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Big data analytics tools and technology

Big data analytics tools and technology


ig data analytics cannot be narrowed down to a single tool or technology. Instead, several types of tools work together to help you collect, process, cleanse, and analyze big data. Some of the major players in big data ecosystems are listed below.


Hadoop is an open-source framework that efficiently stores and processes big datasets on clusters of commodity hardware. This framework is free and can handle large amounts of structured and unstructured data, making it a valuable mainstay for any big data operation.


NoSQL databases are non-relational data management systems that do not require a fixed scheme, making them a great option for big, raw, unstructured data. NoSQL stands for “not only SQL,” and these databases can handle a variety of data models.


MapReduce is an essential component to the Hadoop framework serving two functions. The first is mapping, which filters data to various nodes within the cluster. The second is reducing, which organizes and reduces the results from each node to answer a query.


YARN stands for “Yet Another Resource Negotiator.” It is another component of second-generation Hadoop. The cluster management technology helps with job scheduling and resource management in the cluster.


Spark is an open source cluster computing framework that uses implicit data parallelism and fault tolerance to provide an interface for programming entire clusters. Spark can handle both batch and stream processing for fast computation.


Tableau is an end-to-end data analytics platform that allows you to prep, analyze, collaborate, and share your big data insights. Tableau excels in self-service visual analysis, allowing people to ask new questions of governed big data and easily share those insights across the organization.


The big benefits of big data analytics
The ability to analyze more data at a faster rate can provide big benefits to an organization, allowing it to more efficiently use data to answer important questions. Big data analytics is important because it lets organizations use colossal amounts of data in multiple formats from multiple sources to identify opportunities and risks, helping organizations move quickly and improve their bottom lines.



Some benefits of big data analytics include:



Cost savings. Helping organizations identify ways to do business more efficiently

Product development. Providing a better understanding of customer needs

Market insights. Tracking purchase behavior and market trends



The big challenges of big data
Big data brings big benefits, but it also brings big challenges such new privacy and security concerns, accessibility for business users, and choosing the right solutions for your business needs. To capitalize on incoming data, organizations will have to address the following:



Making big data accessible. Collecting and processing data becomes more difficult as the amount of data grows. Organizations must make data easy and convenient for data owners of all skill levels to use.


Maintaining quality data. With so much data to maintain, organizations are spending more time than ever before scrubbing for duplicates, errors, absences, conflicts, and inconsistencies.


Keeping data secure. As the amount of data grows, so do privacy and security concerns. Organizations will need to strive for compliance and put tight data processes in place before they take advantage of big data.


Finding the right tools and platforms. New technologies for processing and analyzing big data are developed all the time. Organizations must find the right technology to work within their established ecosystems and address their particular needs. Often, the right solution is also a flexible solution that can accommodate future infrastructure changes.



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