Can Data-As-A-Service Help Enterprises Get More Value Out Of Data Lake?
Like all traditional members of the “as-a-Service” family such, Data-as-a-Service is a relatively new term, as it is being used since 2015. The purpose of DaaS is that it delivers data on demand to the consumers through various APIs in order to avoid the typical need to fetch as well as store large data assets and then search for the required information in the data asset. This open source software solution basically runs between the systems which manage your data and the tools which you use for analysing the data. This service can be used in various sectors such as retail, healthcare, e-commerce, financial sectors, telecommunications, homeland security, organisations, etc. where the users can be able to access the databases provided by the vendors or host their own databases on their self-managed systems. It is a self-service model where you can explore, organise, describe as well as analyse data regardless of its location, size or structure with the help of tools such as Tableau, Python, etc. According to a report by Telecommunications Market Research Reports, the future revenue from big data-driven telecom analytics and Data-as-a-service to grow big data revenue into $5.4 billion industry by 2019. The telecom analytics market alone is expected to grow at a compound annual growth rate of nearly 50% between 2014 and 2019. This alternative cloud computing service model is different from the traditional as-a-service models. Organisations have been struggling with the data lakes (cloud storage services) since the last few years due to the heavy flow of data and unavailability of the right tools. Future Of Analytics in DaaS The purpose of the Data-as-a-Service model is to dump the hazards and hardships of data management into a third party cloud-based provider. Customer Resource Management (CRM), Enterprise Resource Planning, etc. are the most common business applications which are powered by Data-as-a-Service technology. According to Oracle, Data as a Service (DaaS) is a revolutionary new service that gives you unprecedented levels of connection to customers. It is a response to the growing volume and variety of data generated in today’s digital world. Users consume data across a variety of systems and processes—data that can be used to make customer engagements more relevant and impactful. This data-driven insight helps you connect with customers across marketing and sales to virtually all areas of your business. One of the DaaS platforms is the Dremerio, which enables business analysts and data scientists to explore and analyse data at any time. The platform empowers users to curate, accelerate and share any data at any time. It allows the analysts to easily cut through complicated source data with nested structures or mixed types, creating the new virtual dataset. Benefits Of Data-as-a-Service Agility The consumers don’t need any kind of extensive knowledge of the underlying data and can move quickly due to simplicity in the data accessing process. Cost-effectiveness Managing the large volume of data are being done in-house and there is no need to spend an extra penny for handling the bulk of data. The service space can deploy their data delivering applications in a cost-effective manner. Data Quality By DaaS, one can receive high-quality data because the amount of data which are being accessed are controlled through the data service itself. This service adds a strong layer of security as well as augmented data quality.
Like all traditional members of the “as-a-Service” family such, Data-as-a-Service is a relatively new term, as it is being used since 2015. The purpose of DaaS is that it delivers data on demand to the consumers through various APIs in order to avoid the typical need to fetch as well as store large data assets […]
Like all traditional members of the “as-a-Service” family such, Data-as-a-Service is a relatively new term, as it is being
["DaaS"]
["big data", "API", "cloud computing", "AI", "Git", "RAG", "Python", "analytics", "DaaS", "R", "data lake"]
["AI", "analytics", "RAG", "cloud computing", "Python", "R", "Git", "API", "big data", "data lake"]
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.