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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

25AugAugust 25, 2022

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...

By Gautam GoswamiApache Kafka, Architecture, Data Engineering, Data IngestionApache Kafka, assign schema info in the schema registry, Avro, building streaming IoT platform, centralized schema management, Confluent Schema Registry, Credit card fraud detection, Data ingestion, data pipeline, Data Pipelines, Data Streaming, deserialized the messages, distributed storage layer for schemas, Hadoop, JSON Schema, Kafka based data pipeline, Kafka based data streaming pipelines, Kafka connect, Kafka producers and consumers, multi-broker Kafka topic, Multi-node kafka cluster, predictive maintenance, producer-consumer contract, Protobuf schemas, real-time analytics, schema change history, schema evolution, schema of registered data streams, schema registry, Schema Registry on Kafka Based Data Streaming Pipelines, Schema Registry on Kafka Streaming Pipelines, service layer for metadata, streaming applications, streaming data to Kafka topicComments Off

25DecDecember 25, 2020

Real-time Distributed Data-streaming with Kafka

Real time distributed data streaming Originally written in Scala and Java, Apache Kafka is a fast, horizontally scaling, fault-tolerant messaging platform for distributed data streaming first started at LinkedIn. It provides a publisher-subscriber mechanism for processing and storing data streams in a fault-tolerant way. It is used for building real-time data pipelines by streaming social data, Geo-spatial data or sensor data from various devices. Kafka acts like a plugin for Spark, Hadoop, Storm, HBase, Flink and many others for big data analytics. Using...

02MarMarch 2, 2019

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...

By Gautam GoswamiApache Hadoop, Data Engineering.vmx file, Assign static IP to Ubuntu VMware workstation players, Choosing a network switch over router, Configuration of data nodes with name node, Configure Internet Protocol Version 4 (TCP/IPv4) on Windows, Create Multi Node Hybrid Cluster, Create Multi Node Hybrid Cluster For Hadoop Installation, creating network topology for Hadoop installation, D-Link DES-1005C 10/100 Network Switch, Dell Inspiron 1525 Laptop with 2 GB RAM and Ubuntu 14.04 as operating system, Dell Inspiron 5458 Laptop with 16 GB RAM and Windows 10 as host operating system, Desktop with 8 GB RAM and Windows 7 Professional as host operating system, Ethernet LAN setup using Network Switch, guest OS, Hadoop, Hadoop Installation, Lenovo B40-80 Laptop with 4 GB RAM and Windows 10 as host operating system, Multi Node Hybrid Cluster, Multi Node Hybrid Cluster For Hadoop Installation, Network Adapter setting on the VM Player, Network Topology For Hadoop Installation, Network Topology To Create Multi Node Hybrid Cluster, Network Topology To Create Multi Node Hybrid Cluster For Hadoop Installation, OS Ubuntu 14.04, PC to Router, PC to Switch, Router to Switch, single node cluster running on Ubuntu, Straight-through cables, Ubuntu on top of Windows, Ubuntu on top of Windows 10 using VMware workstation player, VMware Workstation 12 player, VMware Workstation 7.x player, Windows using VMware workstation playerComments 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

08SepSeptember 8, 2017

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...

By Gautam GoswamiData Engineering, Hadoop Eco SystemAmazon web service, Apache Sqoop, Apache's sqoop, Data ingestion, Data ingestion mechanism, distributed storage, distributed storage mechanism, enterprise data, Google cloud, Hadoop, Hadoop 2.x, Hadoop Distributed File System, Hadoop storage system, HDFS, huge volume of enterprise data, Microsoft Azure, multi node cluster, Oracle to Hadoop, Sqoop, structured data, Transfer structured data from Oracle to Hadoop, Using Apache sqoopComments Off

29AugAugust 29, 2017

Data Ingestion phase for migrating enterprise data into Hadoop Data Lake

The Big Data solutions helps to achieve valuable information to iron out the accurate strategic business decision. Exponential growth of digitalization, social media, telecommunication etc. are fueling enormous data generation everywhere. Prior to process of huge volume of data, we should have efficient data storage mechanism in a distributed manner to hold any form of data starting from structured to unstructured. Hadoop distributed file systems (HDFS) can be leveraged efficiently as data lake by installing on multi node cluster....

