Processing Engine

Unlocking the Power of Patterns in Event Stream Processing (ESP): The Critical Role of Apache Flink’s FlinkCEP Library

We call this an event when a button is pressed, a sensor detects a temperature change or a transaction flows through. An event is an action or state change that is important to an application. Event stream processing (ESP) refers to a method or technique to stream the data in real-time as it passes through a system. The main objective of  ESP is to focus on the key goal of taking action on the data as it arrives. This enables real-time analytics...

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Real-Time Redefined: Apache Flink and Apache Paimon Influence Data Streaming’s Future

    Apache Paimon is made to function well with constantly flowing data, which is typical of contemporary systems like financial markets, e-commerce sites, and Internet of Things devices. It is a data storage system made to effectively manage massive volumes of data, particularly for systems that deal to analyze data continuously such as streaming data or with changes over time like database updates or deletions. To put it briefly, Apache Paimon functions similarly to a sophisticated librarian for our data....

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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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