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Data Warehouse or Cloud - Where do You Store Big Data Featured

Data Warehouse or Cloud - Where do You Store Big Data Ruchindra Gunasekara

One of the most misunderstood and divisive areas in business intelligence solutions is the area of data storage. Due to the current and future data storage technologies, many businesses are doing everything possible to ensure they know better and are ahead in data storage and usage. With the commodification of data, companies like Google and Amazon that have large data storage centers are capable of processing and storing data with minimum latency and serve massive user bases. When we talk about big data, traditional hard drives and other storage methods will not meet the new requirements.

Although significant strides have been made to improve the performance and scalability of storage technologies, there is still a massive space for improvement. Despite the complexities in big data technologies, they can pose many benefits, many associated with the use and further development of the technology. Advanced data storage capabilities can transform businesses, companies, and societies. Similarly, big data is a key enabler of analytics. With the right insights, decision-making can be improved, and so can other critical business aspects like accuracy and revenue. Big data analytics results in a competitive advantage for companies that depend on big data compared to those that have not adopted data-driven initiatives for their operations.

The need for big data storage

Unlike decades ago, the invention of computers, smartphones, and sensors has significantly increased the influx of data from various sources. This was also compounded by the rise of the internet and social media platforms, which attracted millions of people. For instance, governments started keeping track of records of every citizen and their documents, leading to an increased need for proper storage of data and advanced processing systems. While this was a significant contribution to innovation in data storage, the rise of the Internet of Things (IoT) and the internet, in general, has increased the necessity for bigger, more advanced systems to store and process data faster and effectively. Enterprises have been forced to scale up their big data storage capacities.

Big data storage methods

The current well-established big data storage methods in existence are:

Warehouse storage

A data warehouse has some similarities to a warehouse for storing physical goods. It is a large building facility that stores and processes data within an organization. The warehouse is crucial for big data analytics. These large data warehouses are used for various reporting and business intelligence (BI), data analytics, mining, monitoring, and research. The warehouses are optimized to store and process massive amounts of data at all times and feed them to users.

Data warehouse tools like Hadoop are used to manage data efficiently. These tools enable finding, access, visualization, and data analysis to help in business decision-making. Furthermore, warehouses are built with an expectation of exponential growth of data in mind. The biggest advantage of data warehouses is their ability to translate raw data into information insight that can be used to make crucial business decisions. They also provide a way of querries, analytics and reporting.

Cloud storage

This is another method of storing vast data and is the method that most people know. Some common examples of cloud storage are Google Drive and iCloud, which most computer or smartphone users have used in one way or another. Cloud storage allows data to be stored electronically online, where authorized people can access from everywhere. This eliminates the need to have a hard drive or a computer. Unlimited data can be stored and accessed everywhere with this type of storage. The cloud storage approach offers a readily available infrastructure and the ability to scale it based on need.

Cloud storage is cheaper than all physical storage options, unlike the data warehouse. Furthermore, it can be accessed from anywhere at any time.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

scottkoegler.me/

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