DOCSLIB.ORG

Modernize Your Data Warehouse

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Modernize Your Data Warehouse Modernize your data warehouse Isabel Huerga Ayza Senior Developer Advocate © 2020, Amazon Web Services, Inc. or its Affiliates. Agenda • Cloud data warehouses • Amazon Redshift architecture and features • Accelerating your data warehouse migration • Demo © 2020, Amazon Web Services, Inc. or its Affiliates. Benefits of a cloud data warehouse Get insights Scale, elasticity, Increases in No infrastructure from all your data and flexibility productivity costs and pay-as-you-go © 2020, Amazon Web Services, Inc. or its Affiliates. © 2020, Amazon Web Services, Inc. or its Affiliates. JustGiving Supports 24 Million Users on Charity Site Using AWS • Needed a new platform to support general “ Using the new AWS tools, we can operations and new analytics service extract much finer-grained data points • Moved to AWS, using a wide range of services based on millions of donations and • Can scale system faster in response to billions of visits, and then use that unanticipated spikes in traffic information to provide a better platform • Receives query results in seconds compared to 30 for our visitors. minutes under old system Richard Atkinson Chief Information Officer, JustGiving • Obtains deeper insights into billions of data points, using information to deliver better services JustGiving is a major online platform that supports charitable giving. The organization is based in London.” © 2020, Amazon Web Services, Inc. or its Affiliates. Amazon Redshift Benefits Integrated catalog & security Massively parallel processing Usage-based pricing Exabyte data lake querying Columnar data storage Predictable costs Result caching Virtually unlimited AWS-grade security Easy to provision & manage elastic linear scaling Certifications such as SOC, PCI, DSS, ISO, Automated administrative tasks FedRRAMP, HIPAA © 2020, Amazon Web Services, Inc. or its Affiliates. Data warehouse (business data) Amazon Redshift Data lake (event data) Customers moving to data lake architectures Amazon Redshift enables you to have a lake house approach © 2020, Amazon Web Services, Inc. or its Affiliates. Amazon Redshift federated query Queries on Amazon RDS and Amazon Aurora PostgreSQL databases JDBC/ODBC Analytics on live data without data movement Unified analytics across data warehouse, data lake & operational databases Flexible and easy way to ingest data Performant and secure access to data © 2020, Amazon Web Services, Inc. or its Affiliates. Redshift Cluster Architecture SQL Clients / BI Tools • Leader node • SQL endpoint JDBC/ODBC • Stores metadata • Coordinates parallel SQL processing & Leader ML optimizations node • Leader node is free with 2+ nodes • Compute nodes Compute Compute Compute • Local, columnar storage node node node • Executes queries in parallel Spectrum • Load, unload, backup, restore from S3 Load … … … … … Redshift • Amazon Redshift Spectrum nodes UnloadQuery ... • Execute queries directly against data lake Backup 1 2 3 4 N Restore Amazon S3 Exabyte-scale object storage © 2020, Amazon Web Services, Inc. or its Affiliates. Redshift Instance Types Amazon Redshift analytics—RA3 (new) A Redshift cluster can have up to128 ds2.8xlarge • Solid-state disks + Amazon S3 or RA3.16xlarge nodes (i.e. 2PB or 8 PB of local or • Amazon Redshift Managed Storage (RMS) managed storage, respectively) & can support Dense compute DC2 EBs of data with its Redshift Lakehouse feature • Solid-state disks Dense storage DS2 • Magnetic disks Instance type Disk type Size Memory # CPUs # Slices RA3 4xlarge RMS Scales to 64 TB 96 GB 12 4 RA3 16xlarge RMS Scales to 64 TB 384 GB 48 16 DC2 large SSD 160 GB 16 GB 2 2 DC2 8xlarge SSD 2.56 TB 244 GB 32 16 DS2 xlarge Magnetic 2 TB 32 GB 4 2 DS2 8xlarge Magnetic 16 TB 244 GB 36 16 © 2020, Amazon Web Services, Inc. or its Affiliates. Evolving Architecture RA3 Managed Storage SQL Clients / BI Tools Amazon Redshift Managed Storage JDBC/ODBC • Pay separately for storage and compute Leader node • Large high-speed SSD backed cache • Automatic scaling (up to 64TB/instance) Compute Compute Compute node node node • Supports up to 8.2PB of cluster storage Amazon Redshift Managed Storage Exabyte-scale object storage © 2020, Amazon Web Services, Inc. or its Affiliates. Evolution – shared nothing to disaggregated compute Local storage has enabled the fastest Compute Compute Compute Compute Cloud-based DWs node node