this tool, you can import data from databases, Social media, Hadoop, and excel spreadsheets. It supports a wide variety of interactive visualizations and makes data analysis even more comfortable. Predictive models can help with the estimation of risks and forecasting for various marketing campaigns.
This tool is a go-to option for a user looking for an easy user interface and doesn’t mind paying for the convenience.
The pros and cons of using Sas Visual Analytics are listed below:
Pricing: SAS Visual Analytics offers a free 14-day trial and starts from $8000/year.
Talend is a big data analytics tool that automates big data integration. It is the only tool that unites data integration and governance to deliver trusted data at your fingertips.
By generating native code, Talend simplifies using MapReduce and Spark. Optimizes the IT budget to take full advantage of the latest data sources, analytics technologies, and elastic capacity when you need it.
It also provides first-in-class support via email, phone, and web support to licensed users for great customer experiences.
The pros and cons of using Talend are listed below:
Pricing: Talend open Source is entirely free for all users. The rest of the suite’s products offer subscription-based costs that are flexible based on your specific needs.
Elasticsearch is an open-source big data analytics tool. Its distributed, RESTful search and analytics engine is used for solving many use cases. One of the big data analysis tools comes with maximum reliability, scalability, and management. It comes as an integrated solution with Logstash and Kibana.
From startups to the global 2000, Elasticsearch provides solutions to leading companies to find documents, protect against security threats, monitor infrastructure, and more.
The pros and cons of using Elasticsearch are listed below:
Pricing: Elasticsearch provides paid plans for standard, gold, platinum, and enterprise-level projects. Pricing varies from plan to plan, depending on the requirement.
KNIME stands for Konstanz Information Miner, which is an open-source big data analytics tool. It is utilized for Enterprise reporting, integration, CRM, research, data analytics, data mining, and business intelligence purposes. Also, it allows users to manipulate, analyze, and model data through visual programming.
The pros and cons of using KNIME are listed below:
Pricing: Knime platform is free. However, they offer other commercial products that extend the capabilities of the Knime analytics platform.
IBM SPSS Modeler is a predictive big data analytics platform. SPSS is proprietary software that is used for data mining and predictive analytics. It is one of the most widely used big data analysis tools, with out-of-box advanced algorithms and analysis techniques, suited for multi-cloud and hybrid environments with security posture and robust governance.
This tool provides a drag-and-drag interface to do everything from data exploration to machine learning. Leading organizations globally depend on this tool for data discovery, predictive analytics, model management, deployment, and machine learning to monetize data assets.
The pros and cons of using IBM SPSS Modeler are listed below:
Pricing: IBM SPSS Modeler provides premium plans starting from $4,950 to $12,300. Plans include IBM SPSS Modeler Personal, Professional, and Premium.
Lumify is an open-source tool for big data fusion, analysis, and visualization. It is one of the best big data analysis tools that help users to explore connections and relationships in their data via a suite of analytic options.
The pros and cons of using Lumify are listed below:
Pricing: This tool is free to use.
With this article, we came to know the various tools available to support Big data operations these days. Some of them on the list are open-source tools, and others were paid tools.
We hope the information shared here has thrown some light on your knowledge of Big Data Analytics tools.
Before considering the tool for your project needs, always explore the trial version before and connect with the existing customers to know the reviews.
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Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .
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