Tag - Big Data

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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Using Kafka to manage Large Messages

Architecture to leverage Apache Kafka for sharing large messages (GB size)

In today's data-driven world, the capability to transport and circulate large amounts of data, especially video files, in real-time is crucial for news media companies. For example, an incident occurred in a specific location, and a news reporter promptly filmed the entire situation. Subsequently, the complete video was distributed for broadcasting across their multiple studios situated in geographically distant locations. To construct or create a comprehensive solution for the given problem statement, we can utilize Apache Kafka in conjunction with...

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Zero Copy Principle

The Zero Copy Principle With Apache Kafka

The Apache Kafka, a distributed event streaming technology, can process trillions of events each day and eventually demonstrate its tremendous throughput and low latency. That’s building trust and over 80% of Fortune 100 businesses use and rely on Kafka. To develop high-performance data pipelines, streaming analytics, data integration, etc., thousands of companies presently use Kafka around the globe. By leveraging the zero-copy principle, Kafka improves efficiency in terms of data transfer. In short, when doing computer processes, the zero-copy...

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Causes and remedies of poison pill in Apache Kafka

Causes and remedies of poison pill in Apache Kafka

A poison pill is a message deliberately sent to a Kafka topic, designed to consistently fail when consumed, regardless of the number of consumption attempts. Poison Pill scenarios are frequently underestimated and can arise if not properly accounted for. Neglecting to address them can result in severe disruptions to the seamless operation of an event-driven system. The poison pill for various reasons: The failure of deserialization of the consumed bytes from the Kafka topic on the consumer side. Incompatible serializer and deserializer...

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Handling bad messages via DLQ by configuring JDBC Kafka Sink Connector

Any trustworthy data streaming pipeline needs to be able to identify and handle faults. Exceptionally while IoT devices ingest endlessly critical data/events into permanent persistence storage like RDBMS for future analysis via multi-node Apache Kafka cluster. (Please click here to read how to setup multi-node Apache Kafka Cluster). There could be scenarios where IoT devices might send fault/bad events due to various reasons at the source points and henceforth appropriate actions can be executed to correct it further. The Apache  Kafka...

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Few intrinsic of Apache Zookeeper and their importance

As a bird’s eye view, Apache Zookeeper has been leveraged to get coordination services for managing distributed applications. Holds responsibility for providing configuration information, naming, synchronization, and group services over large clusters in distributed systems. To consider as an example, Apache Kafka uses Zookeeper for choosing their leader node for the topic partitions. Please click here if you want read on how to setup the multi-node Apache Zookeeper cluster on Ubuntu/Linux zNodes The key concept of the Zookeeper is the znode which can be acted...

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Orchestrating Multi-Brokers Kafka Cluster through CLI Commands

This short article aims to highlight the list of commands to manage a running multi-broker multi-topic Kafka cluster utilizing built-in scripts. These commands will be helpful/beneficial when the cluster is not integrated or hooked up with any third party administrative tool having GUI facilities to administer or control on the fly. Of course, most of them are not free to use. Can refer here to set up a multi-broker Kafka cluster.By executing the built-in scripts available inside the bin...

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Why Kappa Architecture for processing of streaming data. Have competence to superseding Lambda Architecture?

Data is quickly becoming the new currency of the digital economy, but it is useless if it can’t be processed. The processing of data is essential for subsequent decision-making or executable actions either by the human brain or various devices/applications etc. There are two primary ways of processing data namely batch processing and stream processing. Typically batch processing has been adopted for very large data sets and projects where there is a necessity for deeper data analysis, on the...

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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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