Top Big Data Analytics Tools for 2022 that You Must Enhance

These data analytics tools are best for data mining and to leverage for sure

Big data analytics tools

Big data analytics tools

Data analytics tools enable businesses to analyze vast stores of data to add competitive advantage. The plus point for having these kinds of tools is to mine data that tracks a diverse array of business activity from present sales to the historic inventory and the process. If you are wondering which big data analytics tools to choose. Then here you go! Here are the top data analytics tools for 2022. 



Tableau is one of the top data analytics tools which was acquired by Salesforce in 2019. It has built a large and enthusiastic user base due to the quality and depth of its data visualization. The data analytics platform is well known for allowing users to combine the collected inputs and offering a dashboard display to enhance the visual data mining. 



It is driven by Azure Cloud, the company’s Power BI platform adds to this strength. The Power BI tool is one of the top data analytics tools that can justifiably be the most popular in the market these days. This becomes important as the large user base prompts constant product upgrades from which Power BI can benefit. 



If you are keen to use AI or ML to improve the quality of data mining then possibly considering Qlik is the best option. It offers a vision compelling in the data analytics field. And the other interesting part is that it has advanced versions of AI and ML built into its Qlik Sense platform. It is the top data analytics tools that can help in deploying analytics to any cloud using their multi-cloud infrastructure. 



ThoughtSpot is also one of the well known big data analytics tools that offers next-generation search first tool that is popular in the market. The best part is that it offers any number of compelling features when it comes to AI-based recommendation systems that can leverage crowdsourcing too. ThoughtSpot’s calling card in a crowded market is its search-based query interface too. The platform uses augmented analytics to obtain the insights. 



It is envisioned as a foundation of enterprise analytics by connecting diverse competing platforms into a single unified system. In a competitive data world the platform seeks to join unified systems together. Users can leverage the platform to update a vast array of data sources from the internet and mobile. 



This platform is best for power users but not for the untrained staff. It is truly committed to the data analytics platform and is of great help when it comes to scalability. It can support the cloud-native applications and enables faster speed. And also supports handling difficult enterprise analytics workloads too. 



TIBCO is a ML-augmented data analytics platform that works for enterprise data scientists or for the less trained staff. It is a well-developed, feature-rich data analytics software platform. It also involves a large menu of drag and drop analytic functions to speed up the data mining. 



SAS offers a fully mature program that quenches the demanding queries of data scientists that is accessible to lesser-trained staff. It has upgraded its augmented analytics tools and is now the key demand of analytics customers.  The platform is keeping with the times that leverages microservices and the cloud for greater scalability and is more flexible performance.



IBM is an enterprise platform that is integration among data products that combines both enterprise-level managed and self-driven query work with augmented analytics and reporting. The platform is capable of generating natural language processing and natural language generation. The interoperability between other elements of the IBM data portfolio is well regarded. 



SAP is one of the data analytics tools that integrates a wide array of analytics functionality into a single cohesive solution. It is an API menu that enables connections with embedded solutions and is keeping with today’s emerging technologies.


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