Analyzing streaming data in large-scale systems is becoming a focal point day by day to take accurate business decisions due to mushrooming of digital data generation sources around the globe including social media. Real-Time analytics are becoming more attractive due to possibilities of getting insights from the time-value of data (in other words, when data is in motion).
Apache Flink, an open source highly innovative stream processor engine has been grounded which help to take advantage of stream-based approaches. Besides providing fault-tolerant, actual real-time analytics, it has the capability to analyze historical data and simplify developed data pipeline. Also, Flink is offering batch jobs too. Before understanding why Flink has been designated as 4th generation data processing engine in Big Data world, we need to be familiar with few data stream definitions.
The data element is the smallest element/unit of data which can be processed by the data streaming application as well as can be sent over the network. The data stream can be defined as continuous partitioned and partially ordered stream of data elements those can be potentially infinite. Data stream source is the source of data that continuously produces, possibly infinite amount of data elements and can’t be loaded into the memory. We can visualize the Twitter streaming as an example. Data stream sink is an operator and can be considered as the database where no output data stream, only input data stream. Besides, we can consider Map Reduce programming model where mapper consumes the data element from the input reader, process and subsequently write back to HDFS.
Can be contacted for real time POC development and hands-on technical training. Also to develop/support any Hadoop related project. Email:- [email protected] Gautam is a consultant as well as Educator. Prior to that, he worked as Sr. Technical Architect in multiple technologies and business domain. Currently, he is specializing in Big Data processing and analysis, Data lake creation, architecture etc. using HDFS. Besides, involved in HDFS maintenance and loading of multiple types of data from different sources, Design and development of real time use case development on client/customer demands to demonstrate how data can be leveraged for business transformation, profitability etc. He is passionate about sharing knowledge through blogs, seminars, presentations etc. on various Big Data related technologies, methodologies, real time projects with their architecture /design, multiple procedure of huge volume data ingestion, basic data lake creation etc.