Transformation Track AWS Cloud Experience Argentina - Bases de Datos en AWS
0 likes194 views
The document discusses different database options on AWS, including relational databases like Amazon RDS and Aurora, non-relational databases like DynamoDB, graph databases like Neptune, and data warehousing with Redshift. It provides examples of how customers like Airbnb and Duolingo use different AWS databases together based on data model and use case. Key benefits highlighted are scalability, manageability, security, and cost effectiveness of AWS databases.
Transformation Track AWS Cloud Experience Argentina - Bases de Datos en AWS
- 1. 2 0 1 9 #AWSCLOUDEXPERIENCE
- 2. DataBases on AWS Diana Díaz AWS Partner Trainer Amazon Web Services
- 3. Two fundamental areas of focus “Lift and shift” existing apps to the cloud Quickly build new apps in the cloud
- 4. Two fundamental areas of focus “Lift and shift” existing apps to the cloud Quickly build new apps in the cloud
- 5. Old-guard commercial databases Very expensive Proprietary Lock-in Punitive licensing You’ve got mail
- 6. Managed services transform operations Power, HVAC, net Rack & stack Server maintenance OS patches DB software patches Database backups High Availability DB software installs OS installation Scaling Operating Databases in AWS App optimization you Power, HVAC, net Rack & stack Server maintenance OS patches DB software patches Database backups Scaling High Availability DB software installs OS installation you App optimization Operating Databases in the Old World
- 7. Scale compute and storage with a few clicks; minimal downtime for your application Automatic Multi-AZ data replication; automated backup, snapshots, and failover Data encryption at rest and in transit; industry compliance and assurance programs Running many databases with Amazon RDS Managed Relational Database Service with choice Managed & Automated Deploy and maintain hardware, OS, and DB software; built-in monitoring Performant & scalable Available & durable Secure & compliant
- 8. Key Amazon RDS Features Managed Relational Database Service with choice Amazon RDS Configuration Improve Availability Increase Throughput Reduce Latency Push-Button Scaling Multi AZ Read Replicas Provisioned IOPS Read ReplicasPush-Button Scaling Provisioned IOPS Region Multi-AZ availability zone availability zone
- 9. Moving to open source database engines + Commercial-grade performance and reliability?
- 10. Amazon Aurora MySQL and PostgreSQL compatible relational database built for the cloud Performance and availability of commercial-grade databases at 1/10th the cost 5x throughput of standard MySQL and 3x of standard PostgreSQL; scale-out up to15 read replicas Fault-tolerant, self-healing storage; six copies of data across three AZs; continuous backup to S3 Network isolation, encryption at rest/transit Managed by RDS: no server provisioning, software patching, setup, configuration, or backups Performance & scalability Availability & durability Highly secure Fully managed
- 11. Large relational databases with Amazon Aurora Scale-out, distributed, multi-tenant architecture • Your data is replicated 6 ways across 3 AZs • Continuous backup to Amazon S3 (built for 99.999999999% durability) • Up to 15 Aurora Replicas with instant crash recovery AZ 1 AZ 2 AZ 3 Virtualized, cross-AZ storage layer Size for the peak load -or- Continuously monitor and manually scale up/down
- 12. Aurora Serverless . . . Responds to your application load automatically • Scale capacity with no downtime • Multi-tenant proxy is highly available • Scale target has warm buffer pool • Shuts down when not in use
- 13. Aurora Multi-master Database Node (Writer) Database Node (Writer) REPLICATION (redo records) SN1 SN2 SN3 SN4 SN5 SN6 AZ-2AZ-1 AZ-3 AZ – Availability Zone SN – Storage Node
- 14. Aurora is used by ¾ of the top 100 AWS customers Aurora customer adoption Fastest growing service in AWS history
- 15. AWS Database Migration Service Migrating Databases to AWS 90,000+ Databases migrated Migrate between on-premises and AWS Migrate between databases Data replication for zero-downtime migration Automated schema conversion
- 16. Two fundamental areas of focus “Lift and shift” existing apps to the cloud Quickly build new apps in the cloud
- 17. A one size fits all database doesn’t fit anyone Modern Applications Need Purpose-Built Databases 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 Graph Search
- 18. AWS purpose-built strategy The right tool for the right job Relational Non-Relational Aurora RDS ElastiCacheDynamoDB Key-value Document Neptune Graph Microsoft SQL Server
- 19. Let’s take a closer look at… Key-value Graph In-memory
- 20. Let’s take a closer look at… Key-value Graph In-memory
- 21. Key-value data • Simple key value pairs • Partitioned by keys • Resilient to failure • High throughput, low-latency reads and writes • Consistent performance at scale Gamers Primary Key Attributes GamerTag Level Points High Score Plays Hammer57 21 4050 483610 1722 FluffyDuffy 5 1123 10863 43 Lol777313 14 3075 380500 1307 Jam22Jam 20 3986 478658 1694 ButterZZ_55 7 1530 12547 66 … … … … … PUT { TableName:"Gamers", Item: { "GamerTag":"Hammer57", "Level":21, "Points":4050, "Score":483610, "Plays":1722 } } GET { TableName:"Gamers", Key: { "GamerTag":"Hammer57“, “ProjectionExpression“:”Points” } }
