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Building with Purpose-Built Databases: Match Your Workload to the Right Database

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Learn how to evaluate a new workload for the best managed database option based on specific application needs related to data shape, data size at limit, computational requirements, programmability, throughput and latency needs, and more. This session explains the ideal use cases for relational and non-relational database services, including Amazon Aurora, Amazon DynamoDB, Amazon ElastiCache for Redis, Amazon Neptune, and Amazon Redshift.

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Building with Purpose-Built Databases: Match Your Workload to the Right Database

  1. 1. P U B L I C S E C T O R S U M M I T B RUSSE LS
  2. 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Building with Purpose-Built Databases MatchYourWorkloadtotheRightDatabase Márcio Santos Solutions Architect samarcio@amazon.com
  3. 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Richsetof servicesfor everchanging workloads Over 40 data services in a rich ecosystem of over 160 web services, all designed to work together using open standards. Rapidly improving selection of services, driven by customer demand Maximum agility and maximum cost savings on a constantly improving foundation For any workload Analytics NewApplications Moving Legacy Application toCloud
  4. 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Trends thatimpactthewaywethinkabout data Data grows 10x every 5 years driven by network connected smart devices Transition from IT to DevOps increases rate of change Micro-services architecture decreases need for one-size fits all databases and increases need for real-time monitoring and analytics Explosion of data Micro-services changes data and analytics requirements Rapid rate of change driven by DevOps Dev Ops
  5. 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Our strategyand our beliefs backin 2010 1. There is going to be an explosion in data. 2. Cloud will enable different architectures. 3. One size does not fit all - databases should be purpose-built.
  6. 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Our approach Architect services ground-up for the cloud and for the explosion of data Offer a portfolio of purpose-built services, optimized for your workloads Help you innovate faster through managed services Provide services that help you migrate existing apps and databases to the cloud
  7. 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Broad and deep portfolio, purpose-built for builders Data Movement Analytics Data Lake Infrastructure & Management Ledger Visualization & Machine Learning Databases + 10 more QuickSight SageMaker Comprehen d Le x Polly Rekognition Translate Transcribe Deep Learning AMIs Aurora MySQL, PostgreSQL RDS and RDS on VMWARE MySQL, PostgreSQL, Oracle, SQL Server MariaDB DynamoDB Key-value ElastiCache Redis, Memcached Timestream Time Series Neptune Graph DocumentDB Documents Redshift EMR Spark, Hadoop Athena Elasticsearch Service Kinesis Data Analytics Glue Scala, Python S3/Glacier Lake Formation Glue Crawl, ETL Managed Blockchain Quantum Ledger Database (QLDB) Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams | Managed Streaming for Kafka
  8. 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T A one sizefitsalldatabasedoesn’t fitanyone Modernapplicationsneedpurpose-builtdatabases Users: 1M+ Data volume: TB–PB–EB Locality: Global Performance: Milliseconds–microseconds Request Rate: Millions Access: Mobile, IoT, devices Scale: Up-out-in Economics: Pay-as-you-go Developer access: Instant API access Relational Key-value Document In-memory
  9. 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Purpose-built Purpose-built databases and analytic engines. Right tool for the right job
  10. 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Therearedifferent types ofpurpose-built databases Relational Key-value Document In-memory Graph Time series Ledger Referential integrity, ACID transactions, schema- on-write Low-latency, key look-ups with high throughput and fast ingestion of data Indexing and storing documents with support for query on any attribute Microseconds latency, key- based queries, and specialized data structures Creating and navigating relations between data easily and quickly Collecting, storing, and processing time series data Complete immutable, verifiable history of all changes to application data Lift and shift, EMR, CRM, finance Real-time bidding, shopping cart, social Content management, personalization, mobile Leaderboards, real-time analytics, caching Fraud detection, social networking, recommendation engine IoT applications, event tracking Systems of record, registrations, supply chain, financial
  11. 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T AWS: Purpose-built databases Relational Key-value Document In-memory Graph Search Amazon DynamoDB Amazon Neptune Amazon RDS Aurora CommercialCommunity Amazon ElastiCache Amazon Elasticsearch Service Amazon DocumentDB Time-series Ledger Amazon Timestream Amazon Quantum Ledger DatabaseMemcachedRedi s
  12. 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Amazon RDS Setuprelationaldatabaseinthecloudwithjustafewclicks Available & durable Automatic Multi-AZ data replication; automated backup, snapshots, failover Easy to administer No need for infrastructure provisioning, installing and maintaining DB software Highly scalable Scale database compute and storage with a few clicks with no application downtime Fast & secure SSD storage and guaranteed provisioned I/O; data encryption at rest and in transit
  13. 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T AmazonAurora MySQLandPostgreSQL-compatiblerelationaldatabasebuiltforthecloud Availability and durability Fault-tolerant, self-healing storage; six copies of data across three AZs; continuous backup to S3 Fully managed Managed by RDS: no hardware provisioning, software patching, setup, configuration, or backups Highly secure Network isolation, encryption at rest/transit Performance and scalability 5x throughput of standard MySQL and 3x of standard PostgreSQL; scale-out up to 15 read replicas Performance and availability of commercial-grade databases at 1/10th the cost
  14. 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Scale-out,distributed,multi-tenantarchitecture • Purpose-built log-structured distributed storage system designed for databases • Storage volume is striped across hundreds of storage nodes distributed over 3 different Availability Zones • Six copies of data, two copies in each Availability Zone to protect against AZ+1 failures • Master and replicas all point to the same storage Availability Zone 1 Availability Zone 2 Availability Zone 3 Shared storage volume Storage nodes with SSDs Master SQL Transactions Caching Replica Replica SQL Transactions Caching SQL Transactions Caching
  15. 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T AuroraGlobal Database High-performancedatabaseforglobally-distributedapplications Single Global Database with cross region replication Replication typically completes in less than a second No impact on database performance Write master in one region and read replicas in other regions Cross-region disaster recovery Local read latency for applications with global users Primary Region Secondary Region Application Storage Storage Replication <1s
  16. 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Databasesin privatedatacenters Stilldifficultandexpensivetosetupandmanage Difficult to set up and manage databases for high availability across multiple nodes Personnel needed to create the database image, install operating system, packages, and setup Burdensome to support multiple versions and applying patching ?
