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![Traditionally, analytics looked like this
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](https://image.slidesharecdn.com/eventopsroadshowv1-190314182532/85/Big-Data-Analytics-Machine-Learning-e-Inteligencia-Artificial-12-320.jpg)
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Big Data Analytics, Machine Learning e Inteligência Artificial
The document discusses Amazon Web Services' (AWS) approach to data management. It highlights AWS's scale, speed, and innovation as well as its customer obsession. It outlines AWS's portfolio of data and analytics services including storage, databases, data lakes, data warehouses, machine learning services, and data movement. It provides examples of how customers can build data lakes, enable self-service analytics, and simplify security management using AWS services.
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Big Data Analytics, Machine Learning e Inteligência Artificial
- 1. Data has gravity– The AWS Data Management approach Adolfo Abreu Business Development Latam Team Database, Big Data, Analytics, AI, ML, IoT
- 2. What do youknow about Amazon? © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.2 Unprecedented SCALE Hyper SPEED Relentless INNOVATION Customer OBSESSION
- 3. Amazon Web Services ©2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.3 Applying amazon.com concepts to infrastructure Unprecedented SCALE Hyper SPEED Relentless INNOVATION Customer OBSESSION Infinite IT resources available in minutes 18 regions, 55 availability zones New capabilities launched daily Working Backwards from the customer PR FAQ
- 4. Personalized Recommendations Fulfillment automation & InventoryManagement Drones Voice driven Interactions Inventing entirely new Customer experiences At Amazon, we’ve been making investments in Machine Learning for the last 20 years
- 5. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved.
- 8. Analytics Redshift Data warehousing EMR Hadoop +Spark Athena Interactive analytics Kinesis Data Analytics Real time Elasticsearch Service Operational Analytics QuickSight SageMaker S3/Glacier Glue ETL & Data Catalog Lake Formation Data Lakes Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams Data Movement Business Intelligence & Machine Learning Data Lake Our portfolio - purpose-built for builders
- 9. Analytics QuickSight SageMaker S3/Glacier Glue ETL &Data Catalog Lake Formation Data Lakes Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams Data Movement Business Intelligence & Machine Learning Data Lake Redshift Data warehousing EMR Hadoop + Spark Kinesis Data Analytics Real time Elasticsearch Service Operational Analytics Athena Interactive analytics RDS MySQL, PostgreSQL, MariaDB, Oracle, SQL Server Aurora MySQL, PostgreSQL DynamoDB Key value, Document ElastiCache Redis, Memcached Neptune Graph Timestream Time Series QLDB Ledger Database RDS on VMware Databases Our portfolio - purpose-built for builders
- 10. Redshift Data warehousing EMR Hadoop +Spark Athena Interactive analytics Kinesis Data Analytics Real time Elasticsearch Service Operational Analytics RDS MySQL, PostgreSQL, MariaDB, Oracle, SQL Server Aurora MySQL, PostgreSQL QuickSight SageMaker DynamoDB Key value, Document ElastiCache Redis, Memcached Neptune Graph Timestream Time Series QLDB Ledger Database S3/Glacier Glue ETL & Data Catalog Lake Formation Data Lakes Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams Data Movement Analytics Databases Business Intelligence & Machine Learning Data Lake Managed Blockchain Blockchain Templates Blockchain RDS on VMware Our portfolio - purpose-built for builders
- 11. Quickly build new appsin the cloud Gain new insights “Lift and shift” existing apps to the cloud Three type of projects
- 12. Traditionally, analytics lookedlike this 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
- 13. Snowball Snowmobile Kinesis Data Firehose Kinesis DataStreams S3 Redshift EMR Athena Kinesis Elasticsearch Service Data lakes on AWS Kinesis Video Streams AI Services QuickSight 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
- 14. How it works S3 IAMKMS OLTP ERP CRM LOB Devices Web Sensors Social Kinesis Build data lakes quickly • Identify, crawl, and catalog sources • Ingest and clean data • Transform into optimal formats Simplify security management • Enforce encryption • Define access policies • Implement audit login Enable self-service and combined analytics • Analysts discover all data available for analysis from a single data catalog • Use multiple analytics tools over the same data Athena Redshift AI Services EMR QuickSight Data catalog
- 15. Relational Key-value DocumentIn-memory Graph Time-series Ledger DynamoDB NeptuneAmazon RDS Aurora CommercialCommunity Timestream QLDBElastiCacheDocumentDB AWS databases services
