re:Invent 2019: What is new in AWS? - Serverless Turkey meetup

8f43892395260c6ad14618987099ddcc?s=47 Serhat Can
December 25, 2019

re:Invent 2019: What is new in AWS? - Serverless Turkey meetup

We talk about the new features and products announced at re:Invent 2019.

8f43892395260c6ad14618987099ddcc?s=128

Serhat Can

December 25, 2019
Tweet

Transcript

  1. 1.

    © 2019, Amazon Web Services, Inc. or its affiliates. All

    rights reserved. What’s New in AWS
 re:Invent 2019 Serhat CAN - Technical Evangelist at Atlassian Serkan ÖZAL - CEO and Founder at Thundra
  2. 4.

    Categories Capabilities Options Broadest and deepest platform choice General purpose

    Burstable Compute intensive Memory intensive Storage (High I/O) Dense storage GPU compute Graphics intensive Elastic Block Store Elastic Inference 270 + instance types for virtually every workload and business need Choice of processor
 (AWS, Intel, AMD) Fast processors
 (up to 4.0 GHz) High memory footprint
 (up to 12 TiB) Instance storage
 (HDD and NVMe) Accelerated computing
 (GPUs and FPGA) Networking
 (up to 100 Gbps) Bare Metal Size 
 (Nano to 32xlarge)
  3. 6.

    Announcing AWS Graviton2 Processor First Arm-based processor 
 available in

    major cloud Built with 64-bit Arm Neoverse 
 cores with AWS-designed silicon using 7 nm manufacturing technology Up to 16 vCPUs,10 Gbps enhanced networking, 3.5 Gbps EBS bandwidth Built on 64-bit Arm Neoverse cores 
 with AWS-designed silicon using 16 nm manufacturing technology Up to 64 vCPUs, 25 Gbps enhanced networking, 18 Gbps EBS bandwidth 7x performance, 4x compute cores, 
 and 5x faster memory Graviton Processor Graviton2 Processor Enabling the best price/performance for your cloud workloads
  4. 7.

    Announcing Graviton2 based instances Coming in 2020 Available 
 in

    Preview M6g C6g R6g Up to 40% better price-performance for general purpose, compute intensive, and memory intensive workloads. Built for: General-purpose workloads such as application servers, mid-size data stores, and microservices. Built for: Compute intensive applications such as HPC, video encoding, gaming, and simulation workloads. Built for: Memory intensive workloads such as open-source databases, or in-memory caches. Local NVMe-based SSD storage options will also be available in general purpose (M6gd), compute-optimized (C6gd), and memory-optimized (R6gd) instances
  5. 8.

    Inference account for majority of machine learning Infrastructure costs %

    Infrastructure Cost Machine Learning Training (<10%) Machine Learning Inference (>90%)
  6. 9.

    Optimizing ML performance with a custom chip ASIC Chart for

    illustrative purposes GPU CPU Performance/WATT Applications
  7. 10.

    Featuring
 AW S Inferentia ML inference deployment options on Amazon

    EC2 Custom chip
 EC2 Inf1 instances GPU based
 EC2 G4 instances CPU based
 EC2 C5 instances Applications that leverage common ML frameworks Applications that
 require access to CUDA,
 CuDNN or TensorRT libraries Small models and low sensitivity to performance Powered by
 AWS Inferentia Amazon EC2 G4 instances based
 on NVIDIA T4 GPUs Intel Skylake CPUs
 Support for AVX-512/
 VNNI instruction set Best price/performance for
 ML inferencing in the cloud Up to 40% lower cost per inference and up to 3x higher throughput than G4 instances Available today! Launched! Launched!
  8. 11.

    EC2 Inf1 instances is built from the ground up by

    AWS High performance Low cost AWS Neuron AWS Custom 2nd Gen Intel Xeon Scalable Processors AWS Inferentia AWS Nitro
  9. 12.

    EC2 Inf1 instances are optimized for ML inferencing Object detection

    Natural language processing Personalization Speech recognition Image processing Fraud detection
  10. 14.

    EC2 Image Builder – benefits Quickly and easily automate the

    creation, management, and deployment of up-to-date and compliant “golden” VM images Improve service uptime by testing images before use in production Generate automation to build VM images with a GUI Reduce cost of building secure, compliant & up-to-date images
  11. 15.

    EC2 Image Builder – benefits Build golden VM images for

    use on AWS and on- premises Enforce policies on VM image usage across AWS accounts Works for both Windows and Linux
  12. 16.

