Once the Namenode provides the location of the data, the client applications can interact with Data Node directly. It keeps on looking for the request to access the data. Initially, the Data Node was connected to the Name Node.
In a functional file system, the data replicates across many Data Nodes. This daemon runs on the Slave node where it stores the data in Hadoop File System. Whenever the clients request the Name Node return the list of Data Node servers where the actual data resides. This Namenode comes into the picture where the client wants to add/copy/move/ delete the file. The name node store the directory tree of all file in the file system. It is the centerpiece of the HDFS file system. The name node is a daemon that is running on the master machine. This component has two daemons namely the namenode as well as the data node. HDFS is a distributed file system that runs on master-slave technology. In the Hadoop Ecosystem, HDFS is good at Data Storage, Map Reduce is good at Data Processing, and YARN is good at task dividing.Īre you new to the concept of Hadoop?, then check out our post on What is Hadoop? To process any data, the client submits the data and program to Hadoop. Hadoop does distribute processing of huge data across the cluster of commodity of servers that work on multiple servers simultaneously. Apache Spark that has been talked about most about the technology was born out of Hadoop.
Hadoop has also given birth to countless innovations in the big data space.
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Hadoop has overcome its dependency as it does not rely on hardware but instead achieves high availability and also detects the point of failures in the software itself. Hadoop YARN is another component of the Hadoop framework that is good at managing the resources amongst applications running in a cluster and scheduling a task. The Hadoop map-reduce is a processing unit in Hadoop that processes the data in parallel. This platform is capable of storing a massive amount of data in a distributed manner in HDFS. This file system is highly available and fault-tolerant to its users. Many data-intensive fields of business such as banking, insurance, retail, transportation, and telecommunications may use these sophisticated software applications to clean up a database’s information.Ĥ.Apache Hadoop is a framework that can store and process huge amounts of unstructured data ranging from terabytes to petabytes. This error can be because the data is wrong, incomplete, formatted incorrectly, or is a duplicate copy of another entry. Data scrubbing:Rĭata scrubbing is the process of detecting and removing or correcting any information in a darticle tabase that has some sort of error. This can occur within a single set of records or between multiple sets of data that need to be merged or that will work together 3.
During this process, records are checked for accuracy and consistency, and they are either corrected or deleted as necessary. Data cleansing:ĭata cleansing is the process of ensuring that a set of data is correct and accurate. It’s a process of integrating the data from multiple data sources into a single operational data set Data Sources can be homogenous/heterogeneousĪ) Horizontal Merging: Process of joining the records horizontally when the 2 sources are having different data definitions.Ĭolumns in the Dept Table– Dept no, Dname, Locī) Vertical Merging: Process of joining the records vertically when the 2 sources are having similar data structuresįrequently Asked Informatica Interview Questions 2. A staging is a temporary memory or buffer where the following data transformation activities take place Transforming the data from one format to the client required format/ business standard format. ERP Sources: SAP R/3, Peoplesoft, JDEdwards, BAAN, RAMCO systemsĮnhance your IT skills and proficiency in Data Warehousing by taking up the Informatica Training.Other sources: MS-Access, MS-Excel, MQ Series, TIBCO.File sources: XML, COBOL, Flat files, weblog.Relational Systems-Oracle, SQL Server, DB2, Sybase, Informix, Redbrick.It is the process of reading the data from the following operational source systems (External to the organization and Internal databases) Two types of ETL’s used in implementing data acquisition.ġ) A) Code Based ETL: An ETL application can be developed using programming languages such as PL/SQLī) GUI Based ETL: An application can be designed with a simple GUI interface, point & click techniquesĮx: Informatica, Datastage, Data junction, Abintio, Data services, Data manager, Oracle data integrator (OWB), SSIS (SQL Server Integration Service) Data Extraction: It is a process of extracting relevant business information from multiple operational source systems, transforming the data into a homogenous format and loading into the DWH/Datamart. In DWH terminology, Extraction, Transformation, Loading (ETL) is called as Data Acquisition
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