What Is Big Data Technology?
What is Big Data? A powerful software tool to analyze, classify and interpret the large amount of unstructured and structured data which couldn’t normally be processed traditionally or manually is known as Big Data Technology. This aids in formulating predictions and taking preventive measures on the near future so that several risks can be avoided. This may sound like science fiction to some people but it’s the fact. It is being used by all major companies in every vertical sector and there is no dearth of opportunities for small and budding businesses to get in on this trend too. Now this isn’t just a concept of the past; it is becoming reality today.
The most interesting aspect of big data technology is its potential applications in strategic situations. For example, some companies may utilize this technology to provide detailed insights about customer behavior trends and nuances so that appropriate adjustments can be made to enhance sales productivity and drive more conversions. Retailers can use streaming analytics to monitor the supply chain and make informed decisions about where to place their focus in the market. Some may even go as far as employing this kind of technology to predict future demand for particular products so that over-supply can be prevented and profitability maximized.
While the advantages of using predictive analytics are many, one disadvantage is the difficulty of collecting, storing and analyzing big data technology. The data may need to be stored on servers, which can be susceptible to physical failures. Furthermore, storing the data may require a lot of resources from your business as it consumes power and other resources. Therefore, the advantages of using predictive analytics are considerably greater than the disadvantages. However, there are certain drawbacks too that you need to consider before deploying streaming data technology across your enterprise.
Although ideally the advantages of using big data analytics and other big data technologies are very significant, the reality is that they are not evenly spread around. Some are quite localized, while others are quite generalized. For example, HDFS has its advantages in storage and file system management but it is unable to provide better insights regarding demand. Similarly, MapDroid’s ability to manage large sets of data across multiple devices is relatively untapped compared to Hadoop’s breadth in terms of tasks and capabilities. The two technologies that remain relatively limited in the enterprise space are Bigtable and SoliKit.
Since most of the big data technologies today are web-based and do not have the capability to store large amounts of data on hard disks, it is mostly used for applications and analytical functions. Therefore, almost all the technologies available today can be considered as supporting applications for large-scale analytical and business decisions making process. Streaming is also primarily used for analyzing the big data sets but it is primarily used for providing the necessary interactivity and real-time functionality.
Streaming is mostly used for unifying different workloads across multiple devices for analytics purposes. One popular use for streaming is to run an application in the browser by leveraging the computing resources provided by the device and web browsers. In this way, it is able to reduce deployment costs and at the same time it makes it possible for users to access the application from any place via a web browser. In order to meet the challenges in providing real-time functionality and interactivity to consumers, most of the big data analytics systems today are running on the cloud.
Cloud-based technologies are fast becoming the tools of choice for the big data analytics and business decision making process. These include: data warehouse, online transactions, social media, mobile device management, desktop device management and more. With a comprehensive portfolio of tools, companies need only to choose the right cloud platform for their business. The most popular and well-established players in this space are Amazon Web Services, IBM WebSphere Application Service (AWAS), Google Cloud Platform, IBM’s SoftLayer platform, Hadoop Distributed Storage, and Kibana Business Intelligence.
Today, most of the large enterprises are already adopting advanced analytics solutions for their web and mobile applications. Companies running on mobile devices are already realizing the benefits of big data technology and its ability to create relevant and actionable information, and this is why they are investing in on-demand analytical processing database solutions. To meet the growing challenge of securing more data in the form of massive amounts of petabytes, on demand analytical processing database plans like Bigtable and Hadoop are the best and viable options for data security and operational efficiency. In addition to on-demand analytical processing, most of these companies are also leveraging off-the-shelf software from the market that is already pre-installed in their application servers. Data integration is also one of the most critical and widely used techniques for integrating big data technologies with on-demand database platforms.