Recomendados
Más contenido relacionado
La actualidad más candente
La actualidad más candente (20)
Similar a Microsoft Data & AI Experience LATAM 2018 - Azure Cosmos DB
Similar a Microsoft Data & AI Experience LATAM 2018 - Azure Cosmos DB (20)
Más de Matias Quaranta
Más de Matias Quaranta (10)
Último
Último (20)
Microsoft Data & AI Experience LATAM 2018 - Azure Cosmos DB
- 1. Microsoft Data & AI Experience 2018 LATAM Azure Cosmos DB Matías Quaranta - @ealsur
- 2. James Whittaker - @docjamesw “We are reducing the world to data”
- 3. Crear arquitecturas con alcance global viene acompañado de problemas de escala global Escalar almacenaje y ancho de banda Crear experiencias rápidas Asegurar alta disponibilidad Codear aplicaciones distribuídas globalmente Administrar complejos esquemas Balance en la consistencia de la información
- 4. Column-family Document Graph Turnkey global distribution Elastic scale out of storage & throughput Guaranteed low latency at the 99th percentile Comprehensive SLAs Five well-defined consistency models Table API Key-value Un servicio de base de datos multimodelo distribuído globalmente y dinámicamente escalable Azure Cosmos DB MongoDB
- 5. El futuro de los datos Crear arquitecturas pensando en el futuro
- 6. El futuro de los datos Crear arquitecturas pensando en el futuro 80% de los datos en los negocios se origina en forma desestructurada estructurados desestructurados 20% 80% <
- 7. El futuro de los datos Crear arquitecturas pensando en el futuro
- 8. El futuro de los datos Crear arquitecturas pensando en el futuro
- 9. El futuro de los datos Crear arquitecturas pensando en el futuro
- 10. El futuro de los datos Crear arquitecturas pensando en el futuro Motor de indexado automático y agnóstico del esquema Facilita el manejo del indexado en escenarios de replicación global Indexado automático y sincrónico Hash, range, y geoespacial Funciona para todos los modelos de datos Motor optimizado para escrituras Physical index Schema
- 11. El futuro de los datos Crear arquitecturas pensando en el futuro
- 12. El futuro de los datos Crear arquitecturas pensando en el futuro
- 13. Migrando a Cosmos DB Desde cualquier origen y tipo de dato
- 14. Interactuando en SQL Jerarquía de recursos
- 15. Interactuando en SQL Soporte geo-espacial
- 16. Escalando hacia el infinito Geo-replicación, failover automático y transparente
- 17. Escalando hacia el infinito Geo-replicación, failover automático y transparente
- 18. Escalando hacia el infinito Capacidad de procesamiento y almacenaje dinámicamente escalable
- 19. Escalando hacia el infinito Request Units Request Units (RU) is a rate-based currency Abstracts physical resources for performing requests Key to multi-tenancy, SLAs, and COGS efficiency Foreground and background activities % IOPS % CPU % Memory
- 20. Escalando hacia el infinito Cosmos DB Container (e.g. Collection) Partition Key: User Id Logical Partitioning Abstraction Behind the Scenes: Physical Partition Sets hash(User Id) Psuedo-random distribution of data over range of possible hashed values
- 21. Escalando hacia el infinito hash(User Id) …. Andrew Mike … Partition 1 Partition 2 Partition n Bob Dharma Shireesh Karthik Rimma Alice Carol … Behind the Scenes: Physical Partition Sets Frugal # of Partitions based on actual storage and throughput needs (yielding scalability with low total cost of ownership)
- 22. Escalando hacia el infinito hash(User Id) Partition X Dharma Shireesh Karthik Rimma Alice Carol … + Dharma Shireesh … Partition X1 Rimma Karthik … Partition X2 Partition Ranges can be dynamically sub-divided To seamlessly grow database as the application grows While sedulously maintaining high availability
- 23. Escalando hacia el infinito Capacidad de procesamiento y almacenaje dinámicamente escalable Read < 1 ms Writes < 6 ms Read < 10 ms Writes < 15 ms 99%50%
- 24. Escalando hacia el infinito Cinco niveles de consistencia personalizada Strong consistency High latency Eventual consistency, Low latency
- 25. Escalando hacia el infinito Cinco niveles de consistencia personalizada
