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.

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First, let’s start by defining what big data analytics is.

What is Big Data Analytics

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.

How is Big Data Analyzed?

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 the machine learning and statistics to parse through the data ecosystem and compile user behavior 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]

Big Data Analytics Tools list

Below listed are the best big data analytics tools -

  • Xplenty
  • Tableau 
  • Zoho Analytics
  • Splunk
  • SAS Visual Analytics
  • Talend
  • ElasticSearch
  • KNIME
  • IBM SPSS Modeler
  • Lumify

#1. Xplenty

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:

Pros Cons
  1. Enables developers to customize data solution stack using API.
  2. Test functions using “Xconsole” so that developers can review and launch the data processes immediately.
  3. For scheduling and monitoring jobs, it supports the point-and-click interface.
  4. Xplenty supports integration with lots of third-party tools for logging, visualization, and data analytics purposes.
  5. Xplenty supports out-of-the-box data transformations to integrate data from various sources into a single pipeline.
  1. Challenging to adopt, Xplenty requires lots of Big data processing knowledge to use it.
 

Pricing:  Xplenty includes both free and paid plans. The free trial lasts for 14 days, and paid plans include both standard and enterprise editions.

#2. Tableau

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.

Features:

  • Tableau offers various data source options for users to connect and fetch data.
  • Tableau provides suitable options to collaborate and share data with others in the form of visualizations, dashboards, sheets, etc., in real-time. 
  • Another handy Tableau feature is the use of time series and forecasting.
  • Tableau supports advanced visualizations.

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The pros and cons of using Tableau are listed below:

Pros Cons
  1. Tableau is exceptionally flexible for creating custom visualization reports.
  2. Extremely active user base with out of the box support
  3. A powerful engine that can blend various data points
  4. Quickly creates interactive visualizations.
  5. Ease of implementation.
  1. Tableau has no inbuilt tool to deploy and migrate among other servers.
  2. Requires some data manipulation to match queries successfully.
  3. Reauthentication is often needed for saved database connections.
 

Pricing: Tableau costs $70/user/month and offers a free 14-day trial.

#3. Zoho Analytics

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.

Features:

  • Zoho Analytics allows you to connect to any data source.
  • You can carry out in-depth analytics, uncover hidden insights, and derive vital metrics using powerful analytical capabilities.
  • Develop and share reports with the team for informed business decisions.
  • Powerful and extensible Zoho Analytics allows you to build and integrate reporting and analytical capabilities into your business applications. 

The pros and cons of using Zoho Analytics are listed below:

Pros Cons
  1. Zoho Analytics allows you to create and share powerful, ad hoc reports quickly without coding knowledge. 
  2. Import data from applications and databases, even behind firewalls.
  3. It offers comprehensive gamification models.
  4. Up to 10 users are allowed in the free edition.
  5. Excellent embedded AI feature
  1. For the professional edition, workflow automation is limited.
  2. Dashboards seem a bit cramped and busy.
  3. Hourly data sync is not supported in the entry-level plan.

Pricing: Zoho Analytics offers a free 15-day trial and provides premium plans based on the users and rows.

#4. Splunk

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.

Features:

  • With Splunk, you can accelerate cloud-driven transformation.
  • Manage both today’s hybrid cloud and multi-cloud environment easily.
  • Splunk optimizes cyber defenses with industry-leading data, analytics, and security operations solutions. 

The pros and cons of using Splunk are listed below:

Pros Cons
  1. Flexible data and report sharing 
  2. Adaptable for any organizational needs
  3. Real-time monitoring of all data
  4. Visual reporting and analysis
  5. Customizable user interfaces
  6. Search queries can be saved for repeated use or converted into apps. 
  7. Quick log queries across various types of infrastructure
  8. Can set up detailed and specific alerts for different KPIs
  1. Infrastructure maintenance requires more workforce. 
  2. The query builder may be prohibitive for non-technical users. 
  3. Steep learning curve compared to competitor tools

Pricing: Splunk offers a free plan that allows you to index only 500 MB/day and cost from $2000/year for 1 GB/day.  

#5. SAS Visual Analytics

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.

Features:

  • SAS Visual Analytics provides interactive dashboards, reports, BI, and analytics.
  • With this tool, users can present data with advanced Visualization techniques and guided analysis through auto charting. 
  • Supports flexible deployment

The pros and cons of using Sas Visual Analytics are listed below:

Pros Cons
  1. The flexible drag-and-drop analytics feature
  2. Extremely fast, robust, and scalable
  3. Well-suited to support a large number of users simultaneously
  4. Works with a vast number of records without lagging 
  5. Customizable portfolio dashboards for each line of business
  6. Can access quality BI dashboards from many devices
  7. Easy to use drag and drop features
  1. Has a low number of connection options with third-party apps 
  2. Less compatible with external sources
  3. Price prohibitive compared to other tools on this list. 

