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What is Data Storage and How is it Used?

Analytics Vidhya

As modern companies rely on data, establishing dependable, effective solutions for maintaining that data is a top task for each organization. The complexity of information storage technologies increases exponentially with the growth of data. appeared first on Analytics Vidhya.

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A Dive into the Basics of Big Data Storage with HDFS

Analytics Vidhya

Introduction HDFS (Hadoop Distributed File System) is not a traditional database but a distributed file system designed to store and process big data. It provides high-throughput access to data and is optimized for […] The post A Dive into the Basics of Big Data Storage with HDFS appeared first on Analytics Vidhya.

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Optimizing Data Storage: Exploring Data Types and Normalization in SQL

KDnuggets

Learn about the data types and normalization techniques in SQL, which will be very helpful for optimizing your data storage.

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On-Premise vs Cloud: Where Does the Future of Data Storage Lie?

Monte Carlo

And while this analogy isn’t a perfect encapsulation of how some data teams operate after moving from on-premises to a modern data stack, it’s close. So perhaps a better analogy for these recently migrated data teams would be if I gave you 1,000 nail guns…and then watched you turn them all sideways to hit 1,000 nails at the same time.

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From Oracle to Databases for AI: The Evolution of Data Storage

KDnuggets

From Oracle, to NoSQL databases, and beyond, read about data management solutions from the early days of the RBDMS to those supporting AI applications.

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A Comprehensive Guide to Data Lake vs. Data Warehouse

Analytics Vidhya

Introduction In this constantly growing era, the volume of data is increasing rapidly, and tons of data points are produced every second. Now, businesses are looking for different types of data storage to store and manage their data effectively.

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What Comes After HDF5? Seeking a Data Storage Format for Deep Learning

KDnuggets

In this article we are discussing that HDF5 is one of the most popular and reliable formats for non-tabular, numerical data. This article suggests what kind of ML native data format should be to truly serve the needs of modern data scientists. But this format is not optimized for deep learning work.