Processing Engine

Kafka with Flink

Why Apache Kafka and Apache Flink work incredibly well together to boost real-time data analytics

When data is analyzed and processed in real-time, it can yield insights and actionable information either instantly or with very little delay from the time the data is collected. The capacity to collect, handle, and retain user-generated data in real-time is crucial for many applications in today’s data-driven environment. There are various ways to emphasize the significance of real-time data analytics like timely decision-making,  IoT and sensor data processing, enhanced customer experience, proactive problem resolution, fraud detection and security,...

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Basic Understanding Of Stateful Data Streaming Supported By Apache Flink

Technologies related to Big Data processing platform are enhancing the maturity in order to efficiently execute the streaming data which is becoming a major focus point to take business decision instantly specially in telecom and retail sector. Collecting data continuously from the various sensors installed/fitted with an industrial heavy equipment, click stream on an e-commerce application’s navigation etc can be considered as streaming data generation sources. By leveraging streaming application, we can process/analyze these continues flow of data without...

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Apache Flink – A 4G Data Processing Engine

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 helps to take advantage of stream-based approaches. Besides...

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Establishment of Data Lake specific to multi-channel e-commerce application to understand customer’s buying pattern

Post order fulfillment data is becoming a very important asset of e-commerce vendors to understand complete buying pattern of customers. Especially for the e-commerce vendors who sells multiple products starting from electronics to apparels. Extraction and transformation are time-consuming operations when partially structured data starts moving from the various sources and finally land into the relational data warehouse.  Data extracted from the social media are semi-structured (JSON or XML).  As an example, Facebook provides information in JSON format through Graph API and same...

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