node node Shared storage enables flexibility at the cost of performance What if we could get the benefits of both without a network performance penalty? © 2020, Amazon Web Services, Inc. or its Affiliates. AQUA: Advanced Query Accelerator Preview! ComputeCompute ComputeCompute ComputeCompute New distributed & hardware-accelerated processing ComputeComputeClusters ComputeComputeClusters ComputeComputeClusters RedshiftClustersClusters RedshiftClustersClusters RedshiftClustersClusters layer ClusterClusters ClusterClusters ClusterClusters With AQUA, Amazon Redshift is up to 10x faster than any other cloud data warehouse, no extra cost AQUA AQUA AQUA AQUA node node node node AQUA Nodes with custom AWS-designed analytics processors to make operations (compression, encryption, filtering, and aggregations) faster than traditional CPUs Available in Preview with RA3. No code changes Amazon Redshift Managed Storage required © 2020, Amazon Web Services, Inc. or its Affiliates. Node Scaling Modify node type, number of nodes, or both Execute immediately or on a schedule During resize, cluster is in read-only © 2020, Amazon Web Services, Inc. or its Affiliates. Concurrency Scaling Scale-out to multiple Amazon Redshift clusters from a single endpoint in seconds Amazon Redshift concurrency scaling JDBC/ODBC + + Support virtually unlimited concurrent users and queries while maintaining SLAs Per-second billing for additional clusters used Free 1-hr. usage per day (free for 97% of clusters) © 2020, Amazon Web Services, Inc. or its Affiliates. Efficiency with workload management and query priorities Workload Auto WLM - Dynamically manage concurrency manager and memory to optimize throughput and performance ETL BI/Analytics Data science Priority: High Priority: Normal Priority: Low Auto WLM priorities - Influence workload performance, intelligent algorithms to keep low-priority queries running Concurrency query slots = Auto SQA (Short Query Accelerator) – prioritized … selected queries in dedicated space QMR (Query Monitoring Rules) - Define actions Amazon based on thresholds Redshift Leader node cluster Efficient sharing of cluster between users & Compute nodes business groups © 2020, Amazon Web Services, Inc. or its Affiliates. Machine learning based automatic optimizations Automates table maintenance Optimizes for peak performance as data and workloads scale Leverages machine learning Offers prescriptive recommendations with ability to dynamically apply changes © 2020, Amazon Web Services, Inc. or its Affiliates. Accelerating your migration © 2020, Amazon Web Services, Inc. or its Affiliates. AWS migration tooling AWS Schema Conversion Tool (AWS SCT) converts your commercial database and data warehouse schemas to open- source engines or AWS native services, such as Amazon Aurora and Amazon Redshift AWS Database Migration Service (AWS DMS) easily and securely migrates and/or replicates your databases and data warehouses to AWS © 2020, Amazon Web Services, Inc. or its Affiliates. AWS SCT The AWS SCT helps automate database schema and code conversion tasks when migrating from source to Features target database engines Create assessment reports for homogeneous/heterogeneous migrations Convert Convert database schema Convert data warehouse schema Convert embedded application code Code browser that highlights places where manual edits are required Source DB AWS SCT Target DB Secure connections to your databases with SSL Service substitutions/ETL modernization to AWS Glue Migrate data to data warehouses using SCT data extractors Optimize schemas in Amazon Redshift © 2020, Amazon Web Services, Inc. or its Affiliates. AWS SCT data extractors Extract data from your data warehouse and migrate to Amazon Redshift • Extracts data through local migration agents • Data is optimized for Amazon Redshift and saved in local files • Files are loaded to an Amazon S3 bucket (through network or AWS Snowball Edge) and then to Amazon Redshift Microsoft SQL Server Source DW AWS SCT Amazon Amazon NETEZZA S3 bucket Redshift © 2020, Amazon Web Services, Inc. or its Affiliates. AWS DMS Migrating Migrate between on-premises and AWS databases Migrate between databases to AWS Automated schema conversion Data replication for zero downtime migration © 2020, Amazon Web Services, Inc. or its Affiliates. Demo © 2020, Amazon Web Services, Inc. or its Affiliates. Thank you! Isabel Huerga Ayza @isahuerga isahuerga © 2020, Amazon Web Services, Inc. or its Affiliates. .