    AWS Compute Optimizer Recommends optimal instances for EC2 and and

    EC2 Auto Scaling groups from 140+ instances from M, C, R, T, and X families
 Lower costs and improve workload performance Applies insights from millions of workloads to make recommendations Saves time comparing and selecting optimal resources for your workload
  13. 18.

    Same AWS-designed infrastructure as in 
 AWS data centers (built

    on AWS Nitro System) Fully managed, monitored, and operated by AWS as if in AWS Regions Single pane of management in the cloud providing the same APIs and tools as 
 in AWS Regions AWS Outposts: Bringing AWS on-premises
  14. 19.

    AWS Outposts Rack • Industry standard 42U rack • Fully

    assembled, ready to be rolled 
 into final position • Installed by AWS, simply plugged into power and network • Centralized redundant power conversion unit and DC distribution system for 
 higher reliability, energy efficiency, 
 easier serviceability • Redundant active components including 
 top of rack switches and hot spare hosts Dimensions • 24” Wide • 48” Deep • 80” Tall
  15. 20.

    Available in 2 Variants VMware APIs and services to 


    leverage existing skills, automation, and governance policies For customers running VMware 
 SDDC on-premises AWS APIs, services, and features 
 as in the AWS cloud EC2 and EBS with support for 
 services including RDS, ECS, EKS, 
 EMR, ALB, others Native AWS VMware Cloud on AWS
  16. 21.

    • Compute & Storage—Amazon EC2 instances 
 and EBS volumes

    • Networking—Amazon VPC • Database—Amazon Relational Database 
 Service (RDS) • Containers—Amazon Elastic Container Service 
 (ECS) & Amazon Elastic Kubernetes Service (EKS) • Data Processing—Amazon Elastic Map Reduce (EMR) Run AWS services locally
  17. 22.

    EC2 Auto Scaling Groups AWS CloudFormation CloudWatch CloudTrail Elastic BeanStalk

    Cloud9 and more… With the same AWS APIs & tools as in the AWS Region
  18. 23.

    AWS Local Zones • New type of AWS infrastructure deployment

    • Places compute, storage, database, and other services closer to customers • For demanding applications that require single-digit latencies AWS infrastructure at the edge Local compute, storage, database, and other services Connect to services in AWS Regions Deliver new low latency apps NEW
  19. 24.

    AWS Wavelength • Extends AWS infrastructure to 5G networks •

    Run latency-sensitive portions of applications in “Wavelength Zones,” and seamlessly connect to the rest of your applications and the full breadth of services in AWS • Same AWS APIs, tools, and functionality • Global partner network NEW
  20. 26.

    Wavelength Zone Same AWS-designed infrastructure as in 
 AWS data

    centers Hosted in a site within a CSP partner network Integrated into the CSP 5G Network Managed and monitored from an AWS region
  21. 28.

    AWS container services landscape Management Deployment, Scheduling, Scaling & Management

    of containerized applications Hosting Where the containers run Amazon Elastic Container Service Amazon Elastic Kubernetes Service Amazon EC2 AWS Fargate Image Registry Container Image Repository Amazon Elastic Container Registry
  22. 29.

    Managed by AWS No EC2 Instances to provision, scale or

    manage Elastic Scale up & down seamlessly. Pay only for what you use Integrated with the AWS ecosystem: VPC Networking, Elastic Load Balancing, IAM Permissions, CloudWatch and more. Run Kubernetes pods or ECS tasks. AWS Fargate
  23. 30.

    EKS for Fargate (Serverless Kubernetes) Bring existing pods Production ready

    Right-Sized and Integrated You don’t need to change your existing pods. Fargate works with existing workflows and services that run on Kubernetes. Launch ten or tens of thousands 
 of pods in seconds. Easily run pods across multiple AZs for high-availability. Only pay for the resources you need to run your pods. Includes native AWS integrations for networking, and security. Fargate runs tens of millions of containers for AWS customers every week
  24. 31.

    Provisioned Concurrency for AWS Lambda Provisioned Concurrency keeps functions initialized

    and hyper-ready to respond in double-digit milliseconds. Customers fully control when or how long to enable Provisioned Concurrency. Taking advantage of Provisioned Concurrency requires no changes to your code.. Serverless LEARN MORE CON213-L: Leadership session: Using containers and serverless to accelerate modern application development. Wednesday, 9:15am Ideal for latency-sensitive applications You fully control when to enable it No changes required to your code Fully serverless PREVIEW NEW
  25. 32.