  17. 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Amazon RDSonVMware Managedserviceforon-premisesdatabases RDS deployed as a service in on-premises VMware private data centers (vSphere) Automates management of on-premises databases and hybrid backup and scaling Available and durable Enable hybrid features and tap into AWS for high availability, backup, and restore Secure and compliant Automate management of databases for workloads that must remain on-premises to adhere to strict data policies Fully managed Easy to provision, monitor, and operate relational databases in your private data center Scalability and performance Scale storage, compute, and memory of on-premises databases from a single, simple interface
  18. 18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Amazon DynamoDB Fastandflexiblekeyvaluedatabaseserviceforanyscale Comprehensive security Encrypts all data by default and fully integrates with AWS Identity and Access Management for robust security Performance at scale Consistent, single-digit millisecond response times at any scale; build applications with virtually unlimited throughput Global database for global users and apps Build global applications with fast access to local data by easily replicating tables across multiple AWS Regions Serverless No hardware provisioning, software patching, or upgrades; scales up or down automatically; continuously backs up your data
  19. 19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T DynamoDB –GlobalTables Fullymanagedactive-activeglobally-distributeddatabase Build high-performance, globally distributed applications Low latency reads and writes to locally available tables Multiregional redundancy and resiliency Easy to set up and no application rewrites required
  20. 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Amazon DocumentDB (withMongoDB compatibility) Fast,scalable,highlyavailableMongoDB-compatibledatabase Fast Scalable Fully managed MongoDB compatible Millions of requests per second with millisecond latency; twice the throughput of MongoDB Separation of compute and storage enables both layers to scale independently; scale out to 15 read replicas in minutes Managed by AWS: no hardware provisioning; auto patching, quick setup, secure, and automatic backups Compatible with MongoDB 3.6; use the same SDKs, tools, and applications with Amazon DocumentDB
  21. 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Amazon ElastiCachefor Redisand Memcached Fullymanagedin-memorydatastoreinthecloud Extreme Performance In-memory data store and cache for sub-millisecond response times Fully Managed AWS manages all hardware and software setup, configuration, monitoring Easily Scalable Read scaling with replicas Write and memory scaling with sharding Non-disruptive scaling Open Source Compatible Redis 5 support Redis clients compatible Memcached 1.5 support Reliable Multi-AZ Deep monitoring Automatic failover Secure & Compliant Amazon VPC HIPAA, PCI, FedRAMP Encryption at-rest and in-transit Authentication
  22. 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Amazon Neptune Fullymanagedgraphdatabase Open Fast Reliable Easy Supports Apache TinkerPop & W3C RDF graph models Query billions of relationships with millisecond latency 6 replicas of your data across 3 AZs with full backup and restore Build powerful queries easily with Gremlin and SPARQL
  23. 23. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Amazon ElasticsearchService Fullymanaged,scalable,andsecure Fully managed; Deploy production-ready clusters in minutes Secure access with VPC to keep all traffic within AWS network Zone awareness replicates data between two AZs; automatically monitors & replaces failed nodes Direct access to Elasticsearch open-source APIs; supports Logstash and Kibana Easy to Use Secure AvailableOpen
  24. 24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T AmazonTimestream Fast,scalable,fullymanagedtimeseriesdatabase 1,000x faster and 1/10th the cost of relational databases Collect data at the rate of millions of inserts per second (10M/second) Trillions of daily events Adaptive query processing engine maintains steady, predictable performance Analytics optimized for time series data Built-in functions for interpolation, smoothing, and approximation Serverless Automated setup, configuration, server provisioning, software patching
  25. 25. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T AmazonQuantumLedger Database(QLDB) Fullymanagedledgerdatabase Immutable Maintains a sequenced record of all changes to your data, which cannot be deleted or modified; you have the ability to query and analyze the full history Cryptographically verifiable Uses cryptography to generate a secure output file of your data’s history Easy to use Easy to use, letting you use familiar database capabilities like SQL APIs for querying the data Highly scalable Executes 2–3x as many transactions than ledgers in common blockchain frameworks