Load more

Recommended publications
  • [XIAY]⋙ Big Data Science & Analytics: a Hands-On Approach By
    Big Data Science & Analytics: A Hands-On Approach Arshdeep Bahga, Vijay Madisetti Click here if your download doesn"t start automatically Big Data Science & Analytics: A Hands-On Approach Arshdeep Bahga, Vijay Madisetti Big Data Science & Analytics: A Hands-On Approach Arshdeep Bahga, Vijay Madisetti We are living in the dawn of what has been termed as the "Fourth Industrial Revolution", which is marked through the emergence of "cyber-physical systems" where software interfaces seamlessly over networks with physical systems, such as sensors, smartphones, vehicles, power grids or buildings, to create a new world of Internet of Things (IoT). Data and information are fuel of this new age where powerful analytics algorithms burn this fuel to generate decisions that are expected to create a smarter and more efficient world for all of us to live in. This new area of technology has been defined as Big Data Science and Analytics, and the industrial and academic communities are realizing this as a competitive technology that can generate significant new wealth and opportunity. Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools. Big data science and analytics deals with collection, storage, processing and analysis of massive-scale data. Industry surveys, by Gartner and e-Skills, for instance, predict that there will be over 2 million job openings for engineers and scientists trained in the area of data science and analytics alone, and that the job market is in this area is growing at a 150 percent year-over-year growth rate.
    [Show full text]
  • Data Warehousing on AWS
    Data Warehousing on AWS March 2016 Amazon Web Services – Data Warehousing on AWS March 2016 © 2016, 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. Page 2 of 26 Amazon Web Services – Data Warehousing on AWS March 2016 Contents Abstract 4 Introduction 4 Modern Analytics and Data Warehousing Architecture 6 Analytics Architecture 6 Data Warehouse Technology Options 12 Row-Oriented Databases 12 Column-Oriented Databases 13 Massively Parallel Processing Architectures 15 Amazon Redshift Deep Dive 15 Performance 15 Durability and Availability 16 Scalability and Elasticity 16 Interfaces 17 Security 17 Cost Model 18 Ideal Usage Patterns 18 Anti-Patterns 19 Migrating to Amazon Redshift 20 One-Step Migration 20 Two-Step Migration 20 Tools for Database Migration 21 Designing Data Warehousing Workflows 21 Conclusion 24 Further Reading 25 Page 3 of 26 Amazon Web Services – Data Warehousing on AWS March 2016 Abstract Data engineers, data analysts, and developers in enterprises across the globe are looking to migrate data warehousing to the cloud to increase performance and lower costs.
    [Show full text]
  • Amazon Documentdb Deep Dive
    DAT326 Amazon DocumentDB deep dive Joseph Idziorek Antra Grover Principal Product Manager Software Development Engineer Amazon Web Services Fulfillment By Amazon © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda What is the purpose of a document database? What customer problems does Amazon DocumentDB (with MongoDB compatibility) solve and how? Customer use case and learnings: Fulfillment by Amazon What did we deliver for customers this year? What’s next? © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Purpose-built databases Relational Key value Document In-memory Graph Search Time series Ledger Why document databases? Denormalized data Normalized data model model { 'name': 'Bat City Gelato', 'price': '$', 'rating': 5.0, 'review_count': 46, 'categories': ['gelato', 'ice cream'], 'location': { 'address': '6301 W Parmer Ln', 'city': 'Austin', 'country': 'US', 'state': 'TX', 'zip_code': '78729'} } Why document databases? GET https://api.yelp.com/v3/businesses/{id} { 'name': 'Bat City Gelato', 'price': '$', 'rating': 5.0, 'review_count': 46, 'categories': ['gelato', 'ice cream'], 'location': { 'address': '6301 W Parmer Ln', 'city': 'Austin', 'country': 'US', 'state': 'TX', 'zip_code': '78729'} } Why document databases? response = yelp_api.search_query(term='ice cream', location='austin, tx', sort_by='rating', limit=5) Why document databases? for i in response['businesses']: col.insert_one(i) db.businesses.aggregate([ { $group: { _id: "$price", ratingAvg: { $avg: "$rating"}} } ]) db.businesses.find({
    [Show full text]
  • Planning and Designing Databases on AWS (AWS-PD-DB)
    Planning and Designing Databases on AWS (AWS-PD-DB) COURSE OVERVIEW: In this course, you will learn about the process of planning and designing both relational and nonrelational databases. You will learn the design considerations for hosting databases on Amazon Elastic Compute Cloud (Amazon EC2). You will learn about our relational database services including Amazon Relational Database Service (Amazon RDS), Amazon Aurora, and Amazon Redshift. You will also learn about our nonrelational database services including Amazon DocumentDB, Amazon DynamoDB, Amazon ElastiCache, Amazon Neptune, and Amazon QLDB. By the end of this course, you will be familiar with the planning and design requirements of all 8 of these AWS databases services, their pros and cons, and how to know which AWS databases service is right for your workloads. WHO WILL BENEFIT FROM THIS COURSE? • Data platform engineers • Database administrators • Solutions architects • IT professionals PREREQUISITES: We recommend that attendees of this course have previously completed the following AWS courses: • AWS Database Offerings digital training • Data Analytics Fundamentals digital training • Architecting on AWS classroom training COURSE OBJECTIVES: After completion of this course, students will be able to... • Apply database concepts, database management, and data modeling techniques • Evaluate hosting databases on Amazon EC2 instances • Evaluate relational database services (Amazon RDS, Amazon Aurora, and Amazon Redshift) and their features • Evaluate nonrelational database services
