That\u2019s not all, it is growing at the rate of 40% every year. Data is being generated not only by people, but also software and machines. We\u2019re already talking in terabytes (TB) and petabytes (PB) today, but some experts<\/a> estimate that 463 exabytes (EB) of data will be produced every day by 2025.<\/p>\n What is the world doing with so much data? There are a lot of commercial and non-commercial applications:<\/p>\n One thing is common among all of these \u2013 the use of data analytics<\/strong> to gain insights, make predictions and drive innovation, be it in an individual, institutional or business setting.<\/p>\n In the enterprise, data analytics is absolutely necessary to implement artificial intelligence (AI) and machine learning (ML) solutions, improve productivity, identify high-growth markets, streamline operations and provide a better customer experience.<\/p>\n However, the scale and unstructured nature of datasets today make it nearly impossible for traditional IT infrastructure, applications and database management systems to process and analyze data quickly or cost effectively.<\/p>\n <\/p>\n <\/p>\n Scores of new technologies are being developed to address this challenge, including hybrid cloud architectures, edge\/distributed computing, IoT, databases that deal with a wide variety of data formats and queries, massive parallel processing and so on. These make huge demands of the underlying storage and data processing infrastructure \u2013 big data needs powerful CPUs with multiple cores, faster memory, more bandwidth and of course reliable storage with higher capacities that can be accessed and written faster.<\/p>\n Taken as a whole, the speed of data storage and processing depends more on the format of the data and the applications that access it than where it is stored. And counterintuitively, that makes the storage drive even more important to data analytics.<\/p>\n <\/p>\n SSDs have slowly but surely emerged as the de facto choice for ultra-fast storage in the enterprise, especially where a lot of data processing is involved. Further, most analytics platforms today run on the cloud, where users access it on an as-needed basis. However, the cloud service providers\u2019 data centers (where the actual analytics workload is hosted) also benefit from acceleration methods<\/a> such as parallelization (running multiple, concurrent data processes) and shuffling (increasing the volume of transition data processed by applications), which are supported by NAND-flash based SSDs.<\/p>\n Critically, SSDs also offer a price-to-performance ratio that sits snugly between DRAM and HDDs. The cost-per-bit is quite lower than DRAMs but the difference in access times and bandwidth is closing rapidly. On the other hand, SSDs might be more expensive than HDDs in cost-per-GB but the I\/O performance is several orders of magnitude higher, leading to a lower cost-per-IOPS.<\/p>\n Best of all, the pricing of NAND flash memory (the building blocks of SSD) is projected to fall faster than other media<\/a>, and eventually matching HDDs in $\/GB for some category of products, sweetening the SSD value proposition even more.<\/p>\n So what advantages do SSDs bring to the table for enterprises running data analytics applications?<\/p>\n <\/p>\n\n
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<\/a>Enter solid-state drives (SSDs)<\/h3>\n
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