With standard changes in computing architecture and distributed, widespread processing methods leveraging big data to analyze and extract content. Recent advances in web technology enable users to generate processes and ingest content of any structure. cloud database computing helps provide computing infrastructure, centralized storage, and seamless services to organizations and users. In the digital world, data is at the heart of the economy, becoming part of governments, businesses, all sectors of society, and everyone’s daily lives. Data analytics is bringing together the forces of research and production. Many social networking, web, enterprise and IoT data need to be stored in secure, centralized data stores. It also requires that data be stored without any predefined schema and be able to be retrieved in near real-time. When demand increases, applications need to be scaled to maintain demand. Likewise, the data store should support scalability to support the needs of the application. Cloud providers and third-party organizations offer a wide range of data storage and database options. This article highlights the various data storage options available, their applicability, and limitations. Data science is becoming an integral part of every business and personal life. Organizations invest heavily in data science to stay ahead of today's competitive landscape and improve business results. It helps in deriving business insights, key metrics and predictive analytics from the captured data. Data science and its related fields are extremely valuable; it helps management teams make strategic business decisions. Many companies around the world are exploring data science and trying to build data science platforms Figure 1. Data science pipeline With the invention of Web technology, the volume, speed and variety of data from major sources such as IoT/sensors, network and social Network and traditional business systems. Data sources can be structured varieties or unstructured varieties or a combination of both. Data science uses a variety of methods to clean, explore, and transform data, and then applies techniques such as machine learning, deep learning, data visualization, and text analysis to analyze the data. It is closely integrated with artificial intelligence to make decision-making robots a reality. Figure 1 shows the architecture of the data science pipeline in detail.
