The digital universe is growing in size rapidly and will reach 40 zettabytes in 2020. There are many reasons for this growth, including the Internet of Things (IoT), and new users buying smartphones, tablets, and personal computers (PCs). This huge amount of data needs to be processed, but former information technology (IT) using a single central processing unit (CPU) or multicore processor is not able to handle it in reasonable time. In order to address this issue, a new technology has been introduced: big data. This encompasses techniques that allow us to manipulate, extract, and even learn from this huge amount of data. Big data management has allowed well-reputed companies to exist and to be leaders in the IT market (Google, Facebook, Instagram, Twitter). Most of these companies have adopted Apache Hadoop, an ecosystem designed to support distributed applications, large-scale data processing, and storage, and provide high scalability.

In this paper, the authors present big data 2.0 processing systems by presenting Big SQL, the Big Graph, and the Big Stream processing systems. In fact, the authors discuss the different tools and software composing big data 2.0 processing systems by giving some recommendations about which one is valuable for a specific domain or use. The authors also review some areas of research that could improve big data 2.0 processing systems.

This paper is valuable in that it will help the reader find his way through the jungle of big data 2.0 processing systems, thanks to complete coverage of the different components: Big SQL, the Big Graph, and the Big Stream processing systems.