Every business that analyzes their operational (or transactional) data needs to build a custom data pipeline involving several batch or streaming jobs to extract transactional data from relational databases, transform it, and load it into the data warehouse. In this post, we show how you can leverage Amazon Aurora zero-ETL integration with Amazon Redshift and ThoughtSpot for GenAI driven near real-time operational analytics.

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.