solutions architect professional Flashcards
Amazon kinesis use case
large amount of data
Kinesis is SQS on steroids
asynchronous tasks
single direction only
Kinesis firehose
is more for archiving. Proessing latency of 60seconds or higher.
Kinesis datastreams
custom processing per incoming record
sub-1 second processing latency
Choice of stream processing frameworks
Amazon SQS queues
Queues in each direction,
Changing the instance type will require restart
Use multi-az to reduce impact
With alias record points to one of the services
User is going to make the first request. Everything in the backgroupd Route53 will get the IP address and return the ip address. With alias you cannot return multiple ip addresses
redis is similar to multi-AZ
redis can scale up but not out. Once scaled up , cannot scale down( this may change)
Amazon DynamoDB
Can scale-out..
DynamoDB cache
You can use elastic cache. lower latency.
DynamoDB accelerator(DAX)
write-through cache. Lot of times elastic cache is cheper. But with elastic cache your application has to manage the cache but DAX is transparent
DynamoDB accelerator(DAX)
write-through cache. Lot of times elastic cache is cheper. But with elastic cache your application has to manage the cache but DAX is transparent
Amazon SQS and Dynamo DB
Stick SQS in front of Dynamo DB instead of scaling out Dynamo Db
DynamoDB global tables
it is kind of multi-master. app access the gloal table
OAI
Origin access identity