    [Show full text]
  • Lyft Goes All-In on AWS | Business Wire
    5/6/2019 Lyft Goes All-In on AWS | Business Wire Lyft Goes All-In on AWS Lyft uses the breadth of functionality and highly reliable infrastructure of AWS to enhance rider experience and accelerate self-driving technologies in the cloud February 26, 2019 09:00 AM Eastern Standard Time SEATTLE--(BUSINESS WIRE)--Today, Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), announced that Lyft, Inc. is going all-in on the world’s leading cloud—to enhance Lyft’s ridesharing marketplace which is available to an estimated 95 percent of the U.S. population, as well as in select Canadian cities, drive growth of its bike and scooters businesses, and enable its self-driving technology. Since its inception, Lyft has leveraged AWS’s unmatched performance and scalability to power its on-demand transportation platform, which facilitates more than 50 million rides a month. Lyft runs its operations, which provides a multimodal platform for drivers and riders in the U.S. and Canada, as well as backend platform systems, financial applications, and website on AWS. The company is leveraging the breadth and depth of AWS’s services, including database, serverless, machine learning, and analytics, to automate and enhance on- demand, multimodal transportation for riders and drive innovation in its autonomous vehicles business. Running on AWS’s fault-tolerant, highly performant infrastructure, helps support Lyft’s everyday business, and scales easily for peak periods, where demand can skyrocket on weekends and holidays. In addition, Lyft relies on Amazon DynamoDB, a database that delivers high performance at scale, to support its mission-critical workloads, including a ride- tracking system that enables the company to provide more precise vehicle routing.
    [Show full text]
  • All Services Compute Developer Tools Machine Learning Mobile
    AlL services X-Ray Storage Gateway Compute Rekognition d Satellite Developer Tools Amazon Sumerian Athena Machine Learning Elastic Beanstalk AWS Backup Mobile Amazon Transcribe Ground Station EC2 Servertess Application EMR Repository Codestar CloudSearch Robotics Amazon SageMaker Customer Engagement Amazon Transtate AWS Amplify Amazon Connect Application Integration Lightsail Database AWS RoboMaker CodeCommit Management & Governance Amazon Personalize Amazon Comprehend Elasticsearch Service Storage Mobile Hub RDS Amazon Forecast ECR AWS Organizations Step Functions CodeBuild Kinesis Amazon EventBridge AWS Deeplens Pinpoint S3 AWS AppSync DynamoDe Amazon Textract ECS CloudWatch Blockchain CodeDeploy Quicksight EFS Amazon Lex Simple Email Service AWS DeepRacer Device Farm ElastiCache Amazont EKS AWS Auto Scaling Amazon Managed Blockchain CodePipeline Data Pipeline Simple Notification Service Machine Learning Neptune FSx Lambda CloudFormation Analytics Cloud9 AWS Glue Simple Queue Service Amazon Polly Business Applications $3 Glacer AR & VR Amazon Redshift SWF Batch CloudTrail AWS Lake Formation Server Migration Service lot Device Defender Alexa for Business GuardDuty MediaConnect Amazon QLDB WorkLink WAF & Shield Config AWS Well. Architected Tool Route 53 MSK AWS Transfer for SFTP Artifact Amazon Chime Inspector lot Device Management Amazon DocumentDB Personal Health Dashboard C MediaConvert OpsWorks Snowball API Gateway WorkMait Amazon Macie MediaLive Service Catalog AWS Chatbot Security Hub Security, Identity, & Internet of Things loT
    [Show full text]
  • The Directory of Experimentation and Personalisation Platforms Contents I
    The Directory of Experimentation and Personalisation Platforms Contents i Why We Wrote This e-book ..................................................................................................... 3 A Note on the Classification and Inclusion of Platforms in This e-book ........................................ 4 Platform Capabilities in a Nutshell ............................................................................................ 5 Industry Leaders AB Tasty: In-depth Testing, Personalisation, Nudge Engagement, and Product Optimisation ............10 Adobe Target: Omnichannel Testing and AI-based Personalisation ...............................................15 Dynamic Yield: Omnichannel Testing, AI-Based Personalisation, and Data Management .................19 Google Optimize: In-depth Testing, Personalisation, and Analytics .............................................. 24 Monetate: Omnichannel Optimisation Intelligence, Testing, and Personalisation ........................... 27 Optimizely: Experimentation, Personalisation, and Feature-flagging .............................................31 Oracle Maxymiser: User Research, Testing, Personalisation, and Data Management ...................... 38 Qubit: Experimentation and AI-driven Personalisation for e-commerce ......................................... 43 Symplify: Omnichannel Communication and Conversion Suites .................................................. 47 VWO: Experience Optimisation and Growth ..............................................................................51
