A Survey on Real-Time Big Data Analytics: Applications and Tools

Big Data Ingestion and Preparation Tools

“…Security is another challenge for stream data ingestion process which comes out from quick growth of the internet, web-based systems who are facing malicious and suspicions files threatening in their security, so the ingestion process should provide security, auditing, and provenance. The analytical value from the stream data depends on accuracy and completeness of data so achieving good and accurate stream data ingestion is complicated and challenging task that require good planning and expertise (Yadranjiaghdam,B.,Yasrobi,S.,& Tabrizi,N.,217) (Pal, G., Li, G., & Atkinson, K., 2018) (Gurcan, F., & Berigel, M., 2018) 3.4.1 Flume Apache It's a distributed reliable, available and efficient service for importing, collecting, aggregating and bringing in huge amount of data with its streaming feature and ingest it in a way that makes it easy for processing tool, hardly supports fault tolerance with accurate consistency ways, the data model used by flume is particularly used for online analytic application It has the most important role in data ingestion for real time data analytics, which is responsible for data refining and data visualization (Yadranjiaghdam, B., Pool, N., & Tabrizi, N., 2016 The data flow in flume same as pipeline that ingest data from the source to destination. Regarding to figure 5 below that discussed Flume architecture, data is transformed from source to destination based on flume agent which is JVM process that host the components during the data flow from the source to next end and it contains of channel, sink and the source.…”

A new Internet of Things architecture for real-time prediction of various diseases using machine learning on big data environment

“…Most of the healthcare analytics solution mainly focused on Hadoop [20], it can process a large volume and diverse data sources in case of batch oriented computing. Hadoop would be limited for real-time computing, which Spark is faster than Hadoop and has a better performance especially in problems involving iterative machine learning [21].…”

On using MapReduce to scale algorithms for Big Data analytics: a case study

“…MapReduce has quickly become popular and wildly get adopted. There are various field of researches that use MapReduce to enhance their performance, for example survey research in health care [14,29], government [6], sentiment analysis [19], set operations [15], or real-time data analytic [37]. There are researches that focus on state-of-the-art of MapReduce and its applications [20,24].…”