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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

16MarMarch 16, 2017

How Google news is able to group similar news together

Google news uses clustering machine learning techniques to group similar kind of news or articles together. Interestingly, they don't have thousand news editors on trunk instead use the clustering techniques to forms groups of similar data based on the common characteristics. Mahout is a machine learning software from Apache community that applications leverage to analyse large sets of data. Before invention of Mahout, it was too complex to a analyse large sets of data. Mahout extensively utilize Apache Hadoop to...

By Gautam GoswamiData Engineering, Machine LearningApache Mahout, Classification, Clustering, clustering machine learning, clustering machine learning technique, community information, google news, How Google news is able to group similar news together, machine learning, machine learning techniques, Mahout, Recommendation, user informationComments Off

23FebFebruary 23, 2017

Essentially of Data Wrangling

To roll out a new software product commercially irrespective of any domain in the market, 360-degree quality check with test data is mandatory. We can correlate this with a visualized concept of a new vehicle. After completion of vehicle manufacturing, fuel has to be injected to the engine to make it operational. Once the vehicle starts moving, all the quality checks, testing get started like brake performance, mileage, comfort etc with thousands of other factors which are decided/concluded during...

By Gautam GoswamiArchitecture, Data Engineering, Storage Mechanism360-degree quality check, certify the software product, convert or map data from one raw format to another, Data Wrangling, Essentially of Data Wrangling, performance behavior, performance behavior of the product, process of data conversion from one format to another, The process of data conversion from one format to another is known as data wrangling.Comments Off

14FebFebruary 14, 2017

Semi-Structured Data

Semi-structured data lies between structured and unstructured data. Data that get stored in the traditional database system or excel sheet can be denoted as structured data and organized in COLUMNS and ROWS. Unstructured data can be considered as any data or piece of information which can't be stored in Databases/RDBMS etc. Email, Facebook comments, news paper etc. are the examples of unstructured data. Semi-structured data do not follow strict data model structure and neither raw data nor typed data in...

By Gautam GoswamiApache Hadoop, Data EngineeringAnalysing unstructured Data, bi-directional data interchange, Cassandra, client-server web application, Email, excel sheet, Facebook comments, Facebook graph, Facebook graph API, GET method, GET method in REST service, graph API, Hadoop Distributed File System, HBase, HDFS, JavaScript object notation, JSON format, JSON or XML, MongoDb, news paper, NoSQL, NoSQL Database, REST service, REST service request/response, sample unstructured data, Semi-structured data. structured data, traditional database system, transmit over wireComments Off