AWS Snowball User Guide
| P (p. 143) | Q (p. 146) | R (p. 146) | S (p. 149) | T (p. 155) | U (p. 157) | V (p. 158) | W (p. 159) | X, Y,
Z (p. 159)
range GET A request that specifies a byte range of data to get for a download. If an object is
large, you can break up a download into smaller units by sending multiple range
GET requests that each specify a different byte range to GET.
raw email A type of sendmail request with which you can specify the email headers and
MIME types.
RDS See Amazon Relational Database Service (Amazon RDS).
read replica Amazon RDS (p. 113): An active copy of another DB instance. Any updates to
the data on the source DB instance are replicated to the read replica DB instance
using the built-in replication feature of MySQL 5.1.
real-time predictions Amazon Machine Learning: Synchronously generated predictions for individual
data observations.
See Also batch prediction.
receipt handle Amazon SQS (p. 113): An identifier that you get when you receive a message
from the queue. This identifier is required to delete a message from the queue or
when changing a message's visibility timeout.
receiver The entity that consists of the network systems, software, and policies that
manage email delivery for a recipient (p. 147).
recipient Amazon Simple Email Service (Amazon SES) (p. 113): The person or entity
receiving an email message. For example, a person named in the "To" field of a
message.
Redis A fast, open source, in-memory key-value data structure store. Redis comes with
a set of versatile in-memory data structures with which you can easily create a
variety of custom applications.
reference A means of inserting a property from one AWS resource (p. 148) into another.
For example, you could insert an Amazon EC2 (p. 111) security group (p. 151)
property into an Amazon RDS (p. 113) resource.
Region A named set of AWS resource (p. 148)s in the same geographical area. A Region
comprises at least two Availability Zone (p. 116)s.
regression model Amazon Machine Learning: Preformatted instructions for common data
transformations that fine-tune machine learning model performance.
regression model A type of machine learning model that predicts a numeric value, such as the exact
purchase price of a house.
regularization A machine learning (ML) parameter that you can tune to obtain higher-quality
ML models. Regularization helps prevent ML models from memorizing training
data examples instead of learning how to generalize the patterns it sees (called
overfitting). When training data is overfitted, the ML model performs well on the
training data but does not perform well on the evaluation data or on new data.
replacement environment The instances in a deployment group after the AWS CodeDeploy blue/green
deployment.
replica shard See shard.
reply path The email address to which an email reply is sent. This is different from the return
path (p. 149).
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