BIG DATA MANAGEMENT

Integress Acquired by AllCloud to Boost Data Analytics for Cloud Services

AllCloud | July 19, 2021

AllCloud, a professional cloud services provider, acquired Integress, a data analytics company that provides business intelligence to customers in industries from financial services and insurance to transportation. AllCloud aims to bring better data integration and data visualization to cloud-oriented applications.

AllCloud said in their press release that this acquisition would help them expand its Amazon Web Services (AWS) -and Salesforce-centric data and analytics capabilities in North America. AllCloud’s Europe, Middle East, and Africa (EMEA) locations already have separate data analytics services.

Integress founder and CEO Dave Taddei will join AllCloud as senior VP of data and analytics practice for North America. The terms of the deal were not disclosed by AllCloud.

Cloud alliances key in data visualization

Doug Shepard, AllCloud North America president, said Integress was an innovation leader with “deep expertise” in Snowflake’s cloud-based data warehousing platform, which in recent months has added operational and security capabilities, as well as machine learning-based data integration.

He also mentioned Integress’ proficiency in data visualization with Tableau and Sigma and a partnership with AWS, Fivetran, and Matillion as well. Integress has proven expertise in areas like accelerated analytics strategy, machine learning and artificial intelligence, cloud analytics quick start, cloud data pipeline and integrations, and cloud data warehousing.

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MOSTLY AI Opens up Its Synthetic Data Platform to Revolutionize Software Testing for Mid-market Companies

MOSTLY AI | July 06, 2022

MOSTLY AI, who pioneered the creation of AI-generated synthetic data, has today launched new editions of its platform, for mid-market businesses wanting to speed up test data generation through automation, and better support agile processes. By experimenting with the free edition of the platform, test engineers, QA leads, and test automation experts can see for themselves how the pioneering platform easily and automatically synthesizes complex data structures. Boosting efficiency is coupled with the benefit of generating high quality test data for QA - a critical need for businesses that are required to deliver customer experiences that are increasingly personal and relevant. “Scaled synthetic datasets generated through our platform offer absolute protection of customer data, with zero risk of re-identification and therefore full compliance with data privacy laws such as GDPR. What’s more is that the datasets preserve granular behavioral insights embedded in the production data “This is of course valuable for innovative companies focused on accelerating the agile delivery of robust software applications that enhance customer experience.” Dr. Tobias Hann, CEO at MOSTLY AI For tests, such as load and performance testing, MOSTLY AI’s platform completely removes the need to use production data or manually created dummy data, which is what the majority of testers are still using now. Apart from clear privacy issues that come with doing it this way, it’s a massive time thief with a huge chunk of the average tester’s time being spent waiting for test data, looking for it, or creating it manually. “Our research over the past months confirms this risky habit of testers using production or dummy data,” says Hann, adding, “and coupled with the fact that 20% of test data will be synthetically generated by 2025, it’s the right time for us to bring AI-generated synthetic data to the mid-market and be instrumental in reaching the synthetic-data tipping point we know is on the horizon.” AI-generated synthetic data is not mock data or fake data. It’s not generated manually - as it was ten years ago - but by a powerful AI engine that is capable of learning all the qualities of the dataset on which it is trained. Using the MOSTLY AI platform, testers don’t need to manually configure business rules anymore, plus it enables them to create as little data or as much data as they need - generating small, manageable and referentially intact subsets of data to speed up cycles and reduce storage sizes or upscale small datasets to massive sizes for stress testing applications. “Mid-market companies have an advantage over larger corporations - they can adopt and roll out new tech quickly without the red tape that often makes this a drawn-out process. Adopting AI-generated synthetic data for testing is a win-win situation – for testers who get to work smarter and faster, and for businesses wanting to innovate and deliver the best in customer experience,” concludes Hann.

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Snowplow | July 04, 2022

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