When the world big data rapidly expanded a decade ago, there were no signs that they would slow down. It is primarily aggregated across the internet, such as social networking, web search requests, text, and media files. IoT devices and sensors produce another gigantic share of data. These are the main reasons for the global big data market growth of 49 billion dollars.

Spark will Widespread

Apache Spark is a platform for data processing that can easily perform tasks on very large data sets and also spread the functions of data processing over many devices, either on its own or in combination with other distributed computing resources. These two qualities are important to the worlds of big data and machine learning that require vast data stores to sharpen the masses of computer power. Spark removes some of the programming burdens from developers with an easy-to-use API which sums up many of the grunt tasks of distributed computing and big data processing.

Apache Spark has been one of the main computing frameworks that spread throughout the world. Spark offers native binding for Java, Scala, Python, and R languages, and supports SQLs, data sharing, machine learning, and graphic processing. The Spark software can be used in several ways.

The convergence of IoT, Cloud, and Big Data

In order to facilitate interaction between machines and humans (M2H) and machines (M2 M), the Internet of Things is an opportunity for simplifying operations in many areas. Until now it has been greatly improved. In most cases, sensor-generated data is transmitted for analysis to the Big Data System and final reports are made. This is also the main interconnecting point of the two technologies.

For the next ten years, IoT is expecting a future of $19 trillion in the web industry, which will give room for more IoT and Big Data research and development.

Cloud computing plays a significant part in the storage and management of the data by generating an immense amount of data. It is not only about big data growth but also the development of platforms such as Hadoop for data analytics. As a consequence, it provides new cloud computing opportunities. Therefore, service providers like AWS, Google, and Microsoft have cost-effectively their own Big Data Solutions for businesses of all sizes.

Mixed Reality will improve Data Visualization

AR and VR have gained a lot of traction among customers in the past few years. With the launch of Pokémon Go, Augmented Reality had garnered around 100 million users within just a few weeks of launch. Though AR or VR might not be very useful for large corporations, the concept of Mixed Reality might very well be. Mixed reality combines the virtual world with our real-world and devices like Microsoft Hololens are already gaining traction. Mixed Reality will offer huge opportunities for organizations to better perform tasks and also to better understand the big data.

Deep Learning

Deep learning is an advanced form of machine learning which is based on neural networking. Deep learning help recognize specific items of interest from massive volumes of unstructured data. It is mostly useful for learning from huge volumes of structured and unstructured data. Thus businesses and organizations should pay more attention to deep learning algorithms to deal with the heavy influx of big data.

Data Virtualization

Data virtualization will see strong momentum this year. Data virtualization has the ability to unlock the hidden concepts and conclusions from a large set of data. It also allows enterprises and organizations to retrieve and manipulate data on the go.

To address big data problems, the management and use of computer and data-intensive systems require huge amounts of highly distributed datagrams. Virtualization offers the additional flexibility needed to realize large data platforms. Although virtualization is theoretically no prerequisite for big data analysis, in a virtualized environment software frameworks are more effective.

Conclusion 

As mentioned earlier, this year will be an exciting year for big data, and analytics systems will become the top priority for organizations. These systems are expected to perform well operationally, and fulfill promises of business value to the organization.