DOCSLIB.ORG

Amazon Glue Studio User Guide Amazon Glue Studio User Guide

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Amazon Glue Studio User Guide Amazon Glue Studio User Guide Amazon Glue Studio User Guide Amazon Glue Studio User Guide Amazon Glue Studio: User Guide Amazon Glue Studio User Guide Table of Contents What is Amazon Glue Studio? .............................................................................................................. 1 Features of Amazon Glue Studio .................................................................................................. 2 Visual job editor ................................................................................................................ 2 Job script code editor ......................................................................................................... 2 Job performance dashboard ................................................................................................ 3 Support for dataset partitioning .......................................................................................... 3 When should I use Amazon Glue Studio? ...................................................................................... 3 Accessing Amazon Glue Studio .................................................................................................... 3 Pricing for Amazon Glue Studio ................................................................................................... 4 Setting up ......................................................................................................................................... 5 Sign up for Amazon ................................................................................................................... 5 Create an IAM administrator user ................................................................................................. 5 Signing in as an IAM user ........................................................................................................... 6 IAM permissions needed for the Amazon Glue Studio user .............................................................. 6 Amazon Glue service permissions ......................................................................................... 6 Amazon CloudWatch permissions ......................................................................................... 7 Job-related permissions .............................................................................................................. 7 Data source and data target permissions ............................................................................... 7 Permissions required for deleting jobs .................................................................................. 8 Amazon Key Management Service permissions ....................................................................... 8 Additional permissions when using connectors ....................................................................... 8 Set up IAM permissions for Amazon Glue Studio ............................................................................ 8 Configuring a VPC for your ETL job .............................................................................................. 9 Populate the Amazon Glue Data Catalog ...................................................................................... 9 Tutorial: Getting started .................................................................................................................... 10 Prerequisites ............................................................................................................................ 10 Step 1: Start the job creation process ......................................................................................... 10 Step 2: Edit the data source node in the job diagram .................................................................... 11 Step 3: Edit the transform node of the job .................................................................................. 12 Step 4: Edit the data target node of the job ................................................................................ 12 Step 5: View the job script ........................................................................................................ 13 Step 6: Specify the job details and save the job ........................................................................... 13 Step 7: Run the job .................................................................................................................. 14 Next steps ............................................................................................................................... 14 Creating jobs ................................................................................................................................... 15 Start the job creation process .................................................................................................... 15 Create jobs that use a connector ................................................................................................ 16 Next steps for creating a job in Amazon Glue Studio .................................................................... 16 Editing jobs ..................................................................................................................................... 17 Accessing the job diagram editor ................................................................................................ 17 Job editor features ................................................................................................................... 17 Using schema previews in the visual job editor .................................................................... 18 Using data previews in the visual job editor ......................................................................... 18 Restrictions when using data previews ................................................................................ 19 Editing the data source node ..................................................................................................... 19 Using Data Catalog tables for the data source ..................................................................... 20 Using a connector for the data source ................................................................................ 21 Using files in Amazon S3 for the data source ....................................................................... 21 Using a streaming data source ........................................................................................... 22 Editing the data transform node ................................................................................................ 23 Overview of mappings and transforms ................................................................................ 23 Using ApplyMapping to remap data property keys ................................................................ 24 Using SelectFields to remove most data property keys .......................................................... 25 Using DropFields to keep most data property keys ............................................................... 25 iii Amazon Glue Studio User Guide Renaming a field in the dataset ......................................................................................... 26 Using Spigot to sample your dataset .................................................................................. 27 Joining datasets ............................................................................................................... 27 Using SplitFields to split a dataset into two ......................................................................... 29 Overview of SelectFromCollection transform ........................................................................ 29 Using SelectFromCollection to choose which dataset to keep ................................................. 30 Filtering keys within a dataset ........................................................................................... 30 Find and fill missing values in a dataset .............................................................................. 31 Using a SQL query to transform data ................................................................................. 32 Creating a custom transformation ...................................................................................... 33 Configuring data target nodes ................................................................................................... 36 Overview of data target options ........................................................................................ 36 Editing the data target node ............................................................................................. 37 Editing or uploading a job script ................................................................................................ 39 Creating and editing Scala scripts in Amazon Glue Studio ...................................................... 40 Creating and editing Python shell jobs in Amazon Glue Studio ............................................... 41 Adding nodes to the job diagram ............................................................................................... 42 Changing the parent nodes for a node in the job diagram ............................................................. 42 Deleting nodes from the job diagram ......................................................................................... 43 Using connectors and connections .....................................................................................................
