Big data, cloud driving transformations in the world of enterprise technology

As companies look at data as a huge opportunity for competitive advantage, Oracle shares its top 10 big data predictions for 2017


Data virtualization is emerging as a means to enable real-time big data analytics without the need for data movement. Photo: Mint
Data virtualization is emerging as a means to enable real-time big data analytics without the need for data movement. Photo: Mint

Big data and cloud are the two technologies driving dramatic transformations in the world of enterprise technology. As companies look at data as a huge opportunity for competitive advantage, Oracle shares its top 10 big data predictions for 2017

Era of Ubiquitous Machine Learning: In 2017, huge increase is expected to bring availability of machine learning capabilities into tools for both business analysts and end users—impacting how both corporations and governments conduct their business. Moreover, Machine learning will affect user interaction with everything from insurance and domestic energy to health care and parking meters.

Emergence of Cloud to the data: In the future, more and more organizations will need to develop cloud strategies for handling data in multiple locations.

Applications are driving big data adoption: Early use cases for big data technologies focused primarily on IT cost savings and analytic solution patterns. Now, we’re seeing a wide variety of industry-specific, business-driven needs empowering a new generation of applications dependent on big data

Internet of Things (IoT) will integrate with enterprise applications: Enterprises must simplify IoT application development and quickly integrate this data with business applications. The impact will be felt not only in the business world, but also in the exponential growth of smart city and smart nation projects across the globe.

Data virtualization becomes a reality: Organizations in 2017 will realize that it’s not feasible to move everything into a single repository for unified access, and that a different approach is required. Data virtualization is emerging as a means to enable real-time big data analytics without the need for data movement.

Kafka to be the runaway for big data technology: Apache’s Kafka technology is already building momentum, and looks set to hit peak growth in 2017. Kafka employs a traditional, well proven bus-style architecture pattern, but with very large data sets and a wide variety of data structures. This makes it ideal for bringing data into your data lake and providing subscriber access to any events your consumers ought to know about.

A boom in pre-packaged integrated cloud data systems:Pre-packaged offerings including integrated cloud services such as analytics, data science, data wrangling, and data integration are removing the complexity of do-it-yourself solutions. In 2017, a boom in pre-packaged, integrated cloud data labs is expected to come.

Cloud-based object stores become viable: Object stores have many desirable attributes: availability, replication (across drives, racks, domains, and data centers), and backup. Object storage technologies will become a repository for big data as they get more and more integrated with big data computing technologies.

Next-generation compute architectures enable deep learning at cloud scale: In 2017, deep learning at scale, and easy integration with existing business applications and processes will exist.

Hadoop security is no longer optional: Hadoop is an open-source framework created to make it easier to work with big data. In 2017, It is expected to deploy multilevel security solutions for your big data projects in the future.

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