    [Show full text]
  • Informatica Powerexchange for Amazon Redshift
    Informatica® PowerExchange for Amazon Redshift 10.4.0 User Guide for PowerCenter Informatica PowerExchange for Amazon Redshift User Guide for PowerCenter 10.4.0 November 2019 © Copyright Informatica LLC 2014, 2021 This software and documentation are provided only under a separate license agreement containing restrictions on use and disclosure. No part of this document may be reproduced or transmitted in any form, by any means (electronic, photocopying, recording or otherwise) without prior consent of Informatica LLC. Informatica, the Informatica logo, PowerCenter, and PowerExchange are trademarks or registered trademarks of Informatica LLC in the United States and many jurisdictions throughout the world. A current list of Informatica trademarks is available on the web at https://www.informatica.com/trademarks.html. Other company and product names may be trade names or trademarks of their respective owners. U.S. GOVERNMENT RIGHTS Programs, software, databases, and related documentation and technical data delivered to U.S. Government customers are "commercial computer software" or "commercial technical data" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, duplication, disclosure, modification, and adaptation is subject to the restrictions and license terms set forth in the applicable Government contract, and, to the extent applicable by the terms of the Government contract, the additional rights set forth in FAR 52.227-19, Commercial Computer Software License. Portions of this software and/or documentation are subject to copyright held by third parties. Required third party notices are included with the product. The information in this documentation is subject to change without notice. If you find any problems in this documentation, report them to us at [email protected].
    [Show full text]
  • Using Amazon Web Services for Disaster Recovery October 2014
    Amazon Web Services – Using AWS for Disaster Recovery October 2014 Using Amazon Web Services for Disaster Recovery October 2014 Glen Robinson, Attila Narin, and Chris Elleman Page 1 of 22 Amazon Web Services – Using AWS for Disaster Recovery October 2014 Contents Introduction ...............................................................................................................................................................3 Recovery Time Objective and Recovery Point Objective ................................................................................................4 Traditional DR Investment Practices ............................................................................................................................4 AWS Services and Features Essential for Disaster Recovery ...........................................................................................5 Example Disaster Recovery Scenarios with AWS ...........................................................................................................9 Backup and Restore ................................................................................................................................................9 Pilot Light for Quick Recovery into AWS ................................................................................................................. 11 Warm Standby Solution in AWS ............................................................................................................................. 14 Multi-Site Solution Deployed
    [Show full text]
  • Automated Archival for Amazon Redshift
    1 Data is ingested into Amazon Redshift cluster at various frequencies. After every ingestion load, the process creates 2a Automated Archival for Amazon Redshift a queue of metadata about tables populated This architecture automates the periodic data archival process for an Amazon Redshift database. The cold data is available into Amazon Redshift tables in Amazon RDS. instantly and can be joined with existing datasets in the Amazon Redshift cluster using Amazon Redshift Spectrum. Data Engineers may also create the archival 2b queue manually, if needed. 3 Using Amazon EventBridge, an AWS Lambda function is triggered periodically to read the queue from the RDS table and create an Amazon SQS message for every period due for archival. The user may choose a single SQS 1 queue or an SQS queue per schema based on the volume of tables. A proxy Lambda function de-queues the 4 Amazon SQS messages and for every message invokes AWS Step Functions. AWS Step Functions unloads the data from 2a 5 the Amazon Redshift cluster into an Amazon 2b S3 bucket for the given table and period. Amazon S3 Lifecycle configuration moves 7 8 6 3 4 5 data in buckets from S3 Standard storage class to S3 Glacier storage class after 90 days. 9 7 Amazon S3 inventory tool generates manifest 6 files from the Amazon S3 bucket dedicated for cold data on daily basis and stores them in an S3 bucket for manifest files. 10 8 Every time an inventory manifest file is created in a manifest S3 bucket, an AWS Lambda function is triggered through an Amazon S3 Event Notification.
