3X Code Conversion3X Data Engineering
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Related Products
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About
3X Code Conversion is an AI-augmented data engineering accelerator that converts legacy SQL and ETL code into modern cloud platform code. The tool supports migrations from Teradata, Oracle, Netezza, SQL Server, MySQL, PostgreSQL, and Redshift to Snowflake, Databricks, BigQuery, Microsoft Fabric, Synapse, and other modern data platforms. It can ingest SQL scripts, stored procedures, ETL jobs, BTEQ macros, FastLoad jobs, SSIS pipelines, and undocumented legacy codebases. 3X Code Conversion uses deep code understanding, complexity scoring, agentic conversion, automated refactoring, testing, validation, and developer action items. The platform generates converted code, validation reports, standardized formatting, migration summaries, documentation, and AI-assisted code reviews. Built for enterprise cloud migrations, 3X Code Conversion helps data teams move legacy code to modern platforms in hours instead of months.
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About
100% compatible with Netezza. Single command-line upgrade path. Available on premises, on cloud or hybrid. IBM® Netezza® Performance Server for IBM Cloud Pak® for Data is an advanced data warehouse and analytics platform available both on premises and on cloud. With enhancements to in-database analytics capabilities, this next generation of Netezza enables you to do data science and machine learning with data volumes scaling into the petabytes. Failure detection and fast failure recovery. Single command-line upgrade to existing systems. Ability to query many systems as one. Choose the data center or availability zone closest to you, set the number of compute units and amount of storage required to run, and go. IBM® Netezza® Performance Server for IBM Cloud Pak® for Data is available on IBM Cloud®, Amazon Web Services (AWS) and Microsoft Azure. Deployable on a private cloud, Netezza is powered by IBM Cloud Pak for Data System.
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Data Engineers, Software Developers, DevOps Engineers, Solution Architects, Cloud Migration Engineers, Enterprise IT Teams, Technical Consultants
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Audience
Companies and analysts that require a data warehouse and analytics platform to do data science and machine learning with data volumes
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Support
Phone Support
Not Supported
24/7 Live Support
Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Not Supported
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API
Offers API
Not Supported
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Screenshots and VideosNo images available
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Screenshots and Videos |
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Supported
Live Online
Supported
In Person
Supported
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Company Information3X Data Engineering
Founded: 2023
United State
www.3xdataengineering.com
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Company InformationIBM
Founded: 1911
United States
www.ibm.com/products/netezza
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Categories |
Categories |
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Integrations
Advanced Query Tool (AQT)
Not Supported
Amazon Web Services (AWS)
Not Supported
Arcion
Not Supported
Captain Compliance
Not Supported
Cloudera Data Platform
Not Supported
DBHawk
Not Supported
Datametica
Not Supported
DbVisualizer
Not Supported
IBM Cloud
Not Supported
IBM Cloud Pak for Data
Not Supported
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Integrations
Advanced Query Tool (AQT)
Supported
Amazon Web Services (AWS)
Supported
Arcion
Supported
Captain Compliance
Supported
Cloudera Data Platform
Supported
DBHawk
Supported
Datametica
Supported
DbVisualizer
Supported
IBM Cloud
Supported
IBM Cloud Pak for Data
Supported
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