You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review on Business Intelligence and Big Data 
Chapter Title:  Introduction 
1. Abstract of Cited Paper (BIB001):  Abstract One of the major applications of future generation parallel and distributed systems is in big-data analytics. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size. Beyond their sheer magnitude, these datasets and associated applications’ considerations pose significant challenges for method and software development. Datasets are often distributed and their size and privacy considerations warrant distributed techniques. Data often resides on platforms with widely varying computational and network capabilities. Considerations of fault-tolerance, security, and access control are critical in many applications (Dean and Ghemawat, 2004; Apache hadoop). Analysis tasks often have hard deadlines, and data quality is a major concern in yet other applications. For most emerging applications, data-driven models and methods, capable of operating at scale, are as-yet unknown. Even when known methods can be scaled, validation of results is a major issue. Characteristics of hardware platforms and the software stack fundamentally impact data analytics. In this article, we provide an overview of the state-of-the-art and focus on emerging trends to highlight the hardware, software, and application landscape of big-data analytics.

Literature Review:
Technological advancements of IT have led to storing more data at lower cost and drastically increased transmitting rates. Parallel computing has increased computing power as well by processing multiple cores simultaneously. It is hard to find any device that doesn't generate data like sensors, plane engines, online transactions, emails, videos, audios, images, click streams, logs, posts, search queries, health records, social networking interactions, science data, and mobile phones. All of these and their applications have begun to generate huge volume data at high velocity and variety which is impossible to store and process with classical technologies and programming paradigms. This kind of data is called big data. International Data Corporation (IDC) reports that digital universe will continuously expand, be complex and interesting. The volume of data is expected to be 8 ZB by 2020   . Data generation speed is also increasing exponentially. It is estimated that world data will double  or triple itself every year. Therefore this tremendous increase has caused finding new technologies, processing techniques, programming paradigms, and analyzing tools, which are out of classical technology capabilities, to deal with and then extract value from it. Many companies have been using big data for a while and investing in it. Google processes hundreds of PB, Facebook deals with hundreds of TB contents, every day tens of TB videos are uploaded to YouTube, for example on average 72 hours video is uploaded in a minute BIB001 . Twitter has more than 550 million active users and they produce 9100 tweets every second. 3 billion pieces of content are generated on Facebook every day. A Boeing jet engine can produce 10 terabytes operational information in 30 minutes BIB002 . There is increasing publication about big data for last decades. Initially, publications were drawing attention what big data was, how important it was, and its opportunities and challenges. Then more detailed papers were published about big data impact on many diverse areas,from science to retails. Although BI has in common with big data, there are not sufficient papers about how big data impacts on BI. To fill this gap this paper will overview big data and BI then argue big data effects on BI solutions.