- 26. Multi-modelo Interactuando como tablas Azure Cosmos DB: Table API Azure Storage: Standard Table API Azure Storage SDKs 100% Backwards compatible, Seamless experience
- 27. Multi-modelo Interactuando como grafos de relaciones Modelado del mundo real y sus relaciones Relaciones como ciudadanos de primera clase Optimizado para almacenar y recorrer grafos Gremlin estandard Azure Cosmos DB: Graph API
- 28. Multi-modelo Interactuando como grafos de relaciones
- 29. AI & Analytics Lambda and change-tracking patterns
- 30. AI & Analytics Lambda and change-tracking patterns Microsoft.Azure.DocumentDB.ChangeFeedProcessor
- 31. AI & Analytics Serverless event-based
- 32. Cosmos DB Input Serverless event-based
- 33. Cosmos DB Output Serverless event-based
- 34. Cosmos DB Trigger Serverless event-based
- 35. Seguridad & Conformidad Seguridad de nivel empresarial Encryption at Rest Por defecto Certificada Los datos, índices, backups y attachments son siempre encriptados en destino y durante el transporte Sin impacto en la performance para las aplicaciones y habilitado en todas las cuentas ISO 27001, ISO 27018, EUMC, HIPAA, PCI SOC1, SOC2, FedRAMP, IRS 1075, IL2, HITRUST (coming soon)
- 36. Globally Distributed Apps • Business Needs: • High scalability to support massive user base • Low latency access across multiple geographies to build highly responsive applications • Automatic indexing over flexible schemas to support rich queries over user-defined content (e.g. custom form builder) • High availability across multiple data centers
- 37. Internet of Things – Telemetry & Sensor Data • Business Needs: • High scalability to ingest large # of events coming from many devices • Low latency queries and changes feeds for responding quickly to anomalies • Schema-agnostic storage and automatic indexing to support dynamic data coming from many different generations of devices • High availability across multiple data centers
- 38. Retail – Product Catalog & Order Processing • Business Needs: • Elastic scale to handle seasonal traffic (e.g. Black Friday) • Low-latency access across multiple geographies to support a global user-base and latency sensitive workloads (e.g. real-time personalization) • Schema-agnostic storage and automatic indexing to handle diverse product catalogs, orders, and events • High availability across multiple data centers
- 39. Multiplayer Gaming • Business Needs: • Elastic scale to handle bursty traffic on day • Low-latency queries to support responsive gameplay for a global user-base • Schema-agnostic storage and indexing allows teams to iterate quickly to fit a demanding ship schedule • Change-feeds to support leaderboards and social gameplay
- 40. Customer 360 + Operational Analytics • Business Needs: • High scalability to handle large user base • Rich queries and automatic indexing over flexible schemas to consolidate data from a variety of sources • Built-in aggregates and efficient spark connector to analyze user behavior, drive insights, and build a single view of the customer.
- 41. Messaging • Business Needs: • High scalability to ingest and store messages from a massive user base • Low latency access across multiple geographies to support global user-base • High availability across multiple data centers
- 42. Probar antes de comprar Azure Cosmos DB Emulator aka.ms/cosmosdb-emulator
- 43. Probar antes de comprar Azure Cosmos DB gratis azure.microsoft.com/try/cosmosdb/
Notas del editor
- Azure Cosmos DB offers the first globally distributed, multi-model database service for building planet scale apps. It’s been powering Microsoft’s internet-scale services for years, and now it’s ready to launch yours. Only Azure Cosmos DB makes global distribution turn-key. You can add Azure locations to your database anywhere across the world, at any time, with a single click. Cosmos DB will seamlessly replicate your data and make it highly available. Cosmos DB allows you to scale throughput and storage elastically, and globally! You only pay for the throughput and storage you need – anywhere in the world, at any time.
