ClickHouse and Superset are utilized to supercharge website analytics by allowing for fast and flexible querying of raw data from Google Analytics at a minimal cost. A natural language interface is explored using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques, which can simplify the application interface for less technical users. The goal is to enable users to ask questions in natural language and receive accurate SQL responses that ultimately answer their question with underlying data. The LLM model used is Amazon's `titan-embed-text-v1`, while the RAG technique involves combining the power of pre-trained language models with information retrieval systems. The system aims to provide a more accessible means of exploring Google Analytics data, particularly for users who may not be familiar with SQL or technical aspects of analytics. By utilizing a natural language interface and providing context through examples and schema, the system can help users refine their questions and generate accurate queries. However, challenges remain in terms of prompt engineering and ensuring that the generated SQL is accurate and relevant to the user's query. The system has shown promising results but requires further refinement to improve accuracy and performance, particularly for more complex join queries or sub-filtering. To address these challenges, exploring lighter-weight models, refining existing models, and developing a test harness with diverse example problems are necessary next steps.