- 22. Amazon.com case “A deep dive on how we were using our existing databases revealed that they were frequently not used for their relational capabilities. About 70 percent of operations were of the key- value kind, where only a primary key was used and a single row would be returned. About 20 percent would return a set of rows, but still operate on only a single table.” Werner Vogels, A Decade of Dynamo Blog Post
- 23. Amazon DynamoDB Fully-managed nonrelational database for any scale Secure Encryption at rest and transit Fine-grained access control PCI, HIPAA, FIPS140-2 eligible High performance Fast, consistent performance Virtually unlimited throughput Virtually unlimited storage Fully managed Maintenance-free Serverless Auto scaling Backup and restore Global tables Global Tables High-performance, globally distributed applications Multi-region redundancy and resiliency Easy to set up and no application rewrites required
- 24. Let’s take a closer look at… Key-value Graph In-memory
- 25. Graph data • Relationships are first-class objects • Vertices connected by Edges Vertex PURCHASED PURCHASED FOLLOWS PURCHASED KNOWS PRODUCT SPORT FOLLOWS Edge
- 26. Amit Kevin Graph use case Do you know… Customers who also follow sports purchased… gremlin> g.V().has(‘name’,’sara’).as(‘customer’).out(‘follows’).in(‘follows’).out(‘purchased’) where(neq(‘customer’)).dedup().by(‘name’).properties('name') PURCHASED PURCHASED FOLLOWS PURCHASED KNOWS PRODUCT SPORT FOLLOWS Bill Mary FOLLOWS Sara // Identify a friend in common and make a recommendation gremlin> g.V().has('name','mary').as(‘start’). both('knows').both('knows’). where(neq(‘start’)). dedup().by('name').properties('name')
- 27. Highly connected data best represented in a graph Relational model Foreign keys used to represent relationships Queries can involve nesting & complex joins Performance can degrade as datasets grow Graph model Relationships are first-order citizens Write queries that navigate the graph Results returned quickly, even on large datasets
- 28. Amazon Neptune Fast & Scalable ReliableFlexible Store billions of relationships; query with millisecond latency Six replicas of your data across three AZs with full backup and restore Build powerful queries with Gremlin and SPARQL Supports Apache TinkerPop & W3C RDF graph models Gremlin SPARQL Open Standards Fully managed graph database
- 29. Let’s take a closer look at… Key-value Graph In-memory
- 30. Amazon ElastiCache Fully managed, Redis or Memcached compatible, low latency, in memory data store Fully Managed Extreme Performance Easily Scalable AWS manages all hardware and software setup, configuration, monitoring In-memory data store and cache for sub- millisecond response times Read scaling with replicas. Write and memory scaling with sharding. Non disruptive scaling
- 31. Data models and common use cases Amazon Aurora, Amazon RDS, Amazon Redshift Amazon DynamoDB Amazon DynamoDB, Amazon DocumentDB Amazon ElastiCache Amazon Neptune Amazon Elasticsearch ERP, medical records, CRM, finance Real-time bidding, shopping cart, IoT device tracking Content management, personalization, mobile Leaderboards, real- time analytics, caching Fraud detection, social networking, recommendation engine Product catalog, help/FAQs, full-text Relational Key-value In-memory Document SearchGraph
- 32. Airbnb uses different databases based on the purpose User search history: Amazon DynamoDB • Massive data volume • Need quick lookups for personalized search Session state: Amazon ElastiCache • In-memory store for submillisecond site rendering Relational data: Amazon RDS • Referential integrity • Primary transactional database
- 33. CHALLENGE Wanted to enable anyone to learn a language for free. SOLUTION Purpose-built databases from AWS: • DynamoDB: 31B items tracking which language exercises completed • Aurora: primary transactional database for user data • ElastiCache: instant access to common words and phrases Result: More people learning a language on Duolingo than entire US school system 300M total users 7B exercises per month
- 35. Amazon RedShift Highly scalable cloud data warehouse at 10x the performance and 1/10th the cost of traditional data warehouses Fast Delivers fast results for all types of workloads Cost-effective No upfront costs, start small, and pay as you go Integrated Secure Audit everything; encrypt data end-to-end; extensive certification and compliance Integrated with S3 data lakes, AWS services and third-party tools $ Simple Create and start using a data warehouse in minutes Scalable Gigabytes to petabytes to exabytes
- 36. Data models and common use cases Relational Key-value Document In-memory Graph Search 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 Indexing and searching semistructured logs and data ERP, medical records, CRM, finance Real-time bidding, shopping cart, IoT device tracking Content management, personalization, mobile Leaderboards, real-time analytics, caching Fraud detection, social networking, recommendation engine Product catalog, help/FAQs, full-text Amazon Aurora, Amazon RDS. Amazon Redshift Amazon DynamoDB Amazon DynamoDB, Amazon DocumentDB Amazon ElastiCache for Redis & Memcached Amazon Neptune Amazon Elasticsearch
- 37. ¡Thank you!