Load more

Recommended publications
  • ARCHIVED: Lambda Architecture for Batch and Stream Processing
    Lambda Architecture for Batch and Stream Processing October 2018 This paper has been archived. For the latest technical content about Lambda architecture, see the AWS Whitepapers & Guides page: https://aws.amazon.com/whitepapers Archived © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offerings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without warranty of any kind, whether express or implied. This document does not create any warranties, representations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. Archived Contents Introduction 1 Overview 2 Data Ingestion 3 Data Transformation 4 Data Analysis 5 Visualization 6 Security 6 Getting Started 7 Conclusion 7 Contributors 7 Further Reading 8 Document Revisions 8 Archived Abstract Lambda architecture is a data-processing design pattern to handle massive quantities of data and integrate batch and real-time processing within a single framework. (Lambda architecture is distinct from and should not be confused with the AWS Lambda compute service.) This paper covers the building blocks of a unified architectural pattern that unifies stream (real-time) and batch processing.
    [Show full text]
  • ETL Options Across Cloud Simplifying the Data Preparation, Data Transformation & Data Exploration for Augmented Analytics X-Clouds
    ETL options across cloud Simplifying the data preparation, data transformation & data exploration for augmented analytics x-clouds A Mindtree White paper| October, 2020 Scope of this white paper Would you like to revolutionize and uncover the unlimited power of data from various sources and productionalize your AI/ML models for amazing recommendations? Do you want to consolidate multiple formats & multiple data sources to have powerful big data (volume, variety, veracity and velocity) platform that has faster, easier to manage data lineage with repeatable advanced analytics processes, and receive billions of recommendations from PB scale data? Would you like to see the utmost bigdata features, functions, and 'augmented next-gen analytics' best practices for achieving data-driven rich, deeper insights in a very near-real-time (streaming) or batch model? “The building blocks for achieving that goal is to set up a flexible, insights & low-latency search infused Enterprise DataLake (DWH) or ‘augmented analytics’ platform that should include a data driven ETL & ELT- batch and streaming unified platform; with accelerated practices of data preparation, data enrichment, data transformation and data governance & exploration solutions”. Many organizations that are trying to become data-driven or insights oriented organizations in the near future have started setting up the environment and culture needed for building and using the power of advanced analytics for their business to make swift recommendations and business decisions. Augmented analytics platform enhances the quality and availability of the services for growing the business footprints. To be a harbinger and stay ahead in the current competitive world, there are massive requirements to have the capability for getting deeper insights, customer 360 degree preferences & recommendations, and integration of business systems across multi-channels (social media/,etc.) for seamless user onboarding/marketing.
    [Show full text]
  • Serverless Linear Algebra
    Serverless Linear Algebra Vaishaal Shankar Karl Krauth Kailas Vodrahalli UC Berkeley UC Berkeley Stanford Qifan Pu Benjamin Recht Ion Stoica Google UC Berkeley UC Berkeley Jonathan Ragan-Kelley Eric Jonas Shivaram Venkataraman MIT CSAIL University of Chicago University of Wisconsin-Madison ABSTRACT 1 INTRODUCTION Datacenter disaggregation provides numerous benets to As cloud providers push for datacenter disaggregation [18], both the datacenter operator and the application designer. we see a shift in distributed computing towards greater elas- However switching from the server-centric model to a dis- ticity. Datacenter disaggregation provides benets to both aggregated model requires developing new programming the datacenter operator and application designer. By decou- abstractions that can achieve high performance while ben- pling resources (CPU, RAM, SSD), datacenter operators can eting from the greater elasticity. To explore the limits of perform ecient bin-packing and maintain high utilization datacenter disaggregation, we study an application area that regardless of application logic (e.g., an application using all near-maximally benets from current server-centric datacen- the cores on a machine & using only 5% of RAM). Similarly ters: dense linear algebra. We build NumPyWren, a system the application designer has the exibility to provision and for linear algebra built on a disaggregated serverless pro- deprovision resources on demand during application runtime gramming model, and LAmbdaPACK, a companion domain- (e.g., asking for
    [Show full text]
  • Numpywren: Serverless Linear Algebra
    numpywren: serverless linear algebra Vaishaal Shankar Karl Krauth Qifan Pu Eric Jonas Shivaram Venkataraman Ion Stoica Benjamin Recht Jonathan Ragan-Kelley Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2018-137 http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-137.html October 22, 2018 Copyright © 2018, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Acknowledgement This research is supported in part by ONR awards N00014-17-1-2191, N00014-17-1-2401, and N00014-18-1-2833, the DARPA Assured Autonomy (FA8750-18-C-0101) and Lagrange (W911NF-16-1-0552) programs, Amazon AWS AI Research Award, NSF CISE Expeditions Award CCF-1730628 and gifts from Alibaba, Amazon Web Services, Ant Financial, Arm, CapitalOne, Ericsson, Facebook, Google, Huawei, Intel, Microsoft, Scotiabank, Splunk and VMware as well as by NSF grant DGE- 1106400. We would like to thank Horia Mania, Alyssa Morrow and Esther Rolf for helpful comments while writing this paper. numpywren: Serverless Linear Algebra Vaishaal Shankar1, Karl Krauth1, Qifan Pu1, Eric Jonas1, Shivaram Venkataraman2, Ion Stoica1, Benjamin Recht1, and Jonathan Ragan-Kelley1 1 UC Berkeley 2UW Madison 100k 8 Abstract Working Set Size Linear algebra operations are widely used in scientific 75k Maximum Threads 6 computing and machine learning applications.
