About
AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. AWS Glue provides all the capabilities needed for data integration so that you can start analyzing your data and putting it to use in minutes instead of months. Data integration is the process of preparing and combining data for analytics, machine learning, and application development. It involves multiple tasks, such as discovering and extracting data from various sources; enriching, cleaning, normalizing, and combining data; and loading and organizing data in databases, data warehouses, and data lakes. These tasks are often handled by different types of users that each use different products. AWS Glue runs in a serverless environment. There is no infrastructure to manage, and AWS Glue provisions, configures, and scales the resources required to run your data integration jobs.
|
About
The core technology to enable modern data integration and data management solutions. Quickly connect disparate structured and unstructured sources. Catalog your entire data ecosystem. Data stays in the sources and it is accessed on demand, with no need to create another copy. Build data models that suit the needs of the consumer, even across multiple sources. Hide the complexity of your back-end technologies from the end users. The virtual model can be secured and consumed using standard SQL and other formats like REST, SOAP and OData. Easy access to all types of data. Full data integration and data modeling capabilities. Active Data Catalog and self-service capabilities for data & metadata discovery and data preparation. Full data security and data governance capabilities. Fast intelligent execution of data queries. Real-time data delivery in any format. Ability to create data marketplaces. Decoupling of business applications from data systems to facilitate data-driven strategies.
|
About
Traditional methods of integrating mainframe data, ETL, data warehouses, building connectors, are simply not fast, accurate, or efficient enough for business today. More data than ever before is being created and stored on the mainframe, leaving these old methods further behind. Only data virtualization can close the ever-widening gap to automate the process of making mainframe data broadly accessible to developers and applications. You can curate (discover and map) your data once, then virtualize it for use anywhere, again and again. Finally, your data scales to your business ambitions. Data virtualization on z/OS eliminates the complexity of working with mainframe resources. Using data virtualization, you can knit data from multiple, disconnected sources into a single logical data source, making it much easier to connect mainframe data with your distributed applications. Combine mainframe data with location, social media, and other distributed data.
|
About
Simple, fast, safe, and compiled. For developing maintainable software. Simple language for building maintainable programs. You can learn the entire language by going through the documentation over a weekend, and in most cases, there's only one way to do something. This results in simple, readable, and maintainable code. This results in simple, readable, and maintainable code. Despite being simple, V gives a lot of power to the developer and can be used in pretty much every field, including systems programming, webdev, gamedev, GUI, mobile, science, embedded, tooling, etc. V is very similar to Go. If you know Go, you already know 80% of V. Bounds checking, No undefined values, no variable shadowing, immutable variables by default, immutable structs by default, option/result and mandatory error checks, sum types, generics, and immutable function args by default, mutable args have to be marked on call.
|
|||
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
|||
Audience
Anyone looking for a scalable and serverless data integration solution
|
Audience
Medium to large organizations that need a data integration platform
|
Audience
Businesses looking for a tool to empower and optimize sales through enhanced customer insights
|
Audience
Developers interested in a language for building maintainable programs
|
|||
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
|||
API
Offers API
|
API
Offers API
|
API
Offers API
|
API
Offers API
|
|||
Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
|||
Pricing
No information available.
Free Version
Free Trial
|
Pricing
No information available.
Free Version
Free Trial
|
Pricing
No information available.
Free Version
Free Trial
|
Pricing
Free
Free Version
Free Trial
|
|||
Reviews/
|
Reviews/
|
Reviews/
|
Reviews/
|
|||
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
|||
Company InformationAmazon
Founded: 1994
United States
aws.amazon.com/glue
|
Company InformationDenodo Technologies
Founded: 1999
United States
www.denodo.com/en
|
Company InformationRocket
Founded: 1990
United States
www.rocketsoftware.com/product-categories/data-virtualization
|
Company InformationV Programming Language
United States
vlang.io
|
|||
Alternatives |
Alternatives |
Alternatives |
Alternatives |
|||
|
|
|
|
|||
|
||||||
|
||||||
|
|
|
|
|||
Categories |
Categories |
Categories |
Categories |
|||
ETL Features
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control
|
Integration Features
Dashboard
ETL - Extract / Transform / Load
Metadata Management
Multiple Data Sources
Web Services
|
|||||
Integrations
Amazon DataZone
Amazon EC2
Amazon SageMaker Studio
Amazon SageMaker Unified Studio
Amazon Security Lake
Amazon Web Services (AWS)
CData Connect
Cosmian
Microsoft Azure
MongoDB
|
Integrations
Amazon DataZone
Amazon EC2
Amazon SageMaker Studio
Amazon SageMaker Unified Studio
Amazon Security Lake
Amazon Web Services (AWS)
CData Connect
Cosmian
Microsoft Azure
MongoDB
|
Integrations
Amazon DataZone
Amazon EC2
Amazon SageMaker Studio
Amazon SageMaker Unified Studio
Amazon Security Lake
Amazon Web Services (AWS)
CData Connect
Cosmian
Microsoft Azure
MongoDB
|
Integrations
Amazon DataZone
Amazon EC2
Amazon SageMaker Studio
Amazon SageMaker Unified Studio
Amazon Security Lake
Amazon Web Services (AWS)
CData Connect
Cosmian
Microsoft Azure
MongoDB
|
|||
|
|
|
|