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
Effortlessly convert your comments into functional code, as demonstrated in the example where we swiftly generate a Plotly bar chart. You can seamlessly construct complete functions without the need to search for specific library methods or parameters; for instance, we developed a function to upload a DataFrame to an AWS bucket in parquet format. Additionally, you can write SQL queries simply by instructing CodeSquire on the data you wish to extract, join, and organize, similar to the example where we identify the top 10 most prevalent names. CodeSquire is also capable of elucidating someone else's code; just request an explanation of the preceding function, and you'll receive a clear, straightforward description. Furthermore, it can assist in crafting intricate functions that incorporate multiple logical steps, allowing you to brainstorm ideas by starting with basic concepts and progressively integrating more advanced features as you refine your project. This collaborative approach makes coding not only easier but also more intuitive.
API Access
Has API
No
API Access
Has API
No
Integrations
APERIO DataWise
Yes
Amazon SageMaker Data Wrangler
Yes
Astera Dataprep
Yes
Data Sentinel
Yes
Ficstar
Yes
Flyte
Yes
Google Chrome
No
IBM Db2 Event Store
Yes
Indexima Data Hub
Yes
JupyterLab
No
Integrations
APERIO DataWise
No
Amazon SageMaker Data Wrangler
No
Astera Dataprep
No
Data Sentinel
No
Ficstar
No
Flyte
No
Google Chrome
Yes
IBM Db2 Event Store
No
Indexima Data Hub
No
JupyterLab
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
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
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
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
No
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
CodeSquire
Country
India
Website
codesquire.ai/