- 16. Amazon RDS Managed relationaldatabase service with a choice of six popular database engines 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
- 17. Amazon QuickSight First BIservice with pay-per-session pricing for everyone in your organization Serverless, cloud-powered BI service (no servers to manage) Scale from 10s of users to 100s of thousands of users Pay only for what you use • Readers: $0.30/30 min session with a $5/user/month max • Authors: $18/month/Author Integrates with S3, Athena, Redshift, RDS, Aurora, & EMR
- 18. AWS Directory Service MicrosoftAD Custom Date Format Dashboard Save As Aggregate Calculations Readers Groups Private VPC 25 GB SPICE tables Spark and Presto Connector Scheduled refresh Just In Time Provisioning One-click upgrade Search Totals Excel Custom Range 100+ new features released since launch Federated SSO Athena connector Export to CSV S3 Analytics Week Aggregation Aurora PostgreSQL Calculations in SPICE Cross Account S3 Access Aggregate Filters Hourly refresh Row level security Hourly refresh 10K Filter Values On-screen controls Redshift Spectrum Support KPI Chart Spark Connector AWS Directory Service AD Connector Tabular Reports Data labels URL Actions Combo Charts Audit logging with CloudTrail Geospatial maps Count Distinct Parameters Relative Date Filters Filter Groups Table calculations Snowflake Connector SaaS Connectors Teradata Connector HIPAA PCI compliance Amazon QuickSight has been innovating quickly
- 19. Amazon QuickSight—embedded dashboards Superchargeyour applications with embedded dashboards Fully interactive with drill down, filtering, & external links No servers to manage, no long-term commitments Pay for usage with pay-per-session reader pricing Easy embedding with JavaScript SDK
- 20. Discover all thehidden trends and anomalies on millions of metrics Amazon QuickSight—ML Insights Example: anomaly detection
- 21. “Sales for officesupplies in APAC was 15% above expected.” Amazon QuickSight—ML Insights Example: anomaly detection
- 22. “SMB Segment wasthe top contributor.” Amazon QuickSight—ML Insights Example: anomaly detection
- 23. “It’s significant becauseSMB typically only accounts for 30% of sales.” Amazon QuickSight—ML Insights Example: anomaly detection
- 24. QuickSight ML-powered forecastingTraditional BI forecasting Captures seasonality and upward trends Automatically excludes bad data High confidence band Captures only seasonality Missing upward trend Confidence band influenced by bad data QuickSight ML Insights vs. traditional BI forecasting VS.
- 25. Insights in plainlanguage narrative Embedded within your dashboard No more staring at dashboards for hours! Fully customizable to meet every need No coding needed. Easy-to-use UI templates. Amazon QuickSight—ML Insights Auto-narratives
- 26. Most enterprise database& analytics cloud customers
- 27. Most startup database& analytics cloud customers
- 28. Artificial Intelligence, Machine Learning,Deep Learning, Data Science #buzZZzzzWords… ?!
- 29. Artificial Intelligence • “thescience and engineering of making intelligent machines” (John McCarthy - ~1950s) Weak AI Strong AI (AGI) I like artificial intelligence Lubię sztuczną inteligencję
- 30. Machine Learning “is afield of computer science that gives computers the ability to learn without being explicitly programmed” (Arthur Samuel - ~1959)
- 31. Deep Learning • “isa subarea of machine learning that uses deep neural networks to model complex problems” Deep Learning Machine Learning Artificial Intelligence
- 32.
- 33. Centimeter-accurate positioningReal timeper pixel image segmentation Autonomous cars
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- 37. © 2018, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Find real value in raw data streams
- 38. Customer & IndustryMaturity © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Advanced knowledge Basic understanding of AI/ML Low or no knowledge of AI/ML
- 39. § Cross IndustryStandard Process for Data Mining § Current de facto process for doing Data Science § Highlights the cyclical and iterative natures of Data Science https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining Machine Learning modeling process
- 40. Artificial Intelligence @AWS Application Services Platform Services Frameworks & Infrastructure Apache MXNet PyTorchCognitive Toolkit Keras Caffe2 & Caffe TensorFlow AWS Deep Learning AMI (Ubuntu & Amazon Linux – Cuda 8 & 9) GPU (P2 & P3) MobileCPU IoT (Greengrass) Amazon Machine Learning Mechanical Turk Spark & EMR Vision: Rekognition Rekognition Video Speech: Polly Transcribe Language: Lex Translate Comprehend Textract Gluon SageMaker Ground Truth RL, Neo, Pipeline DeepLens DeepRacer Marketplace Time Series: Forecast Recommendation: Personalize Elastic Inference Inferentia RoboMaker
- 41. Thank you! © 2018,Amazon Web Services, Inc. or its Affiliates. All rights reserved.41