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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
API Access
Has API
Integrations
Amazon SageMaker Data Wrangler
Arroyo
Blotout
CSViewer
CodeConvert
Flyte
Hadoop
IBM Db2 Event Store
Indexima Data Hub
Mage Sensitive Data Discovery
Integrations
Amazon SageMaker Data Wrangler
Arroyo
Blotout
CSViewer
CodeConvert
Flyte
Hadoop
IBM Db2 Event Store
Indexima Data Hub
Mage Sensitive Data Discovery
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
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