In The fundamentals of data warehouse architecture, we covered the standard layers and shared components of a well-formed data warehouse architecture. In this second part, we’ll cover the core components of the multi-tiered architectures for your data warehouse.
When using data to make impactful business decisions, certain doubts may start to arise, like “What does this column exactly mean?” or “Can I trust this data source I want to use?” Questions like these speak to a larger need for increased data literacy and trust in data. ThoughtSpot continually invests in this area, giving users the confidence to build the correct Answers needed for their analysis—and ensuring they can trust the data they are shown.
We’ve talked about the many ways large language models (LLMs) and artificial intelligence (AI) are impacting business efficiency, data and analytics, and even FinOps. But we’ve yet to talk about arguably one of the most important areas of concern: security.
By the end of this two-part series, we will dive into what data warehouse architecture is and how to implement one for your organization. Part one will look at architectural layers and common data warehouse components, while part two dives into multi-tiered data warehouse architecture.
We’re thrilled to announce that both ThoughtSpot and Mode (acquired by ThoughtSpot in July 2023) have been recognized as Leaders in Snowflake's recent Modern Marketing Data Stack report! Given the ever-evolving landscape of modern data analytics products, organizations are looking to ThoughtSpot and Mode when seeking innovative solutions—helping them harness the power of their marketing data.