    [Show full text]
  • Databases on AWS the Right Tool for the Right Job
    Databases on AWS The Right Tool for the Right Job David Gendel, Sr. Solutions Architect, AWS Wednesday, February 17, 2021 © 2021, Amazon Web Services, Inc. or its Affiliates. Traditional Database Architecture Client Tier one database App/Web Tier for all workloads RDBMS © 2021, Amazon Web Services, Inc. or its Affiliates. Traditional Database Architecture • Key-value access Client Tier • Complex queries • OLAP transactions App/Web Tier • Analytics RDBMS All forced into the relational database © 2021, Amazon Web Services, Inc. or its Affiliates. AWS Data Tier Architecture Client Tier On AWS choose best database service for each App/Web Tier workload Data Tier Cache Data Warehouse Time Series Blob Store NoSQL RDBMS Quantum Ledger Search © 2021, Amazon Web Services, Inc. or its Affiliates. Workload Driven Data Store Selection hot reads analytics logging NoSQL complex queries Periodic rich search simple query & transactions data Graph / Key Value / Document Untampered data Data Tier Cache Data Warehouse Time Series Blob Store NoSQL RDBMS Quantum Ledger Search © 2021, Amazon Web Services, Inc. or its Affiliates. AWS Database Services hot reads analytics logging NoSQL complex queries Periodic rich search simple query & transactions data Graph / Key Value / Document Untampered data Data Tier Amazon Amazon Amazon Amazon S3 ElastiCache Redshift Timestream Amazon DynamoDB Amazon Amazon Amazon Neptune / DocumentDB RDS QLDB ElasticSearch © 2021, Amazon Web Services, Inc. or its Affiliates. Easy to Administer Highly Scalable Amazon RDS Available and
    [Show full text]
  • AWS Certified Database - Specialty Beta Exam
    D A T 3 3 8 - R Hands-on workshop: How to migrate to Amazon DocumentDB Hugo Rozestraten Daniel Bento Specialist Solutions Architect Solutions Architect Amazon Web Services Amazon Web Services © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda Amazon DocumentDB architecture Migrating your MongoDB databases to Amazon DocumentDB Online: AWS Data Migration Service (AWS DMS) Offline: Mongodump + Mongorestore Hybrid: Dump + CDC Hands-on workshop DocumentDB architecture Scale compute and storage independently AWS Region Availability Zone 1 Availability Zone 2 Availability Zone 3 Cluster endpoint Read replica endpoint Instance Instance Instance (primary) (replica) (replica) Writes Reads Reads Reads Writes Data copies Data copies Writes Data copies Cluster volume © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Path for migration Migration Migration method Factor Offline (dump) Hybrid Online (DMS) Complexity Very simple Complex Medium Speed Fast* Medium* Slow** Downtime High Minimal Minimal *Consider using AWS Snowball to move files for large volumes **Copying very large tables with DMS can take a long time Check the connectivity and bandwidth for migration (VPN or DX) Migrate MongoDB to Amazon DocumentDB–DMS AWS Cloud AWS Region Corporate Availability Zone 1 Availability Zone 2 Availability Zone 3 data center Amazon Amazon VPC DocumentDB Instance Instance Instance (primary) (replica) (replica) VPN connection MongoDB AWS Direct Connect AWS DMS Migrate MongoDB to Amazon DocumentDB–DMS + verify
    [Show full text]