AWS Data Pipeline Tutorial Building A data Pipeline From Scratch

AWS Data Pipeline Tutorial Building A data Pipeline From Scratch AWS Data Pipeline Tutorial A Data Workflow Orchestration Service Back Home Categories Online Courses Mock Interviews Webinars NEW Community Write for Us Categories Artificial Intelligence AI vs Machine Learning vs Deep LearningMachine Learning AlgorithmsArtificial Intelligence TutorialWhat is Deep LearningDeep Learning TutorialInstall TensorFlowDeep Learning with PythonBackpropagationTensorFlow TutorialConvolutional Neural Network TutorialVIEW ALL BI and Visualization What is TableauTableau TutorialTableau Interview QuestionsWhat is InformaticaInformatica Interview QuestionsPower BI TutorialPower BI Interview QuestionsOLTP vs OLAPQlikView TutorialAdvanced Excel Formulas TutorialVIEW ALL Big Data What is HadoopHadoop ArchitectureHadoop TutorialHadoop Interview QuestionsHadoop EcosystemData Science vs Big Data vs Data AnalyticsWhat is Big DataMapReduce TutorialPig TutorialSpark TutorialSpark Interview QuestionsBig Data TutorialHive TutorialVIEW ALL Blockchain Blockchain TutorialWhat is BlockchainHyperledger FabricWhat Is EthereumEthereum TutorialB lockchain ApplicationsSolidity TutorialBlockchain ProgrammingHow Blockchain WorksVIEW ALL Cloud Computing What is AWSAWS TutorialAWS CertificationAzure Interview QuestionsAzure TutorialWhat Is Cloud ComputingWhat Is SalesforceIoT TutorialSalesforce TutorialSalesforce Interview QuestionsVIEW ALL Cyber Security Cloud SecurityWhat is CryptographyNmap TutorialSQL Injection AttacksHow To Install Kali LinuxHow to become an Ethical Hacker?Footprinting in Ethical HackingNetwork Scanning for Ethical HackingARP SpoofingApplication SecurityVIEW ALL Data Science Python Pandas TutorialWhat is Machine LearningMachine Learning TutorialMachine Learning ProjectsMachine Learning Interview QuestionsWhat Is Data ScienceSAS TutorialR TutorialData Science ProjectsHow to become a data scientistData Science Interview QuestionsData Scientist SalaryVIEW ALL Data Warehousing and ETL What is Data WarehouseDimension Table in Data WarehousingData Warehousing Interview QuestionsData warehouse architectureTalend T utorialTalend ETL ToolTalend Interview QuestionsFact Table and its TypesInformatica TransformationsInformatica TutorialVIEW ALL Databases What is MySQLMySQL Data TypesSQL JoinsSQL Data TypesWhat is MongoDBMongoDB Interview QuestionsMySQL TutorialSQL Interview QuestionsSQL CommandsMySQL Interview QuestionsVIEW ALL DevOps What is DevOpsDevOps vs AgileDevOps ToolsDevOps TutorialHow To Become A DevOps EngineerDevOps Interview QuestionsWhat Is DockerDocker TutorialDocker Interview QuestionsWhat Is ChefWhat Is KubernetesKubernetes TutorialVIEW ALL Front End Web Development What is JavaScript â€" All You Need To Know About JavaScriptJavaScript TutorialJavaScript Interview QuestionsJavaScript FrameworksAngular TutorialAngular Interview QuestionsWhat is REST API?React TutorialReact vs AngularjQuery TutorialNode TutorialReact Interview QuestionsVIEW ALL Mobile Development Android TutorialAndroid Interview QuestionsAndroid ArchitectureAndroid SQLite DatabaseProgramming A Data Workflow Orchest ration Service Last updated on May 15,2020 10.7K Views Archana Choudary Bookmark 3 / 5 Blog from AWS Database Services Become a Certified Professional AWS Data Pipeline TutorialWith advancement in technologies ease of connectivity, the amount of data getting generated is skyrocketing.Buried deep within this mountain of data is the captive intelligence that companies can use to expand and improve their business. Companies need to move, sort, filter, reformat, analyze, and report data in order to derive value from it. They might have to do this repetitively and at a rapid pace, to remainsteadfast in the market. AWS Data Pipeline service by Amazon is the perfectsolution.Lets take a look at the topics covered in this AWS Data Pipeline tutorial:Need for AWS Data PipelineWhat is AWS Data Pipeline?Benefits of AWS Data PipelineAWS Data Pipeline componentsDemo Export data from DynamoDbNeed for AWS Data PipelineData is growing exponentially and that too at a faster pace.Companies of a ll sizes are realizing that managing, processing, storing migrating the data has become more complicated time-consumingthan in the past. So, listed below are some of the issues that companies are facing with ever increasing data:Bulk amount of Data: There is a lot of raw unprocessed data. There are log files, demographic data, data collected from sensors, transaction histories lot more.Variety of formats: Data is available in multiple formats. Converting unstructured data to a compatibleformat is a complex time-consuming task.Differentdata stores: There are a varietyof data storageoptions. Companies have their own data warehouse, cloud-basedstorage like Amazon S3, Amazon Relational Database Service(RDS) database servers running on EC2 instances.Time-consuming costly: Managing bulk of data is time-consuming a very expensive. A lot of money is to be spent on transform, store process data.All these factors make it more complex challenging for companies to manage data on their o wn. This is where AWS Data Pipeline can be useful. Itmakes it easier for users to integrate data that is spread across multiple AWS services and analyze it from a single location. So, through this AWS Data Pipeline Tutorial lets explore Data Pipeline and its components.AWS Data Pipeline Tutorial | AWS Tutorial For Beginners | EdurekaThis video will help you understand how to process, store analyze data with ease from the same location using AWS Data Pipeline.What is AWS Data Pipeline?AWS Data Pipeline is a web service that helps you reliably process and move data between different AWS compute and storage services, as well as on-premises data sources, at specified intervals.With AWS Data Pipeline you can easily access data from the location where it is stored, transform process it at scale, and efficiently transfer the results toAWS services