    Amazon RDS Proxy 
 Fully managed, highly available database proxy

    Supports new scale of serverless application connections Pools and shares database connections Preserve connections during database failovers Manages DB credentials with Secrets Manager and IAM Fully managed—No provisioning, patching, management RDS Proxy Applications RDS 
 Database Instance Connection Pooling PREVIEW NEW
  26. 33.

    AWS Gateway HTTP APIs • Save up to 70% compared

    to REST APIs
 HTTP APIs are optimized for building APIs that proxy to AWS Lambda functions or HTTP backends, making them ideal for serverless workloads. • Significantly faster
 Up to 50% latency reduction. HTTP APIs only support API proxy functionality. For customers who want API proxy functionality and API management features in a single solution, they can use REST APIs from Amazon API Gateway. Serverless PREVIEW NEW https://aws.amazon.com/tr/blogs/compute/announcing-http-apis-for-amazon-api-gateway/
  27. 34.

    Amazon EventBridge with Schema Registry Why? As customer’s applications grows

    and more teams write custom events, there is more effort required to find events and their structure as well as to write code to react to those events. What? The Amazon EventBridge schema registry stores event structure - or schema - in a shared central location and maps those schema to code for Java, Python, and Typescript so it’s easy to use events as objects in their code. How? Schema from their event bus can be automatically added to the registry through the schema discovery feature. Customers can connect to and interact with schema registry from the AWS console, APIs, or through the SDK Toolkits for Jetbrains (Intellij, PyCharm, Webstorm, Rider) and VS Code. Serverless PREVIEW NEW
  28. 35.

    Other AWS Lambda announcements • Parallelization Factor for Kinesis and

    DynamoDB Event Sources • Allows you to process one shard of a Kinesis or DynamoDB data stream with more than one Lambda invocation simultaneously
 https://aws.amazon.com/tr/about-aws/whats-new/2019/11/aws-lambda-supports-parallelization-factor-for-kinesis-and-dynamodb-event-sources/
 • Failure-Handling Features for Kinesis and DynamoDB Event Sources • Allow you to customize responses to data processing failures and build more resilient stream processing applications
 https://aws.amazon.com/tr/about-aws/whats-new/2019/11/aws-lambda-supports-failure-handling-features-for-kinesis-and-dynamodb-event-sources/ • Destinations for Asynchronous Invocations • Allows you to gain visibility to asynchronous invocation result and route the result to an AWS service without writing code
 https://aws.amazon.com/tr/about-aws/whats-new/2019/11/aws-lambda-supports-destinations-for-asynchronous-invocations/ • Language support for Java 11, Node.js 12, Python 3.8
 • SQS FIFO as an event source Serverless
  29. 38.

    Amazon SageMaker Build, Train, Deploy Machine Learning Models Quickly at

    Scale Amazon SageMaker Ground Truth Algorithms & Frameworks Notebooks Training & Tuning Deployment & Hosting Reinforcement Learning ML Marketplace Neo
  30. 39.

    Amazon SageMaker Addressing challenges to machine learning First fully integrated

    development environment (IDE) for machine learning Amazon SageMaker Studio Enhanced notebook experience with quick-start & easy collaboration Amazon SageMaker 
 Notebooks (Preview) Automatic debugging, analysis, and alerting Amazon SageMaker Debugger Experiment management system to organize, track & compare thousands of experiments Amazon SageMaker 
 Experiments Model monitoring to detect deviation in quality & take corrective actions Amazon SageMaker Model Monitor Automatic generation of ML models with 
 full visibility & control Amazon SageMaker Autopilot
  31. 40.

    Build, train, and deploy machine learning models quickly at scale

    Amazon SageMaker Studio IDE Amazon SageMaker Ground Truth Algorithms and Frameworks SageMaker Notebooks SageMaker Experiments Training and Tuning Deployment and Hosting Reinforcement Learning ML Marketplace SageMaker Debugger SageMaker Autopilot SageMaker Model Monitor NEW! NEW! NEW! NEW! NEW! NEW! Neo
  32. 41.

    Train, tune, and deploy models in SageMaker Orchestrate ML workloads

    from your Kubernetes environments Create pipelines and workflows in Kubernetes Fully managed infrastructure in SageMaker Introducing Amazon SageMaker Operators for Kubernetes Kubernetes customers can now train, tune, & deploy models in Amazon SageMaker NEW
  33. 42.

