Big Data vs Good Data. The information age is in full swing, and for most businesses around the globe, the data stream has overrun its banks and become a flood of big data. From social media accounts to supply chain and spend data, businesses have unpreceded access to almost limitless information. But with information, as with most things
Data lakes benefit more from big data technologies, particularly those that can enhance data lake analytics. Programs like Hadoop can process large quantities of data in any format, promoting the adaptability and scalability of a data lake. In addition to this, Hadoop can apply structured views to unprocessed data in a warehouse.
For example, pro’s like more data means more insights, more information, sharper models (w.r.t to how you used it) & similarly handling large data comes with some con’s like storing, managing Big data and artificial intelligence have a synergistic relationship. AI requires a massive scale of data to learn and improve decision-making processes and big data analytics leverages AI for better data analysis. With this convergence, you can more easily leverage advanced analytics capabilities like augmented or predictive analytics and more Big data architectures. A big data architecture is designed to handle the ingestion, processing, and analysis of data that is too large or complex for traditional database systems. The threshold at which organizations enter into the big data realm differs, depending on the capabilities of the users and their tools.
The most apparent difference when comparing data warehouses to big data solutions is that data warehousing is an architecture, while big data is a technology. These are two very different things in that, as a technology, big data is a means to store and manage large volumes of data. On the other hand, a data warehouse is a set of software and
