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As organizations become increasingly dependent on data to drive their operations, the seamless integration of this data has become essential for realizing successful digital transformation, reinforcing the notion that effective data management is a prerequisite for such transformation. In this complex landscape, organizations encounter various obstacles, including the need to break down information silos, ensure swift processing of a growing array of data types—ranging from structured to unstructured—manage substantial data volumes, and facilitate real-time data ingestion for prompt decision-making. Additionally, they must remain vigilant regarding the costs tied to their data infrastructure. In this context, Stambia presents a robust solution that addresses a wide range of data processing requirements, offering deployment options in both cloud and on-premises environments while effectively managing and optimizing expenses related to data ownership and transformation. This versatile strategy not only supports the smooth integration of data across diverse platforms but also significantly improves the overall efficiency of digital operations, allowing organizations to adapt and compete in an ever-evolving data-driven landscape. By leveraging such solutions, organizations can unlock new opportunities while enhancing their responsiveness to market demands.
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
AWS App Mesh
Amazon DynamoDB
Amazon EC2
Amazon EMR
Amazon RDS
Amazon S3
EC2 Spot
Functionize
OpenText Analytics Database (Vertica)
SquaredUp
Integrations Supported
AWS App Mesh
Amazon DynamoDB
Amazon EC2
Amazon EMR
Amazon RDS
Amazon S3
EC2 Spot
Functionize
OpenText Analytics Database (Vertica)
SquaredUp
API Availability
Has API
API Availability
Has API
Pricing Information
$20,000 one-time fee
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
Stambia
Date Founded
2009
Company Location
France
Company Website
www.stambia.com/en/product
Company Facts
Organization Name
Amazon
Date Founded
1994
Company Location
United States
Company Website
aws.amazon.com/datapipeline/
Categories and Features
ETL
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control
Categories and Features
ETL
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control