    Introducing Amazon SageMaker Managed Spot Training Save up to 90%

    in training costs Visualize your cost savings for each trainin job Save training costs compared to Amazon EC2 On-Demand instances Spot capacity is managed & interruptions are automatically handled Get support for built-in and your own algorithms & frameworks All SageMaker training capabilities No more interruptionsSupport for algorithms & frameworks Full visibility Take advantage of Automatic Model Tuning & Reinforcement Up to 90% savings
  34. 43.

    Introducing Amazon Fraud Detector Identify potentially fraudulent online activities such

    as online payment fraud and the creation of fake accounts PREVIEW NEW Step 1: Upload your historical fraud datasets to Amazon S3
 Step 2: Select from pre-built fraud detection model templates 
 Step 3: The model template uses your historical data as input to build a custom model. The model template inspects and enriches data, performs feature engineering, selects algorithms, trains and tunes your model, and hosts the model 
 Step 4: Create rules to either accept, review, or collect more information based on model predictions 
 Step 5: Call the Amazon Fraud Detector API from your online application to receive real-time fraud predictions and take action based on your configured detection rules.
  35. 44.

    Introducing Amazon CodeGuru New machine learning service to automate code

    reviews and 
 identify your most expensive line of code PREVIEW NEW Find your most expensive lines of code Trained on decades of knowledge and experience Catch the code issue today – don't wait to get paged
  36. 45.

    Introducing Amazon Kendra Highly accurate and easy to use enterprise

    search service 
 that’s powered by machine learning PREVIEW NEW
  37. 47.

    Amazon S3 Access Points Simplify managing data access at scale

    for shared data sets on Amazon S3. With S3 Access Points, you can easily create hundreds of access points per bucket, each with a name and permissions customized for the application. This represents a new way of provisioning access to shared data sets. GA NEW
  38. 48.

    AWS Access Analyzer for S3—New An S3 capability to generate

    comprehensive findings if your resource policies grant public or cross-account access Continuously identify resources with overly broad permissions across your entire AWS organization Resolve findings by updating policies to protect your resources from unintended access before it occurs, or archive findings for intended access Access Analyzer for S3
  39. 49.

    Our portfolio
 Broad and deep portfolio, purpose-built for builders S3/Glacier

    Glue ETL & Data Catalog Lake Formation Data Lakes Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams | Managed Streaming for Kafka Data Movement Data Lake Analytics Redshift Data warehousing EMR Hadoop + Spark Kinesis Data Analytics 
 Real time Elasticsearch Service Operational Analytics Athena Interactive analytics NEW NEW NEW AQUA EMR on Outposts UltraWarm Business Intelligence & Machine Learning Data Exchange Data exchange NEW QuickSight Visualizations SageMaker ML Comprehend NLP Transcribe Speech-to-text Textract Extract text Personalize Recommendation Forecast Forecasts Translate Translation CodeGuru Code reviews Kendra Enterprise search NEW NEW Analytics Redshift Data warehousing EMR Hadoop + Spark Kinesis Data Analytics 
 Real time Elasticsearch Service Operational Analytics Athena Interactive analytics NEW NEW NEW AQUA EMR on Outposts UltraWarm Databases RDS MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, RDS on VMware Aurora MySQL, PostgreSQL DynamoDB Key value, Document ElastiCache
 Redis, Memcached Neptune Graph Timestream Time Series QLDB Ledger Database Managed Apache Cassandra Service Wide column NEW DocumentDB Document NEW NEW RDS Proxy RDS on Outposts RDS MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, RDS on VMware Aurora MySQL, PostgreSQL DynamoDB Key value, Document ElastiCache
 Redis, Memcached Neptune Graph Timestream Time Series QLDB Ledger Database Analytics Databases Managed Blockchain Blockchain Templates Blockchain Managed Apache Cassandra Service Wide column NEW DocumentDB Document Redshift Data warehousing EMR Hadoop + Spark Kinesis Data Analytics 
 Real time Elasticsearch Service Operational Analytics Athena Interactive analytics NEW NEW NEW NEW NEW AQUA EMR on Outposts UltraWarm RDS Proxy RDS on Outposts
  40. 50.

    Data warehousing: Amazon Redshift
 Best performance, most scalable 3x faster

    with RA3* 10x faster with AQUA* Adds unlimited compute capacity on-demand to meet unlimited concurrent access Lowest cost Cost-optimized workloads 
 by paying compute and 
 storage separately 1/10th cost of Traditional 
 DW at $1000/TB/year Up to 75% less than other cloud data warehouses & predictable costs Data lake & 
 AWS integration Analyze exabytes of data across data warehouse, data lakes, and operational database Query data across various analytics services Most secure 
 & compliant AWS-grade security (eg. VPC, encryption with KMS, CloudTrail) All major certifications such 
 as SOC, PCI, DSS, ISO, 
 FedRAMP, HIPPA First and most popular cloud data warehouse *vs other cloud DWs
  41. 51.

