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What is Amazon DynamoDB Accelerator (DAX)?
Amazon DynamoDB is designed to efficiently manage large-scale operations while maintaining outstanding performance metrics. The response times associated with DynamoDB are remarkably low, typically registering in the single-digit millisecond range. However, certain applications require even faster response times, pushing into the microsecond territory, which is where DynamoDB Accelerator (DAX) becomes essential, providing swift access to eventually consistent data. DAX streamlines both the management of operations and the development of applications by functioning as a fully managed service that retains complete API compatibility with DynamoDB, enabling existing applications to require only minor modifications. For workloads characterized by a high volume of reads or those that face unexpected surges in demand, DAX significantly boosts throughput and can lead to reduced operational costs by decreasing the necessity for excessive read capacity units. This capability is especially beneficial for applications that often access the same keys, empowering them to operate efficiently even as load conditions fluctuate. By leveraging DAX, developers can ensure that their applications remain responsive and cost-effective, making it an ideal choice for tackling performance challenges in high-demand environments. Ultimately, DAX enhances the overall user experience by providing rapid data retrieval while optimizing resource utilization.
What is AWS Data Pipeline?
AWS Data Pipeline is a cloud service designed to facilitate the dependable transfer and processing of data between various AWS computing and storage platforms, as well as on-premises data sources, following established schedules. By leveraging AWS Data Pipeline, users gain consistent access to their stored information, enabling them to conduct extensive transformations and processing while effortlessly transferring results to AWS services such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR. This service greatly simplifies the setup of complex data processing tasks that are resilient, repeatable, and highly dependable. Users benefit from the assurance that they do not have to worry about managing resource availability, inter-task dependencies, transient failures, or timeouts, nor do they need to implement a system for failure notifications. Additionally, AWS Data Pipeline allows users to efficiently transfer and process data that was previously locked away in on-premises data silos, which significantly boosts overall data accessibility and utility. By enhancing the workflow, this service not only makes data handling more efficient but also encourages better decision-making through improved data visibility. The result is a more streamlined and effective approach to managing data in the cloud.
Integrations Supported
Amazon DynamoDB
Amazon EC2
AWS App Mesh
Amazon EMR
Amazon RDS
Amazon S3
Amazon Web Services (AWS)
EC2 Spot
Functionize
Kleene
Integrations Supported
Amazon DynamoDB
Amazon EC2
AWS App Mesh
Amazon EMR
Amazon RDS
Amazon S3
Amazon Web Services (AWS)
EC2 Spot
Functionize
Kleene
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
$1 per month
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Amazon
Date Founded
1994
Company Location
United States
Company Website
aws.amazon.com/es/dynamodb/dax/
Company Facts
Organization Name
Amazon
Date Founded
1994
Company Location
United States
Company Website
aws.amazon.com/datapipeline/
Categories and Features
Categories and Features
ETL
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control