8 December 2017

Metaphacts

Metaphacts

AWS Neptune

AWS Neptune

AWS vs Azure

Azure 
AWS 
Storage (Blobs, Tables, Queues, Files) 
S3 
Virtual Machines 
EC2 
Autoscale 
AutoScaling 
Docker Virtual Machine Ext 
EC2 Container Service 
Blob Storage 
Elastic Block Storage 
HDInsight 
EMR 
Cloud Services Websites & Apps 
Elastic Beanstalk 
Backup 
Glacier 
Storsimple 
Storage Gateway 
Import export 
Import / export 
CDN 
Cloudfront 
SQL 
RDS 
DocumentDB 
DynamoDB 
Managed Cache (Redis) 
Elastic Cache 
Data Factory 
Data Pipeline / Glue
Virtual Network 
VPC 
ExpressRoute 
Direct Connect 
Traffic Manager 
Route53 
Load Balancing 
Elastic Load Balancing 
Active Directory 
IAM 
Multi-Factor 
Multi-Factor 
Operational Insights 
CloudTrail 
Application Insights 
CloudWatch 
Event Hubs 
Kinesis 
Notification Hubs 
SNS 
Key Vault 
Key Management Service 
Resource Manager 
Cloud Formation 
API Management 
API Gateway 
Automation 
Opsworks 
Batch 
SQS 
Search 
CloudSearch 
Service Bus 
SWF 
Stream Analytics 
Kinesis 
Biztalk 
SES 
Machine Learning (preview) 
Machine Learning 
Functions 
Lambda 
Gets more expensive as you use more on the 'you only pay for what you use model' (especially Linux instances) 
Less expensive as you use more on the 'you only pay for what you use model'  
(has plenty of linux options and windows options are priced the same as azure) 
Generally, slower in services 
Generally, faster in services 
Very confusing naming for services some of which are not even services but just reused applications in the cloud 
Service names are distinguishable and clear 
Azure charges by the minute 
AWS charges by the hour 
ML and data analytics options are not that great. 
Azure only has cosmodbhdinsight, event hub, storage, its AI services are not great either, tight coupling to azure windows services, not ideal for data science work - with azure for non-windows type work is not seamless. CosmoDB is a strong option –it supports documentdbmongodb, property graph, cassandra, table – not perfect but ok. 

Data Pipeline 
Kinesis 
S3 
Deep Learning library support + AMIs, 
Support for GPU instances 
More flexible DBs, But no multi-model option, Redshift, EMR (supports spark, flink, presto, hbase, hive, pig), glue, athenaelasticsearchcloudsearch, clustering is easier for linux machines (can you see running hadoop on windows?), AWS has neptune that supports property graphs, rdfsparql
tinkerpop.