    Amazon Redshift on RA3 instances 
 Optimize your data warehouse

    by paying for compute and storage separately Delivers 3x the performance of existing cloud DWs DS2 customers can migrate and get 2x performance 
 and 2x storage for the same cost Automatically scales your DW storage capacity Supports workloads up to 8 PB (compressed) COMPUTE NODE
 (RA3) SSD Cache S 3 S TO R A G E COMPUTE NODE
 (RA3) SSD Cache COMPUTE NODE
 (RA3) SSD Cache COMPUTE NODE
 (RA3) SSD Cache Managed storage $/node/hour $/TB/month GA NEW
  42. 52.

    AQUA—Advanced Query Accelerator
 Redshift runs 10x faster than any other

    cloud data warehouse without increasing cost Compute Node Compute Node Compute Node Compute Node Parallel execution Storage Node Storage Node Storage Node Storage Node Multi-tenant locally attached storage Custom ASIC and FPGA Custom ASIC and FPGA Custom ASIC and FPGA Custom ASIC and FPGA 100% compatible with the current version of Redshift AQUA brings compute to the storage layer 
 so data doesn’t have to move back and forth High-speed cache on top of S3 scales out to process data in parallel across many nodes AWS custom-designed analytics processors accelerate data compression, encryption, and data processing COMING 
 IN 2020 NEW Amazon S3
  43. 53.

    Amazon EMR
 Easily Run Spark, Hadoop, Hive, Presto, HBase, and

    more big data apps on AWS Low cost 50–80% reduction in costs with EC2 Spot and Reserved Instances Per-second billing for flexibility Use S3 storage Process data in S3
 securely with high performance using the EMRFS connector Latest versions Updated with latest open source frameworks within 30 days Fully managed no cluster setup, node provisioning, cluster tuning Easy
  44. 54.

    Performance Improvements in Spark for Amazon EMR
 Performance optimized runtime

    for Apache Spark, 2.6x faster performance at 1/10th the cost *Based on TPC-DS 3 TB Benchmarking running 6 node C4x8 extra large clusters and EMR 5.28, Spark 2.4 Runtime total on 104 queries (seconds— lower is better) t runtime) r runtime) h runtime) 0 7.000 14.000 21.000 28.000 10164 16478 26478 Runtime optimized for Apache Spark performance 100% compliant with Apache Spark APIs Best performance 2.6x faster than Spark with EMR without runtime 1.6x faster than 3rd party Managed Spark (with their runtime) Lowest price 1/10th the cost of 3rd party Managed Spark (with their runtime) NEW
  45. 55.

    Amazon Athena Federated Query
 Run SQL queries on data spanning

    multiple data stores Redshift Data warehousing ElastiCache
 Redis Aurora MySQL, PostgreSQL DynamoDB Key value, Document DocumentDB Document On-premises SQL S3/Glacier Run connectors in AWS Lambda: no servers to manage Run SQL queries on relational, non-relational, object, 
 or custom data sources; in the cloud or on-premises Open Source Connectors for common data sources Build connectors to custom data sources PREVIEW NEW
  46. 56.

    UltraWarm for Amazon Elasticsearch Service 
 A new warm storage

    tier for Elasticsearch service Kibana Dashboard Amazon Elasticsearch Service domain Data Node Data Node Data Node Data Node Application Load Balancer Seamlessly extends Elasticsearch service 90% lower cost Scale up to 3PB per domain Analyze years of operational data Amazon S3 UltraWarm Node UltraWarm Node UltraWarm Node Active 
 Master Node Backup
 Master Node Backup
 Master Node Queries PREVIEW NEW
  47. 57.

    Data exchange: AWS Data Exchange
 Easily find and subscribe to

    3rd-party data in the cloud
 Efficiently access 
 3rd party data Simplifies access to data: No need to receive physical media, manage FTP credentials, or integrate with different APIs Minimize legal reviews and negotiations Quickly find diverse 
 data in one place >1,000 data products >80 data providers including include Dow Jones, Change Healthcare, Foursquare, Dun & Bradstreet, Thomson Reuters, Pitney Bowes, Lexis Nexis, and Deloitte Easily analyze data Download or copy data to S3 Combine, analyze, and model with existing data Analyze data with EMR, Redshift, Athena, and AWS Glue GA NEW
  48. 58.

