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What is commercebuild?

Transform your Sage ERP into a comprehensive eCommerce platform in just 60 days with commercebuild. This solution not only integrates smoothly with your existing system but also accurately reflects the complex business logic already present in your Sage software. Backed by Sage certification, commercebuild enables you to deliver an all-encompassing digital experience for your customers, significantly improving your return on investment. Customized eCommerce solutions facilitate a smooth transition of your Sage-managed business into the digital landscape. What sets commercebuild apart is its unique ability to intelligently capture the intricate business rules you’ve developed within your Sage ERP, ensuring a streamlined and effective online presence. This innovative methodology guarantees that your established business logic is preserved as you venture into the world of eCommerce, allowing for continued growth and success in the online marketplace. By embracing this approach, you can effectively meet the evolving demands of your customers while maintaining operational consistency.

What is Amazon SageMaker Data Wrangler?

Amazon SageMaker Data Wrangler dramatically reduces the time necessary for data collection and preparation for machine learning, transforming a multi-week process into mere minutes. By employing SageMaker Data Wrangler, users can simplify the data preparation and feature engineering stages, efficiently managing every component of the workflow—ranging from selecting, cleaning, exploring, visualizing, to processing large datasets—all within a cohesive visual interface. With the ability to query desired data from a wide variety of sources using SQL, rapid data importation becomes possible. After this, the Data Quality and Insights report can be utilized to automatically evaluate the integrity of your data, identifying any anomalies like duplicate entries and potential target leakage problems. Additionally, SageMaker Data Wrangler provides over 300 pre-built data transformations, facilitating swift modifications without requiring any coding skills. Upon completion of data preparation, users can scale their workflows to manage entire datasets through SageMaker's data processing capabilities, which ultimately supports the training, tuning, and deployment of machine learning models. This all-encompassing tool not only boosts productivity but also enables users to concentrate on effectively constructing and enhancing their models. As a result, the overall machine learning workflow becomes smoother and more efficient, paving the way for better outcomes in data-driven projects.

Media

Media

Integrations Supported

Sage 300
Sage X3

Integrations Supported

Amazon Athena
Amazon EMR
Amazon Redshift
Amazon S3
Amazon SageMaker
Amazon SageMaker Feature Store
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Apache Parquet
Databricks
Google Analytics
JSON
Meta Ads
PySpark
SAP Cloud Platform
Salesforce
Snowflake
pandas

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS
iPhone
iPad

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars

Company Facts

Organization Name

commercebuild

Date Founded

2002

Company Location

Canada

Company Website

commercebuild.com

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/sagemaker/data-wrangler/

Categories and Features

eCommerce

CRM
Catalog Management
Channel Management
Customer Accounts
Data Security
Inventory Management
Kitting
Mobile Access
Multi-Store Management
Order Management
Product Configurator
Promotions Management
Reviews Management
SEO Management
Shopping Cart
Templates

Categories and Features

Data Preparation

Not specified

Machine Learning

Not specified

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