During the last years there has been a tremendous adoption of big data technologies in companies of all sizes, making this innovative approach a common toolset to solve the new business needs. From the moment that large companies have started adopting solutions based on big data, IT vendors have improved capabilities and important features in areas such as security, role based access, reliability and self-management.
Real-time data and stream processing are also a big progress in the ecosystem together with IoT. In the last years very powerful and innovative platforms such as Apache Spark and Apache Flink have appeared to deal with large amounts of data in real time. This tools are going to be adopted by IoT projects in many different scenarios that will benefit from simplicity of use, API capabilities and reliability.
In the area of databases, relational databases are here to stay though the fast rise of NoSQL systems. Some relational databases have adopted a few NoSQL features such as key value stores.
There is an increasing interest in the organizations for applying Advanced Analytics to available historical data. Even if only a relatively small data set is available, companies can retrieve useful information from raw data. However the formulation of specific goals and questions is still one of common challenges of Advanced Analytical projects, so that’s why more efforts should be done in the initial phase of analytical projects in order to better formulate the goals, rather than simply trying out modern technologies.
Another tendency is the growing use of deep learning and transfer learning in industrial applications. Special attention should be given to the acceleration of the training process of machine learning models through the use of distributed computing. It is important to mention the relevance of open source solutions in the big data ecosystem where some of the most challenging projects use Apache License, MIT or BSD. This does not mean they are completely run by the community as they usually have one or several companies sponsoring the development.
Technology is becoming robust and enterprise ready. Big data analytics can offer now very interesting features that companies from all sizes need and strong companies are supporting the projects and development efforts. In the upcoming years we’re going to see the final round of adoption for those technologies that will become a common toolset for everyday data pipeline.
The implementation of a data culture is a very important challenge for any organization willing to take better decisions based on data evidence. The establishment of a culture that fosters data sharing between the different departments is key to be able to extract this knowledge. Data interoperability requires pre-processing, data treatment, standardization and in some cases, machine learning techniques are being used for that purpose. With new methodologies and vertical solutions as essential instruments, the users of a data driven organization will get better insights from their customers and operations and will be in a better position that their competitors making a profitable usage of data analytics.
For further information you can download the document: White paper: Big Data Technological Maturity