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Crafting a Multi-Node Multi-Broker Kafka Cluster- A Weekend Project

For the past couple of years, there has been a huge development in the appropriation of Apache Kafka. Kafka is a scalable pub/sub system and in a nutshell, is designed as a distributed multi-subscription system where data persists to disks. On top of it as a highlight, Kafka delivers messages to both real-time and batch consumers at the same time without performance degradation. Current users of Kafka incorporate Uber, Twitter, Netflix, LinkedIn, Yahoo, Cisco, Goldman Sachs, and so forth....

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...

Importance of Schema Registry on Kafka Based Data Streaming Pipelines

Needless to say Apache Kafka delivers messages to both real-time and batch consumers without performance degradation and in addition to that gaining enormous momentum as a foremost component for data streaming pipelines too. Credit card fraud detection, predictive maintenance, or real-time analytics, building streaming IoT platform, etc are the example of real-time use cases. To handle massive amounts of data ingestion, Apache Kafka is the cornerstone of a robust IoT data platform. A schema defines the structure of the data...

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...

Resolved – ” Incompatible clusterIds in… ” in Multi Node Hadoop Cluster Setup

Currently, there are many startups / small companies and their customers, working on Data Analytics, ML, AI and related solutions. Due to their budget constraints, some of them don't want to leverage Cloud-based storage. Alternatively, to process ingested data, they create basic Data Lake using HDFS. During this process, they might encounter the exception of "org.apache.hadoop.hdfs.server.common.Storage: java.io.IOException: Incompatible clusterIDs in /home/....". while starting the Name Node or Master Node in a multi-node Hadoop Cluster. This may occur in the following scenarios: ...

Network Topology To Create Multi Node Hybrid Cluster For Hadoop Installation

The aim of this article is to provide an outline for creating network topology for Hadoop installation in multi node hybrid cluster with limited available hardware resources. This cluster would be beneficial for learning Hadoop, with lower volume of unstructured data processing using various engines etc. Before the cluster setup: We installed Hadoop on a single node cluster running on Ubuntu 14.04 on top of Windows 10 using VMware workstation player. Later we have copied the .vmx file into multiple...

Data Governance & Security Mechanism in Distributed Data Storage System

We are aware that the traditional data storage mechanism is incapable to hold the massive volume of data generated with lightning speed for further utilization even if we perform vertical scaling, and we have anticipated only one fuel, nothing but DATA to accelerate the movement across all the sectors starting from business to natural resources including medical towards rapid growth. But the question is how to persist this massive volume of data for processing? The answer is, storing the data...

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...

Steering number of mapper (MapReduce) in sqoop for parallelism of data ingestion into Hadoop Distributed File System (HDFS)

To import data from most the data source like RDBMS, sqoop internally use mapper. Before delegating the responsibility to the mapper, sqoop performs few initial operations in a sequence once we execute the command on a terminal in any node in the Hadoop cluster. Ideally, in production environment, sqoop installed in the separate node and updated .bashrc file to append sqoop's binary and configuration which helps to execute sqoop command from anywhere in the multi-node cluster. Most of the...

Transfer structured data from Oracle to Hadoop storage system

Using Apache's sqoop, we can transfer structured data from Relational Database Management System to Hadoop distributed file system (HDFS). Because of distributed storage mechanism in Hadoop Distributed File System (HDFS), we can store any format of data in huge volume in terms of capacity. In RDBMS, data persists in the row and column format (Known as Structured Data). In order to process the huge volume of enterprise data, we can leverage HDFS as a basic data lake. In this...

Author - Gautam Goswami