See the 10 Data Analytic Tools with uses and limitations, which can help to analyze the Big data
Big Data is today, the hottest buzzword around, and with the amount of data being generated every minute by consumers, or/and businesses worldwide, there is huge value to be found in Big Data analytics. Data analytics tools in Big data are used to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, and customer preferences. Data analytics is the process of analyzing data sets to draw results, based on the information they get. The data analytics tools help in finding current market trends, customer preferences, and other information. Here is the list of top 10 data analytics tools to make the best out of Big data.
Integrate.io: It is a Data Warehouse Integration Platform designed for e-commerce. And it helps e-commerce companies build a customer 360 view, generating a single source of truth for data-driven decisions, improving customer insights through better operational insights, and increasing ROI.
R-Programming: This tool helps data scientists to create statistics engines that can provide better and more precise insights due to relevant and accurate data collection. It is broadly used by statisticians and data miners. Its use cases include data analysis, data manipulation, calculation, and graphical display.
Apache Hadoop: This tool is a java based free software framework. It helps in the effective storage of huge amounts of data in a storage place known as a cluster. This framework runs in parallel on a cluster and also can process huge data across all nodes.
MongoDB: This tool is a document-oriented NoSQL database used to store high volumes of data. It makes use of collections and documents rather than using rows and columns. These documents consist of key-value pairs which are considered the basic unit of data in MongoDB.
Tableau: This tool is a software solution for business intelligence and analytics which present a variety of integrated products that aid the world’s largest organizations in visualizing and understanding their data. It is an extremely powerful tool.
RapidMiner: This tool operates using visual programming, and also it is much capable of manipulating, analyzing, and modeling the data. It makes data science teams easier and more productive by using an open-source platform for all their jobs like machine learning, data prep, and model deployment.
Apache SAMOA: SAMOA stands for Scalable Advanced Massive Online Analysis. It is an open-source platform for big data stream mining and machine learning. This tool enables the development of new ML algorithms. It provides a collection of distributed algorithms for common data mining and machine learning tasks.
Zoho Analytics: This tool is a self-service BI and analytics software used by the likes of Hyundai, Ikea, HP, and Philips. It allows its users to integrate multiple data sources that include business applications, databases, cloud drives, and more. It helps users generate dynamic, highly customizable, and actionable reports.
Splunk: This tool offers machine learning-centric visibility and detection of entity profiling and scoring, risk behavior detection, anomaly observation, and high fidelity behavior-based alerts. It can handle small, midsized, and large business enterprise data as well as public administrations and nonprofits.
Splice Machine: This tool can dynamically scale from a few to thousands of nodes to enable applications at every scale. It offers low latency row-based storage. This analytical computation maintains ACID properties with a special integration to our underlying row-based storage.
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