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What is Daft?

Daft is a sophisticated framework tailored for ETL, analytics, and large-scale machine learning/artificial intelligence, featuring a user-friendly Python dataframe API that outperforms Spark in both speed and usability. It provides seamless integration with existing ML/AI systems through efficient zero-copy connections to critical Python libraries such as Pytorch and Ray, allowing for effective GPU allocation during model execution. Operating on a nimble multithreaded backend, Daft initially functions locally but can effortlessly shift to an out-of-core setup on a distributed cluster once the limitations of your local machine are reached. Furthermore, Daft enhances its functionality by supporting User-Defined Functions (UDFs) in columns, which facilitates the execution of complex expressions and operations on Python objects, offering the necessary flexibility for sophisticated ML/AI applications. Its robust scalability and adaptability solidify Daft as an indispensable tool for data processing and analytical tasks across diverse environments, making it a favorable choice for developers and data scientists alike.

What is Bravo for Power BI?

Utilize Bravo to effectively evaluate your model's memory consumption and determine which columns can be removed for better optimization. In addition, Bravo's capability to export metadata to VPAX files significantly improves data management efficiency. Ensure your DAX code remains clear and well-organized by taking advantage of Bravo's tools, which make it easy to spot measures that need formatting and simplify the workflow through the DAX Formatter service. Bravo also aids in creating a Date table for your model, providing numerous calendar templates, language options, and holiday selections that cater to different countries. It not only allows for the addition of DAX measures for commonly used time intelligence calculations but also features customizable date templates along with a specialized template editor in Visual Studio Code. This enables companies to effortlessly implement standardization of their calendars across all departments through group policies, ultimately enhancing time management for every user and fostering greater operational cohesion. Furthermore, by ensuring consistent calendar formats, organizations can streamline their scheduling processes and improve overall productivity.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Apache Arrow
Apache Iceberg
Apache Spark
Databricks
Delta Lake
Google Cloud Platform
JSON
Microsoft Azure
Microsoft Power BI
PyTorch
Python
Rust
Spark.work
Unity Catalog
pandas

Integrations Supported

Amazon Web Services (AWS)
Apache Arrow
Apache Iceberg
Apache Spark
Databricks
Delta Lake
Google Cloud Platform
JSON
Microsoft Azure
Microsoft Power BI
PyTorch
Python
Rust
Spark.work
Unity Catalog
pandas

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
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

Daft

Company Location

United States

Company Website

www.getdaft.io

Company Facts

Organization Name

SQLBI Corp

Company Location

United States

Company Website

bravo.bi/

Categories and Features

Data Science

Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports

Categories and Features

Data Visualization

Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery

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