- At global scale, schema/index management is hard Automatic and synchronous indexing of all ingested content - hash, range, geo-spatial, and columnar No need to define schemas or secondary indices upfront Resource governed, write optimized database engine with latch free and log structured techniques Online and in-situ index transformations
- At global scale, schema/index management is hard Automatic and synchronous indexing of all ingested content - hash, range, geo-spatial, and columnar No need to define schemas or secondary indices upfront Resource governed, write optimized database engine with latch free and log structured techniques Online and in-situ index transformations
- At global scale, schema/index management is hard Automatic and synchronous indexing of all ingested content - hash, range, geo-spatial, and columnar No need to define schemas or secondary indices upfront Resource governed, write optimized database engine with latch free and log structured techniques Online and in-situ index transformations
- Changes are persistent in Azure Cosmos DB and can be processed asynchronously. Changes to documents within a collection are available immediately in the change feed. Each change to a document appears exactly once in the change feed, and clients manage their checkpointing logic. The change feed processor library provides automatic checkpointing and "at least once" semantics. Only the most recent change for a given document is included in the change log. Intermediate changes may not be available. The change feed is sorted by order of modification within each partition key value. There is no guaranteed order across partition-key values. Changes can be synchronized from any point-in-time, that is, there is no fixed data retention period for which changes are available. Changes are available in chunks of partition key ranges. This capability allows changes from large collections to be processed in parallel by multiple consumers/servers. Applications can request for multiple change feeds simultaneously on the same collection
- Changes are persistent in Azure Cosmos DB and can be processed asynchronously. Changes to documents within a collection are available immediately in the change feed. Each change to a document appears exactly once in the change feed, and clients manage their checkpointing logic. The change feed processor library provides automatic checkpointing and "at least once" semantics. Only the most recent change for a given document is included in the change log. Intermediate changes may not be available. The change feed is sorted by order of modification within each partition key value. There is no guaranteed order across partition-key values. Changes can be synchronized from any point-in-time, that is, there is no fixed data retention period for which changes are available. Changes are available in chunks of partition key ranges. This capability allows changes from large collections to be processed in parallel by multiple consumers/servers. Applications can request for multiple change feeds simultaneously on the same collection
- Changes are persistent in Azure Cosmos DB and can be processed asynchronously. Changes to documents within a collection are available immediately in the change feed. Each change to a document appears exactly once in the change feed, and clients manage their checkpointing logic. The change feed processor library provides automatic checkpointing and "at least once" semantics. Only the most recent change for a given document is included in the change log. Intermediate changes may not be available. The change feed is sorted by order of modification within each partition key value. There is no guaranteed order across partition-key values. Changes can be synchronized from any point-in-time, that is, there is no fixed data retention period for which changes are available. Changes are available in chunks of partition key ranges. This capability allows changes from large collections to be processed in parallel by multiple consumers/servers. Applications can request for multiple change feeds simultaneously on the same collection
- Changes are persistent in Azure Cosmos DB and can be processed asynchronously. Changes to documents within a collection are available immediately in the change feed. Each change to a document appears exactly once in the change feed, and clients manage their checkpointing logic. The change feed processor library provides automatic checkpointing and "at least once" semantics. Only the most recent change for a given document is included in the change log. Intermediate changes may not be available. The change feed is sorted by order of modification within each partition key value. There is no guaranteed order across partition-key values. Changes can be synchronized from any point-in-time, that is, there is no fixed data retention period for which changes are available. Changes are available in chunks of partition key ranges. This capability allows changes from large collections to be processed in parallel by multiple consumers/servers. Applications can request for multiple change feeds simultaneously on the same collection
- Changes are persistent in Azure Cosmos DB and can be processed asynchronously. Changes to documents within a collection are available immediately in the change feed. Each change to a document appears exactly once in the change feed, and clients manage their checkpointing logic. The change feed processor library provides automatic checkpointing and "at least once" semantics. Only the most recent change for a given document is included in the change log. Intermediate changes may not be available. The change feed is sorted by order of modification within each partition key value. There is no guaranteed order across partition-key values. Changes can be synchronized from any point-in-time, that is, there is no fixed data retention period for which changes are available. Changes are available in chunks of partition key ranges. This capability allows changes from large collections to be processed in parallel by multiple consumers/servers. Applications can request for multiple change feeds simultaneously on the same collection
- Changes are persistent in Azure Cosmos DB and can be processed asynchronously. Changes to documents within a collection are available immediately in the change feed. Each change to a document appears exactly once in the change feed, and clients manage their checkpointing logic. The change feed processor library provides automatic checkpointing and "at least once" semantics. Only the most recent change for a given document is included in the change log. Intermediate changes may not be available. The change feed is sorted by order of modification within each partition key value. There is no guaranteed order across partition-key values. Changes can be synchronized from any point-in-time, that is, there is no fixed data retention period for which changes are available. Changes are available in chunks of partition key ranges. This capability allows changes from large collections to be processed in parallel by multiple consumers/servers. Applications can request for multiple change feeds simultaneously on the same collection
- As a foundational Azure Service, Security and Compliance is an important aspect of Azure Cosmos DB. As I previously mentioned, several high value Microsoft services that have stringent compliance and security requirements are built on Azure Cosmos DB. All of the data is encrypted at rest and it is enabled by default. We made sure that we continue to provide the performance guarantees even with encryption enabled and this is transparent to your application. The service is HIPAA , PCI, ISO certified and other compliance certifications are in the process with all of them estimated to be completed by next month.