V Model Diagram

Comments

Post a Comment

Popular posts from this blog

Hadoop Architecture version 1.x

Image
Hadoop Architecture is split into 3 categories:- Edge Node Name Node and Job Tracker Node Data Nodes and Task Tracker Nodes Client/Edge Node:   It is the interface between Hadoop cluster and outside network.For this reason, they are sometimes referred as gateway nodes. Client applications talk to the Name Node whenever they wish to locate a file, or when they want to add/copy/move/delete a file. The Name Node responds the successful requests by returning a list of relevant Data Node servers where the data lives. Usage They are often used as staging areas for data being transferred into the Hadoop cluster.  Most commonly, edge nodes are used to run client applications and cluster administration tools. Tools like Oozie, Pig, Sqoop, and management tools such as Hue and Ambari run well there.  Client Node directly controls Data Node based on information. Master/Slave:   Here, we have a concept of Master...
Read more

Hadoop Components

Components of Hadoop File System HDFS (Primary Data storage system) Amazon S3 Azure Execution Engine MapReduce - launched since Hadoop version 1 Apache Spark - launched since Hadoop version 2 (Available as part of Hortonworks and Cloudera distributions) Apache Tez - launched since Hadoop version 2 (Available as part of Hortonworks distribution but not in Cloudera) Ecosystem Tools Hive - Datawarehousing tool on HDFS (used primarily for Structured Data and to some extent for Semi-Structured Data) Pig - ETL tool where source is other system and target is HDFS (used for Structured, Semi-Structured and Unstructured Data) Sqoop - Tool for Importing/Exporting data with relational database like Oracle (used for Structured Data) Oozie - Tool for monitoring Workflows and Scheduling Jobs Zookeeper - Configuration Manager
Read more

What is Data Analytics?

Data Analytics is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to make more informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. Data Analytics refers to an assortment of applications from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it is similar to business analytics, another umbrella term for approaches to analyzing data - with the difference that the latter is oriented to business uses, while data analytics has a broader focus. Advantages of Data Analytics The advantages of Data Analytics are in boosting business performance by:- Increasing Business Revenues Improve Operational Efficiency Optimize Marketing...
Read more