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.

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.

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

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.

Yellowfin 9.15 is a significant version release that introduces a major update to our Yellowfin Guided NLQ feature in the form of AI-enabled Natural Language Query (AI NLQ), as well as a host of general product enhancements, fixes and security updates. In this blog, we will cover what AI NLQ brings to your embedded analytics deployment, as well some of the other highlights arriving in the Yellowfin 9.15 version release. For the full technical list of updates, please visit our release notes page.

The rise of natural language query (NLQ) technology in modern business intelligence (BI) and analytics platforms is empowering many companies to streamline data exploration and analysis, and democratize access to insights for more people - not just data experts. But like any technology, the ongoing challenge is to help stakeholders and customers to see the value in using it.

Adopting a data lake in combination with the Fivetran Managed Data Lake Service reduces data ingest compute costs by 77% to 95% over traditional data warehouses.

AI builders deploying models into production focus on ensuring well-performing models are available for users. Once the model is live, the focus shifts to optimizing GPU usage for efficient deployment. While GPU machines offer the best performance, they are costly to run and frequently remain underutilized.