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17SepSeptember 17, 2017

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...

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

03JulJuly 3, 2017

Big Data Analytics in Banking Systems

Typically Banking systems are responsible to validate and verify financial transaction data, geo-location data from mobile devices, merchant data, and authorization including submission data. Data from lots of social media channels and Banking’s mainframe data center have a significant challenge to process and deliver final output. The Issue: Legacy systems are incapable of processing the data in when is in motion. Combining all different format of data is together is another challenge like structured, semi- structured and un-structured. Big data Approach:- Big data analytics...

By Kislay KomalData EngineeringBig Data, Big Data Analysis, Big Data Analytics, Big Data Analytics and Banking Systems, Big Data Analytics in Banking, Big Data Analytics in Banking Systems, Big Data in Banking SystemsComments Off

08JunJune 8, 2017

Why Lambda Architecture in Big Data Processing

Due to the exponential growth of digitization, the entire globe is creating minimum 2.5 Quintilian 2500000000000 Million) bytes of data every day and that we can denote as Big Data. Data generation is happening from everywhere starting from social media sites, various sensors, satellite, purchase transaction, Mobile, GPS signals and much more. With the advancement of technology, there is no sign of slowing down of data generation, instead it will grow in massive volume. All the major organizations, retailers,...

By Gautam GoswamiArchitecture, Data EngineeringApache Kafka, Apache Spark, Batch Data-processing Pipeline, batch layer, Big Data, Big Data Processing, big data technologies, Data ingestion, data pipeline, Data Pipelines, data processing pipeline, data warehousing systems, design framework, Digitization, fault tolerance, Flume Lambda sign λ, GPS signals, Hadoop Distributed File System, HDFS, Lambda, Lambda Architecture, Lambda Architecture in Big Data Processing, Lambda Architecture is a pluggable architecture, leveraging big data technologies, live streaming data, Mobile, Nathan Marz, persistence of data, pluggable architecture, purchase transaction, Quintilian bytes of data, satellite, sensors, Serving Layer, Speed layer, Streaming Data Pipeline, streaming data processing pipeline, streaming layer, Streaming or Speed layerComments Off

29MayMay 29, 2017

Apache Kafka, The next Generation Distributed Messaging System

In Big Data project, the main challenge is to collect an enormous volume of data. We need distributed high throughput messaging systems to overcome it. Apache Kafka is designed to address the challenge. It was originally developed at LinkedIn Corporation and later on became a part of Apache project. A Messaging System is typically responsible for transferring data from one application to another. A message is nothing but the bunch of data/information. To ingest huge volume of data into Hadoop...

By Gautam GoswamiData IngestionApache Kafka, Apache project, Big Data project, collect an enormous volume of data, distributed high throughput messaging systems, distributed messaging systems, ETL, Extraction, Hadoop Distributed File System, HDFS, high throughput, Kafka supports multi-subscribers, LinkedIn Corporation, Messaging System, multi-subscribers, next Generation Distributed Messaging System, transferring data from one application to another, Transformation and LoadingComments Off

04MayMay 4, 2017

Fog Computing

Fog computing also refer to Edge computing . Cisco Systems introduced the term "Fog Computing" and it's not the replacement of cloud computing. Ideally cloud computing points to storing and accessing data and programs over the Internet instead of local computer's hard drive or storage. The cloud is simply a metaphor for the Internet. In Fog computing, data, processing and applications are concentrated in devices at the network edge. Here devices communicate peer-to-peer so that data storage and share...

By Gautam GoswamiArchitecture, Data Engineeringaugmented reality, authentication, Cisco Systems, Cloud Computing Data Centers, compute, conserve the network bandwidth, data, data applications, data crunching, data leak, data processing, data storage, device independence, Edge computing, Fog Computing, Fog Computing is not the replacement of cloud computing, Google Glass, improve systems response time, internet latency, Internet of Things, IoT, leverage Fog computing, lower operational cost, network edge, networking services, privacy, quicker development, reduce network latency, security enhancement, storage, storing and accessing data and programs over the Internet, traditional Cloud Computing Data Centers, virtualization platform, wearable health monitoring devices, wireless network securityComments Off

18AprApril 18, 2017

Mobile Phone Authentication and Fraud

Day by day, we are getting addicted to the mobile phone especially smart phone since smart phone performs many of the functions of a computer, typically having a touch screen interface, Internet access. With the extensive growth of mobile applications, we can utilize various mobile applications starting from games to financial transaction including stock market brokerage. Many banks have launched their own mobile applications so that customer can download and start financial transactions like balance amount verification, money transfer...

By Kislay KomalData Security, MobilityData Security, Mobile Phone, Mobile Phone Authentication, Mobile Phone Authentication and Fraud, Mobile Phone Authentication and Mobile Fraud, Mobile Phone Fraud, Phone AuthenticationComments Off

05AprApril 5, 2017

Basic concept of Data Lake

The left side info graphics represents the basic concept of Data Lake where we can use the approach of ELT (Extraction, loading and then transformation) against traditional ETL (Extraction, Transformation and then loading) process. ETL process implies to traditional data warehousing system where structured data format follows (row and column). By leveraging HDFS (Hadoop Distributed File System), we can develop data lake to store any format data in order to process and analysis. Directly data can be loaded in the Lake...

By Gautam GoswamiData Engineering, Storage Mechanismbasic concept of Data Lake, concept of Data Lake, Data Lake, Data Transformation, data warehouse, data warehousing, data warehousing system, ELT, ETL, Extraction loading and transformation, Extraction Transformation and loading, fault-tolerant, Hadoop Data Lake, Hadoop Distributed File System, HDFS, how to create hadoop data lake, Semi Structured data, structured data, Traditional data warehouse, unstructured dataComments Off

22MarMarch 22, 2017

Real time data analytics helps mobile service providers to achieve aggressive advantages

Usage of smart phones has become an integral part of our daily routine. Keeping aside phone calls and SMS, we are always engaged with lots of other activities Starting from entertainment to domestic shopping, social engagement etc., by installing various types of mobile applications. Of course, mobile internet is mandatory to carry out above. Mobile service providers are facing new and difficult challenges. Due to exponential growth of customer's expectations, they need to serve accordingly with advanced mobile technology and handle...

By Gautam GoswamiData Analysis, Data Engineeringbusiness demand, Collecting the live data, Continuous data flow, Continuous data flow via memory storage, faster reaction time, faster reaction time to the customers, improve service, live data, maximize asset utilization, persist huge volume of data, Real-time data analytics, retain the most profitable customers, spikes in network traffic, store huge volume of data, Usage of smart phonesComments Off