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This article was published as a part of the Data Science Blogathon. In this article, we shall discuss the upcoming innovations in the field of artificial intelligence, big data, machine learning and overall, Data Science Trends in 2022. Times change, technology improves and our lives get better. Deep learning, natural language processing, and computer vision are examples of technologies that have emerged as a result of the rise of Data Science as a field of research and practical application throughout the previous century. In general, it has aided the development of machine learning (ML) as a means of achieving artificial intelligence (AI), a field of technology that is fast changing the way we work and live.
Big data as the name suggests is a large amount of data that is growing exponentially and it is difficult to be managed by traditional data management tools. These large data sets need cost-efficient, innovative, and efficient methods for analysis so that they can be helpful to devise insights. Currently, the world produces 2.5 quintillion bytes of data daily. The explosion of data continues in the roaring '20s, both in terms of generation and storage the amount of stored data is expected to continue to double at least every four years. When we look at Facebook that has hundreds of users across the globe. Facebook generates about 500 petabytes of data. And the company also uses big data to handle data needs. In the age of digital technology and social media, the amount of information generated is increasing exponentially. It has now become possible to analyse the data and get insights from it immediately. The concept of big data has been around for several years. Big data, in particular, offers marketers with unmatched data regarding their customers, which opens possibilities of easily recording, tracking and processing massive volumes of data within all actions. Big data solutions include storage, backup, analysis, visualization, as well as administrative controls for enormous volumes of data. Big data solutions make a complicated data infrastructure more efficient. Furthermore, big data technologies allow smart cities to leverage expanded capabilities. Internet of Things (IoT) technologies, smart sensors, smart transportation and other innovations are all part of this. Several companies have emerged over the years to provide solutions for wrangling huge datasets and understanding the relevant information within them. Some offer powerful data analysis tools, while others aggregate and organize datasets into usable formats. Big data has its uses and applications in almost every industry. Big data has a massive contribution to the advancement in technology, growth in business and organizations, profit in each sector, etc. Looking at the non-stop growth and progress of big data, companies started adopting it more frequently. A3logics is a global IT services, consulting, and business solutions company leveraging best-in-class technologies to drive business efficiency. A3logics creates a next-generation space dedicated to aligning IT with business goals. The company's dedicated team of tech-savvy developers and enthusiasts help organizations embrace top solutions, which will help them fulfil their business objectives. A3logics uses artificial intelligence, blockchain, IoT, big data, augmented and virtual reality, etc to come up with software solutions that reflect the future. With the aim to bridge the gap between technology and business needs, the company works as a global provider of innovative solutions.
As a result, all major cloud providers are either offering or promising to offer Kubernetes options that run on-premises and in multiple clouds. While Kubernetes is making the cloud more open, cloud providers are trying to become "stickier" with more vertical integration. From database-as-a-service (DBaaS) to AI/ML services, the cloud providers are offering options that make it easier and faster to code. Organizations should not take a "one size fits all" approach to the cloud. For applications and environments that can scale quickly, Kubernetes may be the right option. For stable applications, leveraging DBaaS and built-in AI/ML could be the perfect solution. For infrastructure services, SaaS offerings may be the optimal approach. The number of options will increase, so create basic business guidelines for your teams.
But the big data industry has significant inertia moving into 2021. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming. We were very encouraged to hear such exciting perspectives. Even if only half actually come true, Big Data in the next year is destined to be quite an exciting ride. The "analytic divide" is going to get worse. Like the much-publicized "digital divide" we're also seeing the emergence of an "analytic divide." Many companies were driven to invest in analytics due to the pandemic, while others have been forced to cut anything they didn't view as critical to keep the lights on – and a proper investment in analytics was, for these organizations, analytics was on the chopping block. This means that the analytic divide will further widen in 2021, and this trend will continue for ...