According to recent research, scaling intelligent automation could save the Energy & Utilities industry between $237 billion and $813 billion in operational costs. Despite this immense potential, utility providers still grapple with intense competition, increasingly stringent regulations, and aging infrastructure and workforce challenges. The demand for more affordable and cleaner renewable energy only adds to these pressures.

The Director of Product Management at AWS sat down with Fivetran to unpack how Iceberg and metadata are reshaping the future of data lakes, data warehouses and AI.

Epicor’s ability to provide industry-focused, scalable, and customizable ERP solutions has made it a popular choice for organizations across the globe. Epicor’s built-in reporting capabilities are useful for standard reports but can be limiting for organizations that require more advanced analytics. Without deep technical knowledge of Epicor’s data structures, attempting to manually create custom reports can create serious roadblocks to data trust within your organization.

AI Adoption in SMBs: Key Trends, Benefits, and Challenges from 100+ Companies With larger competitors already using AI to streamline operations and gain a competitive edge, SMBs can’t afford to fall behind. But for many, adopting AI is easier said than done. Limited budgets, lack of in-house expertise, and the fear of wasting time and resources on the wrong tools often leave business owners stuck in decision paralysis.

The headless data architecture is an organic emergence of the separation of data storage, management, optimization, and access from the services that write, process, and query it. With this architecture, you can manage your data from a single logical location, including permissions, schema evolution, and table optimizations. And, to top it off, it makes regulatory compliance a lot simpler, because your data resides in one place, instead of being copied around to every processing engine that needs it.

Predictive machine learning continues to be a cornerstone of data-driven decision-making. However, as organizations accumulate more data in a wide variety of forms, and as modeling techniques continue to advance, the tasks of a data scientist and ML engineer are becoming increasingly complex. Oftentimes, more effort is spent on managing infrastructure, jumping through package management hurdles, and dealing with scalability issues than on actual model development.

Snowflake customers can take advantage of Fivetran’s data movement and data replication capabilities as a native application available in the Snowflake Marketplace.

From ancient Greek myths of talking statues to the modern-day Alexa and Siri, the concept of machines capable of understanding and responding to human language has captivated us for centuries. In recent years, this concept has evolved into AI chatbots, highly sophisticated tools that can read our queries and perform tasks ranging from customer service to automated alerts.

For the last couple of years, Retrieval-Augmented Generation (RAG) architectures have become a rising trend for AI-based applications. Generally speaking, RAG offers a solution to some of the limitations in traditional generative AI models, such as accuracy and hallucinations, allowing companies to create more contextually relevant AI applications.