RAG and Fine-Tuning are two prominent LLM customization approaches. While RAG involves providing external and dynamic resources to trained models, fine-tuning involves further training on specialized datasets, altering the model. Each approach can be used for different use cases. In this blog post, we explain each approach, compare the two and recommend when to use them and which pitfalls to avoid.

An effective dashboard requires careful design to present data in the best way, and to help more people (users, customers) find insights without feeling overwhelmed. Yellowfin BI comes with a wide variety of chart types as part of its extensive data visualization tools, and while it is tempting to use a lot of eye-catching charts to make a dashboard that looks great, it is important to select the right chart for the right situation.

Financial and operational reporting for NetSuite can be a challenge. As is the case with many ERP systems, NetSuite’s reporting capabilities tend to be somewhat restrictive. It can be difficult to pull information from multiple NetSuite modules into a single, cohesive report. In other instances, information for which there ought to be a fairly straightforward reporting process turns out to be inaccessible.

Data has become the lifeblood of businesses across all industries. With the exponential growth of data collection and processing, the importance of data compliance has skyrocketed. And navigating this complex business landscape is vital for any organization handling sensitive information. 5 key takeaways from this post on mastering data compliance are.

Data fabric is an architecture that integrates different data systems and tools. It provides unified access to data stored across various locations to organize, manage, and govern it without moving it to a central database or data warehouse or changing its format. Data fabric relies on metadata to ‘understand’ the data’s structure, lineage, and meaning across various sources. This information enables informed decision-making and optimized data usage.

Started in 2009 as a research project at UC Berkeley, Apache Spark transformed how data scientists and engineers work with large data sets, empowering countless organizations to accelerate time-to-value for their analytics activities. Apache Spark is now the most popular engine for distributed data processing at scale, with thousands of companies (including 80% of the Fortune 500) using Spark to support their big data analytics initiatives.

Tips for writing natural language prompts to generate SQL and charts using BigQuery data canvas’ NL2SQL and NL2Chart capabilities.

Learn about BigQuery data canvas in this technical walkthrough with a real-world example based on the public github_repos dataset.