Systems | Development | Analytics | API | Testing

Two years after Seagate first shared their AI and MLOps success story, the data storage leader is now revealing how far they've come since then. In this blog post, you’ll see how the team manages thousands of AI models in production with only a few team members. This is thanks to their AI factory, whichdoes the heavy lifting of automated processes like monitoring, testing, mocking and more.

Choosing the right storage format is crucial for optimizing performance, cost, and flexibility when working with cloud data. While file formats like Apache Parquet and Avro have been popular choices for storing data in data lakes, in recent years a new category called table formats has emerged to provide more management capabilities on top of these files. Among these, Apache Iceberg has been gaining significant adoption and momentum. So what exactly is Iceberg and why does it matter? Let’s dive in.

Not long ago, I wrote about a growing problem in enterprise AI: agents that don’t talk to each other. You’ve got a customer relationship management (CRM) agent doing its thing, a data warehouse agent crunching numbers, a knowledge bot quietly surfacing documents—but none of them are sharing what they know. Instead of a smart, connected ecosystem, we’re stuck with isolated pockets of intelligence: an island of agents.

A recent study by PwC suggests that AI could contribute up to $15.7 trillion to the global economy by 2030, with automation playing a key role in boosting efficiency and innovation. AI agents are central to this transformation, streamlining workflows, handling repetitive tasks, and enabling data-driven decision-making. From virtual assistants in customer service to intelligent fraud detection in finance, these agents are reshaping industries and driving business growth.

In the realm of cloud data warehousing, Snowflake stands out for its scalability and performance. A pivotal feature contributing to its efficiency is data clustering. This guide delves into the intricacies of Snowflake's data clustering, offering insights and best practices for clustered tables to harness its full potential.

Build an interoperable data architecture for analytics and AI using Fivetran Managed Data Lake Service and Google’s Cloud Storage.

In the fast-paced world of digital marketing, having the right tools to track and analyze data can make the difference between a successful campaign and one that falls flat. With an increasing number of touchpoints and channels, marketers are under constant pressure to collect meaningful insights that drive decision-making.

In the era of real-time analytics, traditional batch ETL processes often fall short of delivering timely insights. Apache Kafka has emerged as a game-changer, enabling organizations to build robust, scalable, and real-time ETL pipelines. This article delves into how Kafka for ETL facilitates modern integration processes, its core components, best practices, and real-world applications.