Our Story
Leaders in Open-Source Analytics
Our Founding
Founded in 2015, by renowned Data Scientist Ian J. Ghent, WiseWithData’s mission is to Ignite Open Analytics. Ian, with over 25 years of industry experience, had based his career on using the leading enterprise analytics language at the time, SAS. Before founding WiseWithData, Ian worked at SAS, leading the global roll-out of cutting edge industry solutions he invented.
In discussions with global customers, it was apparent that many were no longer satisfied with their decades old proprietary technology platforms. They saw peers leveraging open data platforms with astonishing results. Increasingly, they yearned for more freedom, performance and functionality. Having been a fan of open source software since the 1990’s, Ian saw a tsunami of change was upon the whole industry, not unlike what had happened with Linux. The shift to Linux sparked entirely new technologies and industries like virtualization, containers and Cloud computing. That shift for analytics would forever democratize the field, to the benefit of all humanity.
Ian and his colleagues saw the awesome potential behind one open source technology in particular, Apache Spark and PySpark, its Python interface. With data processing performance 100X+ that of anything else in the market, along with simple powerful API’s, they knew PySpark was the future for collaborating and innovating with data, big or small. Being experts in legacy analytics technology, WiseWithData set off on an ambitions journey to become world leading experts in PySpark.
8 years later, WiseWithData is recognized by its customers and partners as masters of PySpark. Meanwhile, Spark has become the undisputed de-facto analytics platform, with support from all leading technology players (Databricks, Amazon, Microsoft, Google, etc.).
Our Mission
WiseWithData’s mission is simple, help companies embrace Apache Spark, and support them through the transition from legacy analytics. To that end, they developed the SPROCKET SAS to PySpark migration solution. SPROCKET is the World’s only proven solution to convert SAS code to native PySpark DataFrame code with over 99% automation. This unique offering is the key for customers to succeed in modernizing their analytics technology.
We Are The Experts in SAS, Databricks & PySpark
WiseWithData consults and advises clients on world-leading open-source options to achieve efficient digital transformation.
We innovate with open source technologies everyday helping clients get the most out of their data.
Our versatile team of data scientists work with these values top in mind:
- Strive for creative excellence, and deliver automated solutions that are simple & easy to deploy
- Deliver & show value early, then scale to complete and achieve objectives
- Act with respect, integrity, and commitment while maintaining a focus on environmental sustainability
A Rich History
Of Innovation & Automation
WiseWithData Established By Leading SAS Experts – Its mission, to Ignite Open Analytics & help customers transition to the leading Python PySpark platform
2015
2016
SPROCKET v1.0 – The World’s First and Only SAS To Python PySpark Migration Solution goes live
First large scale migration using SPROCKET Automation a success
2017
2018
WiseWithData Focuses Strategic Direction On Market Leading SPROCKET Migration Solution
WiseWithData Releases SPROCKET Validator, making the UAT process quick and easy
First Government & Financial Institution Migrations Successfully Completed
SPROCKET Searchparty is Released, bringing automation to migration project scoping
2019
2020
SPROCKET Runtime 3.0 Released bringing support for Delta and Databricks
Migrated over 500,000 lines of SAS code for TransUnion, in mere months, saving millions in lost productivity and licensing fees
WiseWithData releases RIPL API as part of SPROCKET Runtime 3.1, bringing more SAS capabilities into Pyspark
WiseWithData and Databricks announce strategic partnership to modernize legacy SAS workloads
2021
2022
Fireball synthetic data generation tool goes live, eliminating need for testing data, and streamlining migration process
SPROCKET VDesk solution goes live, enabling secure migrations without code ever leaving customer premise
TransUnion asks WiseWithData to participate in sharing the story of their successful SAS and cloud modernization initiatives in Interface magazine
SPROCKET SearchParty incorporates Automated Schema Harvesting (ASH) technology to fully automated test data generation workflow
2023
A Rich History
Of Innovation & Automation
2015
WiseWithData Established By Leading SAS Experts
Its mission, to Ignite Open Analytics & help customers transition to the leading Python PySpark platform
2016
SPROCKET v1.0 – The World’s First and Only SAS To Python PySpark Migration Solution goes live
2017
First large scale migration using SPROCKET Automation a success
2018
WiseWithData Focuses Strategic Direction On Market Leading SPROCKET Migration Solution
WiseWithData Releases SPROCKET Validator, making the UAT process quick and easy
2019
First Government & Financial Institution Migrations Successfully Completed
SPROCKET Searchparty is Released, bringing automation to migration project scoping
2020
SPROCKET Runtime 3.0 Released bringing support for Delta and Databricks
Migrated over 500,000 lines of SAS code for TransUnion, in mere months, saving millions in lost productivity and licensing fees
2021
WiseWithData releases RIPL API as part of SPROCKET Runtime 3.1, bringing more SAS capabilities into Pyspark
WiseWithData and Databricks announce strategic partnership to modernize legacy SAS workloads
2022
Fireball synthetic data generation tool goes live, eliminating need for testing data, and streamlining migration process
SPROCKET VDesk solution goes live, enabling secure migrations without code ever leaving customer premise
2023
TransUnion asks WiseWithData to participate in sharing the story of their successful SAS and cloud modernization initiatives in Interface magazine
SPROCKET SearchParty incorporates Automated Schema Harvesting (ASH) technology to fully automated test data generation workflow