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13FebFebruary 13, 2024

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,...

By Gautam GoswamiApache Kafka, Data Engineering, Processing EngineApache Flink, Apache Kafka, Big Data, real time analytics, real-time data streamingComments Off

19DecDecember 19, 2023

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...

By Gautam GoswamiApache Kafka, Data Engineering, Data ScienceApache Kafka, Big Data, HDFSComments Off

20NovNovember 20, 2023

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...

By Gautam GoswamiApache Kafka, Data ScienceApache Kafka, Big Data, event streaming platformComments Off

25SepSeptember 25, 2023

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...

By Gautam GoswamiApache Kafka, Data Engineering, Data ScienceApache Kafka, Big Data, Data Engineering, dead letter queue, Kafka Poison Pill, message serialization in kafka, message validation in kafka, Poison Pill, timeouts and deadlines in kafka, versioning the kafka topicsComments Off

10AprApril 10, 2023

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...

By Gautam GoswamiApache Kafka, Data EngineeringApache Kafka, Big Data, JDBC Sink Connector, Kafka connect, schema registryComments Off

06JanJanuary 6, 2023

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...

By Gautam GoswamiApache Hadoop, Apache Kafka, Architecture, Data Engineering, Hadoop Eco SystemApache Kafka, Apache Zookeeper, Big Data, Concept of zNodes, data log directory, dataDir parameter, dataLogDir parameter, distributed file systems, ephemeral zNodes, Hadoop, how to setup the multi-node Apache Zookeeper, Importance of Apache Zookeeper, persistence zNodes, receive notifications about changes to the ZooKeeper ensemble through watches, sequentialzNodes, Usage of Apache Zookeeper in Kafka Multi Node CLuster, Using Apache Zookeeper for managing distributed applications, ZooKeeper Data Directory, ZooKeeper ensemble, Zookeeper QuorumComments Off

05SepSeptember 5, 2022

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...

By Gautam GoswamiApache Kafka, Data EngineeringApache Kafka, Big Data, bin directory of the Kafka, Kafka, Kafka Administration, list of commands to run multi-broker multi-topic Kafka cluster, modify a Kafka topic, number of replication factor can’t be higher than the number of broker in the cluster, Orchestrating Multi-Brokers Kafka Cluster, Orchestrating Multi-Brokers Kafka Cluster through CLI Commands, retention time of a Kafka topic, retrieve log directories information from the kafka brokersComments Off

18AugAugust 18, 2022

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...

By Gautam GoswamiArchitecture, Data Engineering, Data IngestionAmazon Kinesis, Apache Flink, Apache Hadoop, Apache Kafka, Apache Samza, Apache Storm, batch processing layer, Big Data, Data Lake, data warehouse, event based Kappa architecture, event streaming platform, Hadoop Data Lake, HDFS, Kafka, Kafka Architecture Development Kafka Architecture Development, Kappa Architecture, Kappa Architecture for streaming data, Lambda Architecture, Map-Reduce framework, messaging engine, multiple stream processors, stream processing application, streaming computation system, streaming data analyticsComments Off

11JanJanuary 11, 2021

360° Visualization

By Kislay Komal360 Degree Big Data Visualization, 360 Degree Data Visualization, 360 Degree Visualization, 360° Data Visualization, 360° Visualization, analyze data, Apache Hive, Big Data, big data Proof of Concept, Big Data Visualization, customized data reports, Data Explosion, data processing, Data Visualization, Digitization, distributed data warehouse, enterprise data, HBase, strategic business decision, visualize dataComments Off

25SepSeptember 25, 2017

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...

By Gautam GoswamiData Engineering, Processing Engine4G Data, 4G Data Processing, 4G Data Processing Engine, Amazon EC2, analyze historical data, Apache, Apache Flink, Big Data, core computational belt, data pipeline, Data Processing Engine, Data stream source, dataArtisans, DataSet API, DataStream API, Delta Iterate, DGI, Directed Acyclic Graph, disk spilling, fault-tolerant, flatMap, Flink, Flink Runtime, Garbage Collector, Google cloud, GroupReduc, Hadoop, hashing and sorting, Iterate, Java Virtual Machine, JVM, JVM memory management system, map, memory management system, micro-batching, multi-node clusters, processor engine, real-time analytics, Savepoints, single JVM, Single node clusters, storage mechanism, stream processor, stream processor engine, Twitter streaming, Yahoo, YARN, Yet Another Resource NegotiatorComments Off

Tag - Big Data

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

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

The Zero Copy Principle With Apache Kafka

Causes and remedies of poison pill in Apache Kafka

Handling bad messages via DLQ by configuring JDBC Kafka Sink Connector

Few intrinsic of Apache Zookeeper and their importance

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

360° Visualization

Apache Flink – A 4G Data Processing Engine

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