Pricing: SAS Visual Analytics offers a free 14-day trial and starts from $8000/year.

#6. Talend

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.

Features:

  • Below listed are the intriguing features of Talend -
  • Connects more than 900 different databases, files, and applications as sources or targets for the integration tasks
  • Offers support to the complicated process workflows and the extensive data integration transformations
  • Offers integration project support with release management, team-based collaboration, and a tool-based generation system

The pros and cons of using Talend are listed below:

Pros Cons
  1. Capable of handling multiple data sources
  2. Offers custom solutions 
  3. Streamlines ELT and ETL for Big data usage
  4. Handles data quality, scalability, and management.
  5. Generates optimized code
  6. Includes more than 900 drag-n-drop components
  7. Manages deployments with ease
  8. Can convert and update data from multiple sources
  1. Customization is a challenging task.

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. 

#7. Elasticsearch

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.

Instead of the typical full-text search setup, it offers ways to extend searching capabilities through query DSLs and APIs. Also, used with numerous programming languages, such as PHP, Ruby, JavaScript, etc.

From startups to the global 2000, Elasticsearch provides solutions to leading companies to find documents, protect against security threats, monitor infrastructure, and more.

Features

  • Elasticsearch allows connecting various types of searches such as geo, metric, structured, unstructured, etc.
  • Intuitive APIs for management and monitoring gives complete visibility and control.
  • It uses standard RESTful APIs and JSON. 
  • Builds and handles clients in many languages like Java, Python, NET, and Groovy.
  • Provides an enhanced experience with security, reporting, monitoring, and machine learning features

The pros and cons of using Elasticsearch are listed below:

Pros Cons
  1. It comes with a lot of search options.
  2. Can separate indices based on the logs or metrics category.
  3. ElasticSearch is API-driven. 
  4. It has a powerful JSON-based DSL, which allows teams to build complicated queries and fine-tune them to obtain the most precise results from a search.
  1. Sometimes, document storage and upload can become prolonged.

Pricing: Elasticsearch provides paid plans for standard, gold, platinum, and enterprise-level projects. Pricing varies from plan to plan, depending on the requirement.

#8. KNIME

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.

Features:

  • Access, merge, and transform all your data quickly with KNIME.
  • Intuitive user interface.
  • Parallel execution on multi-core systems
  • Support enterprise-wide data science practices

The pros and cons of using KNIME are listed below:

Pros Cons
  1. Simple ETL operations
  2. KNIME integrates well with other programming languages.
  3. Rich algorithm set.
  4. Easy drop and drag connections
  5. Highly usable and organized workflows.
  6. Easy to set up and no stability issues
  1. Poor data visualization

Pricing: Knime platform is free. However, they offer other commercial products that extend the capabilities of the Knime analytics platform.

#9. IBM SPSS Modeler

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.

Features:

  • IBM SPSS Modeler can read data from flat files, relational databases, spreadsheets, Planning Analytics, and Hadoop.
  • From Scikit-learn and Tensorflow, it provides easy model deployment.
  • SPSS Modeler automatically changes data into the best format for the most accurate predictive modeling.
  • SPSS Modeler offers multiple machine learning techniques.
  • SPSS Modeler enables the use of R, Spark, Python, and Hadoop to amplify the power of analytics.

The pros and cons of using IBM SPSS Modeler are listed below:

Pros Cons
  1. IBM SPSS Modeler doesn’t require programming or coding knowledge.
  2. The premium version comes with all the latest options in Data mining.
  3. The graphical interface makes modeling easy.
  1. The primary disadvantage of this feature is that it hides the internal functionality of the program executed.

Pricing: IBM SPSS Modeler provides premium plans starting from $4,950 to $12,300. Plans include IBM SPSS Modeler Personal, Professional, and Premium.

#10. Lumify

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.

Features:

  • Its primary features include full-text search, 2D and 3D graph visualizations, and automatic layouts.
  • It provides broad options to analyze the links between graph entities.
  • It comes with specific ingest processing and interface elements for images, textual content, and videos.
  • It’s built on proven, scalable big data technologies.

The pros and cons of using Lumify are listed below:

Pros Cons
  1. Easily Scalable
  2. Very Secured platform.
  3. Compatible with cloud-based options, including Amazon’s AWS.
  1. Lumify provides a low analysis of data compared to other tools.

Pricing: This tool is free to use.

Conclusion:

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.