Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Parquet was developed to provide the benefits of efficient, compressed columnar data representation to all projects within the Hadoop ecosystem. Designed with a focus on accommodating complex nested data structures, Parquet employs the record shredding and assembly technique outlined in the Dremel paper, which we consider to be a more effective strategy than merely flattening nested namespaces. This format supports highly efficient compression and encoding methods, and various projects have shown the significant performance improvements that arise from utilizing appropriate compression and encoding strategies for their datasets. Furthermore, Parquet enables the specification of compression schemes at the column level, ensuring its adaptability for future developments in encoding technologies. It is crafted to be accessible for any user, as the Hadoop ecosystem comprises a diverse range of data processing frameworks, and we aim to remain neutral in our support for these different initiatives. Ultimately, our goal is to empower users with a flexible and robust tool that enhances their data management capabilities across various applications.
Description
Scheme serves as a versatile general-purpose programming language that operates at a high level. It facilitates various operations on complex data structures such as strings, lists, and vectors, in addition to handling traditional data types like numbers and characters. Although often associated with symbolic computation, Scheme's extensive range of data types and its adaptable control structures enhance its versatility for numerous applications. Developers have utilized Scheme for a wide array of projects, including text editors, compilers, operating systems, graphic applications, expert systems, numerical computations, financial analysis software, virtual reality frameworks, and virtually any other conceivable application. Learning Scheme is relatively accessible due to its reliance on a limited set of syntactic forms and semantic principles, and the interactive features of most implementations promote hands-on experimentation. However, achieving a deep understanding of Scheme can be quite challenging, as its complexities unfold with deeper exploration. As a result, practitioners often find themselves continually learning and evolving their skills within this rich programming environment.
API Access
Has API
No
API Access
Has API
No
Integrations
Arroyo
Yes
Astera Dataprep
Yes
CSViewer
Yes
Ficstar
Yes
Flyte
Yes
Gable
Yes
Gravity Data
Yes
Indexima Data Hub
Yes
Mage Platform
Yes
OrcaSheets
Yes
Integrations
Arroyo
No
Astera Dataprep
No
CSViewer
No
Ficstar
No
Flyte
No
Gable
No
Gravity Data
No
Indexima Data Hub
No
Mage Platform
No
OrcaSheets
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
The Apache Software Foundation
Founded
1999
Country
United States
Website
parquet.apache.org
Vendor Details
Company Name
Scheme
Website
www.scheme.com/tspl4/intro.html#./intro:h0