    Amazon Managed (Apache) Cassandra Service
 Scalable, highly available, and managed

    Cassandra-compatible database service
 No need to provision, configure, and operate large Cassandra clusters or add and remove 
 nodes manually No servers to manage Single-digit millisecond performance Scale tables up and down automatically based on application traffic Virtually unlimited 
 throughput and storage Single-digit millisecond performance at scale Apache 
 Cassandra-compatible Use the same application code, licensed drivers, and tools 
 built on Cassandra Simple migration Simple migration to Managed Cassandra Service for Cassandra databases on premises or on EC2 PREVIEW NEW
  49. 59.

    ML in Amazon Aurora, Athena, and QuickSight
 Bringing machine learning

    to databases, analytics and BI Incorporate ML into databases, analytics and BI Integrated with Amazon SageMaker & Comprehend ML predictions using standard SQL statements No ML expertise required Reduces time to getting predictions out of models S3 Comprehend Natural language processing Amazon SageMaker ML Aurora Database Athena Interactive analytics QuickSight BI Training SQL Select From Where Predictions NEW
  50. 60.

    Amazon Kinesis Video Streams WebRTC Analytics and Media Services Stream

    live media with ultra-low latency and enable two-way interactivity for millions of camera devices Standards Compliant Exchange audio, video, and data between devices, mobile, and web apps for real-time two-way interactivity Fully Managed Fully managed WebRTC signaling, TURN, and STUN services with easy to use SDKs Real-time, Two-way Interactivity Compliant with web and mobile platforms for easy plug-in free playback Low Latency Live Media Streaming Peer-to-peer audio and video live streaming with sub-1 second latency for playback N E W !
  51. 61.

    When to use which services Situation Solution Existing application •

    MySQL Amazon Aurora, RDS for MySQL • PostgreSQL Amazon Aurora, RDS for PostgreSQL • MariaDB Amazon Aurora, RDS for MariaDB • Oracle Use SCT to determine complexity Amazon Aurora, RDS for Oracle • SQL Server Use SCT to determine complexity Amazon Aurora, RDS for SQL Server • MongoDB Amazon DocumentDB • Cassandra Amazon Managed Apache Cassandra Service New application • If you can avoid relational features Amazon DynamoDB • If you need relational features Amazon Aurora In-memory store/cache • Amazon ElastiCache Time series data • Amazon Timestream Track every application change, crypto verifiable. Have a central trust authority • Amazon Quantum Ledger Database (QLDB) Don’t have a trusted central authority • Amazon Managed Blockchain Data Warehouse & BI • Amazon Redshift, Amazon Redshift Spectrum, and Amazon QuickSight Adhoc analysis of data in AWS or on-premises • Amazon Athena and Amazon QuickSight Apache Spark, Hadoop, HBase 
 (needle in a haystack type queries) • Amazon EMR Log analytics, operational monitoring, & search • Amazon Elasticsearch Service Real-time analytics • Amazon Kinesis and Amazon Managed Streaming for Kafka
  52. 63.

    NEW

  53. 64.

    AWS IAM Access Analyzer An IAM capability to generate comprehensive

    findings if your resource policies grant public or cross-account access 
 Continuously identify resources with overly broad permissions Resolve findings by updating policies to protect your resources from unintended access before it occurs, or archive findings for intended access AWS Identity and Access Management Access Analyzer NEW
  54. 65.

    Simplified Windows and SQL Server BYOL AWS License Manager now

    adds host management capabilities to simplify your ‘Bring your own license’ (BYOL) experience for software licenses, such as Windows and SQL Server, that require a dedicated physical server. NEW https://aws.amazon.com/tr/about-aws/whats-new/2019/12/aws-license-manager-adds-dedicated-host-management-capabilities/
  55. 66.

    Amazon Braket Fully managed service that makes it easy for

    scientists and developers to explore and experiment with quantum computing. Quantum Technology Single environment to design, test, and run quantum algorithms Experiment with a variety of quantum hardware technologies Run hybrid quantum and classical algorithms Get Expert Help
  56. 69.

    Thank you! © 2019, Amazon Web Services, Inc. or its

    affiliates. All rights reserved. Serhat CAN - Technical Evangelist at Atlassian Serkan ÖZAL - CEO and Founder at Thundra