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Data science platform market is slated for an exponential growth of more than 40% in the next two years. A forecast by market research predicts numbers as high as 66% of growth in the next 3 years. All this gives an account of the intensity of the data science market as well as the tools that are involved there. Data science platform s have a wide range of applications when it comes to new and emerging businesses. It also caters to the requirements of the established business and helps them to derive state-of-the-art analytics. In this article, we aim to understand the entire morphology and life cycle of data science platforms in detail. Data science platform: Morphology In simple terms, we can define a data science platform as a technical framework that enables us to understand the working of the data science life cycle. In most cases, the data science life cycle revolves around three important stages. The first stage is called the stage of data integration. Needless to ment...

Companies that are building Cloud-first ecosystems rely on a Data ingestion pipeline to advance into the new era of business analytics, AI Operations, and IT modernization. The need to embrace digital transformation is forcing organizations to adopt new technologies to reengineer the conventional IT setups. Today, every organization looks at Cloud and data management tools to redefine the way they use IT networking and data storage platforms for various business operations. To stay competitive in the business, digital transformation goals are designed with integrated Cloud ecosystems in mind, and a data ingestion pipeline is a very important aspect of this strategy. From redefining Cloud migration workflows to simplifying stream analytics with AI and Machine Learning applications, Data Ingestion tools can help an organization demolish the legacy silos, and replace these with a unified enterprise data warehouse where you can put all your data in one place and build a hyper-scale Cl...