CIO
NOVEMBER 19, 2024
A cloud analytics migration project is a heavy lift for enterprises that dive in without adequate preparation. A modern data and artificial intelligence (AI) platform running on scalable processors can handle diverse analytics workloads and speed data retrieval, delivering deeper insights to empower strategic decision-making.
CIO
NOVEMBER 8, 2024
At the same time, many organizations have been pushing to adopt cloud-based approaches to their IT infrastructure, opting to tap into the speed, flexibility, and analytical power that comes along with it. It’s a decision that maps back to the overarching goals of a business and how they want to leverage their data.
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CIO
NOVEMBER 14, 2024
Migration to the cloud, data valorization, and development of e-commerce are areas where rubber sole manufacturer Vibram has transformed its business as it opens up to new markets. Much of this growth is driven by investments in AI technologies, and IDC also expects cloud infrastructure spend to increase 26% compared to 2023.
Xebia
APRIL 17, 2025
Google Cloud Next 2025 was a showcase of groundbreaking AI advancements. bigframes.pandas provides a pandas-compatible API for analytics, and bigframes.ml The post The AI Future According to Google Cloud Next ’25: My Interesting Finds appeared first on Xebia. BigFrames 2.0 offers a scikit-learn-like API for ML.
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Data architectures to support reporting, business intelligence, and analytics have evolved dramatically over the past 10 years.
CIO
MAY 22, 2025
Knowing this,the Magic has signed a strategic agreement with multinational analytics and AI software developer SAS with the aim to maximize impact during games at the Kia Center in downtown Orlando. Sellout games have historically served as motivation for the team to exceed expectations for home crowds.
CIO
MAY 22, 2025
Here are a few things to look for in a security analytics platform designed to scale with your team: Designed for security analysts Modern AI-powered platforms help analysts move faster not start over. Its about building on what you already have data, processes, and people with smart tools that enhance productivity and reduce complexity.
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To do so, modern enterprises leverage cloud data lakes as the platform used to store data for analytical purposes, combined with various compute engines for processing that data. Businesses today compete on their ability to turn big data into essential business insights.
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Enterprises are pouring money into data management software – to the tune of $73 billion in 2020 – but are seeing very little return on their data investments.
Speaker: Jeremiah Morrow, Nicolò Bidotti, and Achille Barbieri
Data teams in large enterprise organizations are facing greater demand for data to satisfy a wide range of analytic use cases. Yet they are continually challenged with providing access to all of their data across business units, regions, and cloud environments. Leveraging Dremio for data governance and multi-cloud with Arrow Flight.
Speaker: Javier Ramírez, Senior AWS Developer Advocate, AWS
You have lots of data, and you are probably thinking of using the cloud to analyze it. But how will you move data into the cloud? In this session, we address common pitfalls of building data lakes and show how AWS can help you manage data and analytics more efficiently. In which format? What about streaming data?
Speaker: Ahmad Jubran, Cloud Product Innovation Consultant
In order to maintain a competitive advantage, CTOs and product managers are shifting their products to the cloud. Many do this by simply replicating their current architectures in the cloud. Join Ahmad Jubran, Cloud Product Innovation Consultant, and learn how to adapt your solutions for cloud models the right way.
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Increasingly, enterprises are leveraging cloud data lakes as the platform used to store data for analytics, combined with various compute engines for processing that data. Read this paper to learn about: The value of cloud data lakes as the new system of record.
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Embedding analytics in your application doesn’t have to be a one-step undertaking. Read more about how to simplify the deployment and scalability of your embedded analytics, along with important considerations for your: Environment Architecture: An embedded analytics architecture is very similar to a typical web architecture.
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How to enable data teams to model and deliver a semantic layer on data in the cloud. How a semantic layer delivers massive ROI with streamlined query performance, concurrency, cost management, and ease of use. How you can reach optimal performance on large datasets while improving query performance and user concurrency by 10x.
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