  26. 26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Amazon Managed Blockchain Easilycreateandmanagescalableblockchainnetworks Choice of Hyperledger Fabric or Ethereum Hyperledger Fabric available today; Ethereum coming soon Fully managed Create blockchain networks with a few clicks; Manage them with simple API calls Easily analyze blockchain activity Easy to move data into QLDB for further analysis Scalable and secure Support thousands of client applications running millions of transaction; integrates with AWS KMS
  27. 27. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Whatifyou wantto liftand shiftto thecloud Relational databases Non-relational databases Data warehouses Hadoop and Spark Redshif t EM R Operational analytics Elasticsearch ServiceAuror a DynamoD B Business Intelligence QuickSightRDS
  28. 28. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T AWS DatabaseMigrationService M I G R A T I N G D A T A B A S E S T O A W S Migrate between on-premises and AWS Migrate between databases Automated schema conversion Data replication for zero downtime migration
  29. 29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Traditionally,analyticslooked likethis Relational data GBs-TBs scale [not designed for PB/EBs] Expensive: Large initial capex + $10K-$50K/TB/year 90% of data was thrown away because of cost OLTP ERP CRM LOB Data Warehouse Business Intelligence
  30. 30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Exabyte scale Store and analyze relational and non-relational data Purpose-built analytics tools Cost effective • Store at 2.3 cents per GB-month in Amazon S3 • Query with Amazon Athena at ½ cent per GB scanned • DW with Amazon Redshift for $1,000/TB/year Give access to everyone • Amazon QuickSight: $0.30 for 30 minutes of use DatalakesonAWS Snowball Snowmobile Kinesis Data Firehose Kinesis Data Streams S3 Redshift EMR Athena Kinesis Elasticsearch Service Kinesis Video Streams AI Services QuickSight
  31. 31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Amazon LakeFormation Buildasecuredatalakeindays Move, store, catalog, and clean your data faster Move, store, catalog, and clean your data faster with machine learning Enforce security policies across multiple services Enforce security policies across multiple services Gain and manage new insights Empower analyst and data scientist to gain and manage new insights
  32. 32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Amazon Redshift Highlyscalableclouddatawarehouse Virtually unlimited concurrency Extends your data lake Dynamically scales to support virtually unlimited number of concurrent users and growing data volumes Analyze exabytes of data in the Amazon S3 data lake together with petabytes of data loaded into Amazon Redshift’s high performance SSDs 10x performance 1/10th the cost Get faster time-to-insight for all types of analytics workloads; powered by machine learning, columnar storage and MPP Start at $0.25 per hour, scale out as low as $1,000 per terabyte per year
  33. 33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T AmazonRedshiftConcurrencyScaling Consistently fast performance at virtually unlimited concurrency Automatically spins up additional clusters on-demand Handles virtually unlimited number of concurrent users Accrued minutes make it free for most customers Redshift Managed S3 Cluster Leader Node Data Data Caching Layer Cluster Leader Node
  34. 34. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T AmazonRedshiftSpectrum Extend the data warehouse to exabytes of data in S3 data lake S3 data lakeRedshift data Amazon Redshift Spectrum query engine Redshift SQL queries using Amazon S3 at any scale Join data across Amazon Redshift and Amazon S3
  35. 35. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Amazon RedshiftML based auto-tuning Clustersalwaysoptimizedforbestperformanceandlowestcost Tailored recommendations to increase performance & reduce cost Redshift’s machine learning engine uncovers optimizations Operations such as vacuum and analyze run behind the scenes Available today AUT O AUT O AUT O ADVISE ADVISE
  36. 36. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Amazon EMR AnalyticsandMLatscalewithEnterprise-gradeSecurity Updated with the latest open source frameworks within 30 days of release Process data directly in the S3 data lake securely with high performance using the EMRFS connector Launch fully managed Hadoop & Spark in minutes; no cluster setup, node provisioning, cluster tuning Flexible billing with per- second billing, EC2 spot, reserved instances and auto-scaling to reduce costs 50–80% Latest versions Use S3 storage EasyLow cost
  37. 37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T In summary 1. We have ~40 services in this portfolio today 2. Customers are getting more insights and impact from IT because of the cloud than ever before 3. Purpose-built databases are powering tens of thousands of our customer applications
  38. 38. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Thank you! © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Márcio Santos Solutions Architect samarcio@amazon.com
  39. 39. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T

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