By Gautam GoswamiData Engineering, Data IngestionApache software foundation, Apache Sqoop, ata storage mechanism, ATG database, ATG database schema, cloud service providers, collecting Twitter streaming data, Couchbase, Data ingestion, Data Ingestion phase for migrating enterprise data into Hadoop Data Lake, Data Lake, data storage mechanism, DB2, Digitization, distributed storage, efficient data storage mechanism, ELT, enterprise data, export data from Kafka topic to HDFS, fault-tolerant, Flume, Hadoop, HADOOP Cluster, Hadoop Data Lake, Hadoop distributed file systems, Hadoop multi node cluster, HDFS, Hive, huge data reservoirs, huge volume of data, Ingestion, JDBC connector, JDBC protocol, Kafka, Kafka HDFS connector, Kafka to HDFS, Mainframe, mainframe dataset to HDFS, MapReduce, MapReduce distributed computing, migrating enterprise data, moving large amount of streaming data into HDFS, multi node cluster, multiple delimited text files, MySQL, Netezza, NoSql DB, NoSql Stores, Oracle, Oracle 11g Enterprise Edition, Oracle ATG Platform, parallel import process, parallel processing, pluggable mechanism, PostgreSQL, read the messages from Kafka topic, SQLServer, Sqoop, Sqoop installation, Strom, Using Kafka HDFS connectorComments Off

29JunJune 29, 2017

Technical Leadership Training

Topics Training Details Duration Computer Basics Basic of Computers and Programming C C Basic, C Advanced C++ C++ Basic, C++ Advanced, Microsoft Technologies - ASP .Net, SharePoint, - Windows Device Driver Development Java/J2ee Java, JSP, Servlets, Struts, Spring, Hibernate Oracle Stacks Oracle ADF, WCS TIBCO Tibco BW, Active Matrix Agile Scrum, Agile Web Web Development, HTML, Ajax, AngularJS, JQuery LAMP & CMS PHP, MySQL, Joomla, Wordpress, Drupal Mobility HTML5, Android, iOS E-Commerce Oracle-ATG Commerce, DemandWare Cloud Commerce, Magento Commerce Big Data & Hadoop - Introduction to Big Data and Data Analytics - Overview of Hadoop - In depth knowledge in HDFS (Hadoop Distribution File System) - Map Reduce - Customization of Hadoop framework -...

By Kislay KomalAJAX, Android, AngularJSWindows Device Driver DevelopmentLAMP - PHP with MySQLCMS - Joomla, ASP.NET, Basics of Computers and ProgrammingC BasicC Advanced, Big Data, Big data training, C++ Advanced, C++ Basic, Customization of Hadoop framework, Demandware Cloud Commerce, Detail analysis of programming concept for Map Reduce, DrupalMobility - HTML5, EC2, Hadoop, Hadoop Distribution File System, Hadoop Ecosystem and architecture, Hadoop supporting component like Flume, Hadoop Training, HBase, HibernateeCommerce - Oracle-ATG Commerce, Hive, HTML, Introduction to Big Data and Data Analytics, iOS, JSP, Magento CommerceOracle ADF, Map Reduce, OOzie etc, Oracle ATG commerce, oracle atg services, Oracle WCSTIBCO BWAgile & ScrumMultimedia and AnimationWeb Development, Overview of HadoopHDFS, Real time analysis, Saleforce Commerce Cloud, Servlets, Setup and configure Hadoop in Amazon cloud cluster, SharePointJava/J2EE - Java, Spring, Struts, Twitter sentiment analysis using Hadoop eco system, WordPressComments Off

13JunJune 13, 2016

Future of Big Data Analysis Using Hadoop

Even though medical sciences are capable of diagnose the diseases like Cancer, Alzheimer’s etc, these diseases remain still incurable. Because to find the root cause of these diseases, the medical researchers need to analyze patient's medical records, various supportive information, climatic conditions in which they lived in, across different geographical locations. And these a need a platform where a huge volume of data can be stored and analyzed. Hadoop is a powerful platform that allows us to store huge...

By Kislay KomalApache Hadoop, Data EngineeringBig Data, Big Data Analysis, Future of Big Data, Future of Big Data Analysis, Future of Big Data Analysis Using Hadoop, HadoopComments Off

13JunJune 13, 2016

Big Data Explosion

After Kerala's Puttingal Devi Temple fire tragedy, we can visualize sudden data explosion in all digital media. After that tragic incident, huge amount of data are generated in the form of text, voice, photo, video, blogs etc. in internet via social media, news channels, e-news papers and comments, sentiments, various opinions are flooded on whether fire crackers burst should be allowed in devotional places or not. This is a classic example of Big Data where existing traditional software are...

By Kislay KomalApache Hadoop, Data EngineeringBig Data, Big Data Analysis, Big Data Explosion, Data Explosion, Future of Big Data Analysis, Future of Big Data Analysis Using Hadoop, Hadoop, Kerala Puttingal Devi Temple, Puttingal Devi Temple, Puttingal TempleComments Off

Tag - Hadoop

Few intrinsic of Apache Zookeeper and their importance

Importance of Schema Registry on Kafka Based Data Streaming Pipelines

Network Topology To Create Multi Node Hybrid Cluster For Hadoop Installation

Apache Flink – A 4G Data Processing Engine

Transfer structured data from Oracle to Hadoop storage system

Technical Leadership Training

Future of Big Data Analysis Using Hadoop

Big Data Explosion

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