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What is SDTM-ETL?

A groundbreaking software solution is now available, delivering the best cost-benefit ratio for the creation of SDTM/SEND datasets and define.xml! Known as SDTM-ETL, this software is celebrated as the most economical and valuable tool in the market for generating SDTM and SEND datasets. It only necessitates that your EDC system can export clinical data in the widely accepted CDISC ODM format, which is typically supported by most EDC systems. Remarkably, SDTM-ETL operates independently of SAS®, which means you will not need to bear the expense of a SAS® license or any other statistical software. The software boasts a highly user-friendly graphical interface that enables users to create most mappings effortlessly through straightforward drag-and-drop functionality or mouse clicks. Furthermore, the define.xml files (versions 2.0 or 2.1) are produced automatically, and users can easily input details using intelligent wizards, thereby removing the hassle of tedious XML editing and complicated Excel spreadsheets. Many CROs and service providers have already adopted SDTM-ETL for their regulatory submission processes, making it a favored option in the industry. This innovative tool is not only enhancing clinical data management but is also simplifying the submission journey for numerous organizations. As a result, it stands poised to revolutionize the industry standards and practices in clinical data handling.

What is Dagster?

Dagster serves as a cloud-native open-source orchestrator that streamlines the entire development lifecycle by offering integrated lineage and observability features, a declarative programming model, and exceptional testability. This platform has become the preferred option for data teams tasked with the creation, deployment, and monitoring of data assets. Utilizing Dagster allows users to concentrate on executing tasks while also pinpointing essential assets to develop through a declarative methodology. By adopting CI/CD best practices from the outset, teams can construct reusable components, identify data quality problems, and detect bugs in the early stages of development, ultimately enhancing the efficiency and reliability of their workflows. Consequently, Dagster empowers teams to maintain a high standard of quality and adaptability throughout the data lifecycle.

Media

Media

Integrations Supported

Airbyte
Amazon Web Services (AWS)
Apache Airflow
Apache Spark
Coginiti
Collate
Dask
DataHub
Fivetran
Google Cloud Platform
Jupyter Notebook
Kubernetes
MLflow
Microsoft Excel
Microsoft Teams
MySQL
OpenMetadata
PostgreSQL
Twilio
pandas

Integrations Supported

Airbyte
Amazon Web Services (AWS)
Apache Airflow
Apache Spark
Coginiti
Collate
Dask
DataHub
Fivetran
Google Cloud Platform
Jupyter Notebook
Kubernetes
MLflow
Microsoft Excel
Microsoft Teams
MySQL
OpenMetadata
PostgreSQL
Twilio
pandas

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$0
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

XML4Pharma

Company Location

Austria

Company Website

www.xml4pharma.com/SDTM-ETL/

Company Facts

Organization Name

Dagster Labs

Date Founded

2019

Company Location

United States

Company Website

dagster.io

Categories and Features

ETL

Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control

Categories and Features

Data Fabric

Data Access Management
Data Analytics
Data Collaboration
Data Lineage Tools
Data Networking / Connecting
Metadata Functionality
No Data Redundancy
Persistent Data Management

Data Management

Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge

ETL

Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

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