AWS Big Data

JULY 20, 2023

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. These robust capabilities ensure that data within the data lake remains accurate, consistent, and reliable.

Data Lake 98

Data Lake Analytics Snapshot Data Quality 98

AWS Big Data

OCTOBER 19, 2023

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure. Apache Hudi 0.13.0 Delta Lake 2.0.0

Data Lake 137

Data Lake Data Warehouse Visualization Snapshot 137

CIO Business Intelligence

AUGUST 21, 2024

But multiagent AI systems are still in the experimental stages, or used in very limited ways. There are already gen AI platforms that can handle images, audio, and even video. There’s a lot of information that you can’t just describe in text,” he says. One internal use case of agentic AI at Salesforce is for software development. “We

Risk 136

Risk Insurance Cost-Benefit Software 136

AWS Big Data

SEPTEMBER 13, 2024

Suvojit Dasgupta is a Principal Data Architect at Amazon Web Services. He leads a team of skilled engineers in designing and building scalable data solutions for AWS customers. He specializes in developing and implementing innovative data architectures to address complex business challenges.

Management 111

Management Snapshot Cost-Benefit Testing 111

IBM Big Data Hub

JANUARY 17, 2023

You can select a hybrid integration strategy that aligns with your organization’s business strategy to meet the needs of your data consumers wanting to access and utilize the data. Data science and MLOps. AI is no longer experimental. Start a trial.

Data-driven 56

Data-driven Enterprise Data Governance Data Science 56

CIO Business Intelligence

JUNE 16, 2022

A real-time data technology stack has to shrink this innovation gap for the business. . Analysts and data scientists need flexibility when working with data; experimentation fuels the development of analytics and machine learning models. Often, enterprise data ecosystems are built with a mindset that’s too narrow.

IT 135

IT Experimentation IoT Data-driven 135

O'Reilly on Data

SEPTEMBER 11, 2019

The question is which one: the topics and themes of this year’s proposals suggest that developers, data engineers, and other technologists are scrambling to acquire new domain-specific knowledge and skills. New trends in data architecture and data services. With respect to security, for example, several factors—e.g.,

IoT 25

IoT Big Data Data Warehouse Uncertainty 25

CIO Business Intelligence

APRIL 14, 2025

Fabien Cros, chief data and AI officer at global consulting firm Ducker Carlisle who also advises clients through the firms SparkWise Solutions, has observed other organizations pushing off transformation efforts in favor of AI experimentation. Many companies are trying to leapfrog, and theres no way they can leapfrog.

IT 125

IT ROI Consulting Experimentation 125

CIO Business Intelligence

APRIL 16, 2025

Innovator/experimenter: enterprise architects look for new innovative opportunities to bring into the business and know how to frame and execute experiments to maximize the learnings. Infrastructure architecture: Building the foundational layers of hardware, networking and cloud resources that support the entire technology ecosystem.

Enterprise 124

Enterprise Strategy Risk Business Objectives 124

Timo Elliott

FEBRUARY 7, 2025

However, many EA teams are perceived by executives to lack the necessary expertise in AI technologies, data architectures, and ethical considerations to effectively guide AI-driven initiatives. Building AI-ready data foundations ensuring organizations have the right data and infrastructure for AI deployment.

Enterprise 52

Enterprise IT Digital Transformation Cost-Benefit 52

AWS Big Data

MAY 1, 2025

The Clinical Insights Data Science team runs critical end-of-day batch processes that need guaranteed resources, whereas the Digital Analytics team can use cost-optimized spot instances for their variable workloads. Additionally, data scientists from both teams require environments for experimentation and prototyping as needed.

Cost-Benefit 61

Cost-Benefit Interactive Management Data Processing 61