    [Show full text]
  • An Overview on Amazon Rekognition Technology
    California State University, San Bernardino CSUSB ScholarWorks Electronic Theses, Projects, and Dissertations Office of aduateGr Studies 5-2021 An Overview on Amazon Rekognition Technology Raghavendra Kumar Indla Follow this and additional works at: https://scholarworks.lib.csusb.edu/etd Part of the Business Intelligence Commons Recommended Citation Indla, Raghavendra Kumar, "An Overview on Amazon Rekognition Technology" (2021). Electronic Theses, Projects, and Dissertations. 1263. https://scholarworks.lib.csusb.edu/etd/1263 This Thesis is brought to you for free and open access by the Office of aduateGr Studies at CSUSB ScholarWorks. It has been accepted for inclusion in Electronic Theses, Projects, and Dissertations by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [email protected]. AN OVERVIEW ON AMAZON REKOGNITION TECHNOLOGY A Project Presented to the Faculty of California State University, San Bernardino In Partial Fulfillment of the Requirements for the Degree Master of Science in Information Systems and Technology: Business Intelligence by Raghavendra Kumar Indla May 2021 AN OVERVIEW ON AMAZON REKOGNITION TECHNOLOGY A Project Presented to the Faculty of California State University, San Bernardino by Raghavendra Kumar Indla May 2021 Approved by: Dr. Benjamin Becerra, Committee Chair Dr. Conrad Shayo, Committee Member Dr. Javad Varzandeh, Committee Member © 2021 Raghavendra Kumar Indla ABSTRACT The Covid-19 pandemic has disrupted the daily operations of many businesses due to which they were forced to follow the guidelines set by the local, state, and federal government to reduce the spread of the Covid-19 virus. This project focused on how few businesses that resumed their operations during the onset of Covid-19 integrated Amazon Web Services Rekognition technology to comply with government rules and regulations.
    [Show full text]
  • Whitepaper Title
    Cloud Native Data Virtualization on AWS Data Virtualization Guide September 2020 Notices Customers are responsible for making their own independent assessment of the information in this document. This document: (a) is for informational purposes only, (b) represents current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS products or services are provided “as is” without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved. Contents Introduction .......................................................................................................................... 1 Data Integration Challenges ................................................................................................ 3 Business Case ..................................................................................................................... 4 Data Virtualization Architecture ........................................................................................... 5 Custom Data Virtualization Solutions ................................................................................. 7 Commercial Data Virtualization Solutions
    [Show full text]
  • Big Data Analytics Options on AWS
    Big Data Analytics Options on AWS December 2018 This paper has been archived. For the latest technical information, see https://docs.aws.amazon.com/whitepapers/latest/big-data- analytics-options/welcome.html Archived © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offerings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without warranty of any kind, whether express or implied. This document does not create any warranties, representations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. Archived Contents Introduction 5 The AWS Advantage in Big Data Analytics 5 Amazon Kinesis 7 AWS Lambda 11 Amazon EMR 14 AWS Glue 20 Amazon Machine Learning 22 Amazon DynamoDB 25 Amazon Redshift 29 Amazon Elasticsearch Service 33 Amazon QuickSight 37 Amazon EC2 40 Amazon Athena 42 Solving Big Data Problems on AWS 45 Example 1: Queries against an Amazon S3 Data Lake 47 Example 2: Capturing and Analyzing Sensor Data 49 Example 3: Sentiment Analysis of Social
    [Show full text]
  • Aws Data Pipeline Developer Guide
    Aws Data Pipeline Developer Guide Axel spreads her patriot handily, self-displeased and sportless. Goober contrive prolixly if flexile Sandor deject or scribe. Which Andreas vituperating so diametrically that Tabby tippings her stake? With popular etl within a managed service logs into aws data pipeline developer guide to guide you can be queried via data analysts. Latest advances in developer guide waf and. Awsgluejob Resources hashicorpaws Terraform Registry. OLAP databases that push the soil of precomputation to constitute extreme. AWS Data Pipeline Developer Guide API Version 2012-10-29 AWS Data Pipeline Developer Guide AWS Data Pipeline Developer Guide Copyright 2014. Your knowledge of the leading public cloud and aws data preparation step synchronizes the kinesis analytics now! Consuming streaming data Twitter Developer. Today on performance of a hive, processing pipeline using different tables, and many different as well with new data will assume that requires setting up! AWS Data Pipeline Tutorial What is Examples Diagnostics. Precomputation data developer guide if you can be. Passionate software project to mitigate these issues based on platforms to guide aws data developer. AWS Developer Tools Links to developer tools SDKs IDE toolkits and command line tools for developing and managing AWS applications AWS Whitepapers. Many active data engineers commit information, and accessible for usage will receive notifications when a single destination databases, and add a free. Aws Glue Python Example viaggio-giapponeit. Lambda, including AWS Data Pipeline, is contained within one task runner. Next monthly fee for government agencies on. Preparation of systemtechnical documentation as per ISO. AWS Data Pipeline is a strong-based data workflow service that helps you process cannot move about between.