such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR. It allows you to create complex data processing workloads that are fault toler ant, repeatable, and highly available.Now why choose AWS Data Pipeline?Benefits of AWS Data PipelineProvides a drag-and-drop console within the AWS interfaceAWS Data Pipeline is built on a distributed, highly available infrastructure designed for fault tolerant execution of your activitiesIt provides a variety of features such as scheduling, dependency tracking, and error handlingAWS Data Pipeline makes it equally easy to dispatch work to one machine or many, in serial or parallelAWS Data Pipeline is inexpensive to use and is billed at a low monthly rateOffers full control over the computational resources that execute your data pipeline logic So, with benefits out of the way, lets take a look at different components of AWS Data Pipeline how they work together to manage your data. Want To Take Your 'Cloud' Knowledge To Next Level? Get Cloud Certified Today! Components of AWS Data PipelineAWS Data Pipeline is a web service that you can use to automate the movement and transformation of data. You can define data-driven workflows so that tasks can be dependent on the successful completion of previous tasks.You define the parameters of your data transformations and AWS Data Pipeline enforces the logic that youve set up. Fig 1: AWS Data Pipeline AWS Data Pipeline Tutorial EdurekaBasically, you always begin designing a pipeline by selecting the data nodes. Then data pipeline works with compute services to transform the data. Most of the time a lot of extra data is generated during this step. So optionally, you can have output data nodes, where the results of transforming the data can be stored accessed from.Data Nodes:In AWS Data Pipeline, a data node defines the location and type of data that a pipeline activity uses as input or output. It supports data nodes like:DynamoDBDataNodeSqlDataNodeRedshiftDataNodeS3DataNodeNow, lets consider a real-time example to understand other components.Use Case:Collect data from different data sources, perform Amazon Elastic Map Reduce(EMR) analysis generate weekly reports.In this use case, we are designing a pipeline to extract data from data sources like Amazon S3 DynamoDB to perform EMR analysis daily generate weekly reports on data.Now the words that I italicized are called activities. Optionally, for these activities to run we can add preconditions.Activities: An activity is a pipeline component that defines the work to performon schedule using a computational resource and typically input and output data nodes. Examples of activities are:Moving data from one location to anotherRunning Hive queriesGenerating Amazon EMR reportsPreconditions: A precondition is a pipeline component containing conditional statements that must be true before an activity can run.Check whether source data is present before a pipeline activity attempts to copy itIf or not a respective database table existsResources: Aresource is a computational resource that performs the work that a pipeline activity specifies.An EC2 instanc e that performs the work defined by a pipeline activityAn Amazon EMR cluster that performs the work defined by a pipeline activityFinally, we have a component called actions.Actions: Actions are steps that a pipeline component takes when certain events occur, such as success, failure, or late activities.Send an SNS notification to a topic based onsuccess, failure, or late activitiesTrigger the cancellation of a pending or unfinished activity, resource, or data nodeNow that you have the basic idea of AWS Data Pipeline its components, lets see how it works.Demo on AWS Data PipelineIn this demo part of AWS Data Pipeline Tutorial article, we aregoing to see how tocopy the contents of a DynamoDB table to S3 Bucket.AWS Data Pipeline triggers an action to launch EMR cluster with multiple EC2 instances(make sure to terminate them after you are done to avoid charges). EMR cluster picks up the data from dynamoDB and writes to S3 bucket.Creating an AWSData PipelineStep1:Create a DynamoDB tabl e with sample test data.Step2:Create a S3 bucket for the DynamoDB tables data to be copied.Step3:Access the AWS Data Pipeline console from your AWS Management Console click on Get Started to create a data pipeline.Step4: Create a data pipeline. Give your pipeline a suitable name appropriate description. Specify source destination data node paths. Schedule your data pipeine click on activate.Monitoring TestingStep5: In theList Pipelines you can see the status as WAITING FOR RUNNER.Step6:After a few minutes you can see the status has again changed to RUNNING. At this point, if you go to EC2 console, you cansee two new instances created automatically. This is because of the EMR cluster triggered by Pipeline.Step7: After finishing, you can access S3 bucket and find out if the .txt file is created. It contains the DynamoDB tables contents. Download it an open in a text editor.So, now you know how to use AWS Data Pipeline to export data from DynamoDB. Similarly, by reversing source destination you can import data to DynamoDB from S3.Go ahead and explore!So this is it!I hope this AWS Data Pipeline Tutorial was informative and added value to your knowledge.If you are interested to take your knowledge on Amazon Web Services to the next level then enroll for the AWS Architect Certification Training course by Edureka.Got a question for us? Please mention it in the comments section of AWS Data Pipeline and we will get back to you.Recommended videos for you Architecting in Cloud-II Watch Now Power The Hadoop Cluster With AWS Cloud Watch Now Efficient Disaster Recovery with Cloud Computing Watch Now Microsoft Azure Tutorial Step-By-Step Tutorial In Azure Watch Now Cloud Computing with AWS II Watch Now Building Scalable Application on Cloud Watch Now AWS Certifications All You Need To Know Watch Now What Is Cloud Computing? 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