    [Show full text]
  • Lambda Architecture for Batch and Stream Processing
    Lambda Architecture for Batch and Stream Processing October 2018 © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offerings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without warranty of any kind, whether express or implied. This document does not create any warranties, representations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. Contents Introduction 1 Overview 2 Data Ingestion 3 Data Transformation 4 Data Analysis 5 Visualization 6 Security 6 Getting Started 7 Conclusion 7 Contributors 7 Further Reading 8 Document Revisions 8 Abstract Lambda architecture is a data-processing design pattern to handle massive quantities of data and integrate batch and real-time processing within a single framework. (Lambda architecture is distinct from and should not be confused with the AWS Lambda compute service.) This paper covers the building blocks of a unified architectural pattern that unifies stream (real-time) and batch processing. After reading this paper, you should have a good idea of how to set up and deploy the components of a typical Lambda architecture on AWS.
    [Show full text]
  • Towards Practical Serverless Analytics
    Towards Practical Serverless Analytics Qifan Pu Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2019-105 http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-105.html June 25, 2019 Copyright © 2019, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Towards Practical Serverless Analytics by Qifan Pu A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Ion Stoica, Chair Professor Joeseph Hellerstein Professor Fernando Perez Spring 2019 Towards Practical Serverless Analytics Copyright 2019 by Qifan Pu 1 Abstract Towards Practical Serverless Analytics by Qifan Pu Doctor of Philosophy in Computer Science University of California, Berkeley Professor Ion Stoica, Chair Distributed computing remains inaccessible to a large number of users, in spite of many open source platforms and extensive commercial offerings. Even though many distributed computation frameworks have moved into the cloud, many users are still left to struggle with complex cluster management and configuration tools there. In this thesis, we argue that cloud stateless functions represent a viable platform for these users, eliminating cluster management overhead, fulfilling the promise of elasticity.
    [Show full text]
  • Scalable Analytics on Serverless Infrastructure
    Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure Qifan Pu (UC Berkeley), Shivaram Venkataraman (UW Madison), Ion Stoica (UC Berkeley) Abstract workload requirements can be dynamically matched with Serverless computing is poised to fulfill the long-held continuous scaling. promise of transparent elasticity and millisecond-level pric- Fine-grained elasticity in serverless platforms is natu- ing. To achieve this goal, service providers impose a fine- rally useful for on-demand applications like creating image grained computational model where every function has a thumbnails [18] or processing streaming events [26]. How- maximum duration, a fixed amount of memory and no persis- ever, we observe such elasticity also plays an important role tent local storage. We observe that the fine-grained elasticity for data analytics workloads. Consider for example an ad- of serverless is key to achieve high utilization for general hoc data analysis job exemplified by say TPC-DS query computations such as analytics workloads, but that resource 95 [34] (See section 5 for more details). This query con- limits make it challenging to implement such applications as sists of eight stages and the amount of input data at each they need to move large amounts of data between functions stage varies from 0.8MB to 66GB. With a cluster of virtual that don’t overlap in time. In this paper, we present Locus, machines users would need to size the cluster to handle the a serverless analytics system that judiciously combines (1) largest stage leaving resources idle during other stages. Us- cheap but slow storage with (2) fast but expensive storage, ing a serverless platform can improve resource utilization as to achieve good performance while remaining cost-efficient.
    [Show full text]
  • AWS Solutions Constructs AWS Solutions AWS Solutions Constructs AWS Solutions
    AWS Solutions Constructs AWS Solutions AWS Solutions Constructs AWS Solutions AWS Solutions Constructs: AWS Solutions Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. AWS Solutions Constructs AWS Solutions Table of Contents Overview ........................................................................................................................................... 1 What is AWS Solutions Constructs? .............................................................................................. 1 Why use AWS Solutions Constructs? ............................................................................................. 1 Getting Started .................................................................................................................................. 2 Prerequisites .............................................................................................................................. 2 Installing the AWS CDK ............................................................................................................... 2 Working with AWS Solutions Constructs ......................................................................................
    [Show full text]